From 17c4ab034d0f25acb250c68b6971545ef02c790b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 7 Jul 2021 07:45:36 -0400 Subject: [PATCH 01/64] added warning and test --- test/test_composite.py | 106 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 test/test_composite.py diff --git a/test/test_composite.py b/test/test_composite.py new file mode 100644 index 0000000000..3d66e845f8 --- /dev/null +++ b/test/test_composite.py @@ -0,0 +1,106 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Class to test composite experiments.""" + +from typing import Optional + +from qiskit.providers import Backend +from qiskit.providers.backend import BackendV1 +from qiskit.providers.options import Options +from qiskit.providers.models import QasmBackendConfiguration +from qiskit.result import Result +from qiskit.test import QiskitTestCase + +from qiskit_experiments.test.mock_job import MockJob +from qiskit_experiments.base_experiment import BaseExperiment +from qiskit_experiments.base_analysis import BaseAnalysis +from qiskit_experiments.composite.parallel_experiment import ParallelExperiment + + +class DummyAnalysis(BaseAnalysis): + """ + Dummy analysis class for test purposes only. + """ + + def _run_analysis(self, experiment_data, **options): + return [], None + + +class DummyExperiment(BaseExperiment): + """ + Dummy experiment class for test purposes only. + """ + + __analysis_class__ = DummyAnalysis + + def circuits(self, backend: Optional[Backend] = None): + return [] + + +class DummyBackend(BackendV1): + """ + Dummy backend for test purposes only. + """ + + def __init__(self): + configuration = QasmBackendConfiguration( + backend_name="dummy_backend", + backend_version="0", + n_qubits=int(1e6), + basis_gates=["barrier", "x", "delay", "measure"], + gates=[], + local=True, + simulator=True, + conditional=False, + open_pulse=False, + memory=False, + max_shots=int(1e6), + coupling_map=None, + ) + super().__init__(configuration) + + @classmethod + def _default_options(cls): + return Options() + + def run(self, run_input, **options): + result = { + "backend_name": "Dummmy backend", + "backend_version": "0", + "qobj_id": 0, + "job_id": 0, + "success": True, + "results": [], + } + return MockJob(backend=self, result=Result.from_dict(result)) + + +class TestComposite(QiskitTestCase): + """ + Test composite experiment behavior. + """ + + def test_parallel_options(self): + """ + Test parallel experiments overriding sub-experiment options. + """ + + exp0 = DummyExperiment(0) + exp2 = DummyExperiment(2) + exp2.set_run_options(shots=2000) + + par_exp = ParallelExperiment([exp0, exp2]) + with self.assertWarnsRegex( + Warning, "Sub-experiment run options are overridden by composite experiment settings." + ): + par_exp.run(DummyBackend()) From 2ae42ed45d24ad40d1c5932ad6bc2b5eabc439a2 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 08:05:02 -0400 Subject: [PATCH 02/64] test commit --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 1437e3c9ae..0d583aaf12 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -31,6 +31,7 @@ Table of Contents API References Experiment Library Tutorials + Experiment Guides Release Notes .. Hiding - Indices and tables From 9a9deb118a1d2a775ddf450ecd35ee511ddf3913 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 08:32:32 -0400 Subject: [PATCH 03/64] moved files around --- .../experiment_cloud_service.rst | 0 .../experiment_cloud_service/filter.png | Bin .../experiment_cloud_service/metadata.png | Bin .../experiment_cloud_service/t1_deleted.png | Bin .../t1_experiment.png | Bin .../experiment_cloud_service/t1_loaded.png | Bin .../verify_experiment.png | Bin .../web_tags_share.png | Bin docs/guides/index.rst | 15 ++++++++++++ docs/{tutorials => guides}/quantum_volume.rst | 0 .../randomized_benchmarking.rst | 0 .../readout_mitigation.rst | 0 .../restless_measurements.rst | 0 docs/{tutorials => guides}/restless_shots.png | Bin .../state_tomography.rst | 0 docs/{tutorials => guides}/t1.rst | 23 ++++++------------ .../t2hahn_characterization.rst | 0 .../t2ramsey_characterization.rst | 0 .../tphi_characterization.rst | 0 docs/tutorials/index.rst | 1 + docs/tutorials/intro.rst | 9 +++++++ 21 files changed, 32 insertions(+), 16 deletions(-) rename docs/{tutorials => guides}/experiment_cloud_service.rst (100%) rename docs/{tutorials => guides}/experiment_cloud_service/filter.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/metadata.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/t1_deleted.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/t1_experiment.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/t1_loaded.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/verify_experiment.png (100%) rename docs/{tutorials => guides}/experiment_cloud_service/web_tags_share.png (100%) create mode 100644 docs/guides/index.rst rename docs/{tutorials => guides}/quantum_volume.rst (100%) rename docs/{tutorials => guides}/randomized_benchmarking.rst (100%) rename docs/{tutorials => guides}/readout_mitigation.rst (100%) rename docs/{tutorials => guides}/restless_measurements.rst (100%) rename docs/{tutorials => guides}/restless_shots.png (100%) rename docs/{tutorials => guides}/state_tomography.rst (100%) rename docs/{tutorials => guides}/t1.rst (92%) rename docs/{tutorials => guides}/t2hahn_characterization.rst (100%) rename docs/{tutorials => guides}/t2ramsey_characterization.rst (100%) rename docs/{tutorials => guides}/tphi_characterization.rst (100%) create mode 100644 docs/tutorials/intro.rst diff --git a/docs/tutorials/experiment_cloud_service.rst b/docs/guides/experiment_cloud_service.rst similarity index 100% rename from docs/tutorials/experiment_cloud_service.rst rename to docs/guides/experiment_cloud_service.rst diff --git a/docs/tutorials/experiment_cloud_service/filter.png b/docs/guides/experiment_cloud_service/filter.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/filter.png rename to docs/guides/experiment_cloud_service/filter.png diff --git a/docs/tutorials/experiment_cloud_service/metadata.png b/docs/guides/experiment_cloud_service/metadata.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/metadata.png rename to docs/guides/experiment_cloud_service/metadata.png diff --git a/docs/tutorials/experiment_cloud_service/t1_deleted.png b/docs/guides/experiment_cloud_service/t1_deleted.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/t1_deleted.png rename to docs/guides/experiment_cloud_service/t1_deleted.png diff --git a/docs/tutorials/experiment_cloud_service/t1_experiment.png b/docs/guides/experiment_cloud_service/t1_experiment.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/t1_experiment.png rename to docs/guides/experiment_cloud_service/t1_experiment.png diff --git a/docs/tutorials/experiment_cloud_service/t1_loaded.png b/docs/guides/experiment_cloud_service/t1_loaded.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/t1_loaded.png rename to docs/guides/experiment_cloud_service/t1_loaded.png diff --git a/docs/tutorials/experiment_cloud_service/verify_experiment.png b/docs/guides/experiment_cloud_service/verify_experiment.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/verify_experiment.png rename to docs/guides/experiment_cloud_service/verify_experiment.png diff --git a/docs/tutorials/experiment_cloud_service/web_tags_share.png b/docs/guides/experiment_cloud_service/web_tags_share.png similarity index 100% rename from docs/tutorials/experiment_cloud_service/web_tags_share.png rename to docs/guides/experiment_cloud_service/web_tags_share.png diff --git a/docs/guides/index.rst b/docs/guides/index.rst new file mode 100644 index 0000000000..3b4fb3aa6f --- /dev/null +++ b/docs/guides/index.rst @@ -0,0 +1,15 @@ +##################### +Experiment Guides +##################### + + +.. nbgallery:: + :glob: + + * + + +.. Hiding - Indices and tables + :ref:`genindex` + :ref:`modindex` + :ref:`search` diff --git a/docs/tutorials/quantum_volume.rst b/docs/guides/quantum_volume.rst similarity index 100% rename from docs/tutorials/quantum_volume.rst rename to docs/guides/quantum_volume.rst diff --git a/docs/tutorials/randomized_benchmarking.rst b/docs/guides/randomized_benchmarking.rst similarity index 100% rename from docs/tutorials/randomized_benchmarking.rst rename to docs/guides/randomized_benchmarking.rst diff --git a/docs/tutorials/readout_mitigation.rst b/docs/guides/readout_mitigation.rst similarity index 100% rename from docs/tutorials/readout_mitigation.rst rename to docs/guides/readout_mitigation.rst diff --git a/docs/tutorials/restless_measurements.rst b/docs/guides/restless_measurements.rst similarity index 100% rename from docs/tutorials/restless_measurements.rst rename to docs/guides/restless_measurements.rst diff --git a/docs/tutorials/restless_shots.png b/docs/guides/restless_shots.png similarity index 100% rename from docs/tutorials/restless_shots.png rename to docs/guides/restless_shots.png diff --git a/docs/tutorials/state_tomography.rst b/docs/guides/state_tomography.rst similarity index 100% rename from docs/tutorials/state_tomography.rst rename to docs/guides/state_tomography.rst diff --git a/docs/tutorials/t1.rst b/docs/guides/t1.rst similarity index 92% rename from docs/tutorials/t1.rst rename to docs/guides/t1.rst index 4e59930f8c..b882969ebc 100644 --- a/docs/tutorials/t1.rst +++ b/docs/guides/t1.rst @@ -1,4 +1,4 @@ -A :math:`T_1` experiment +The `T_1` experiment ======================== In a :math:`T_1` experiment, we measure an excited qubit after a delay. @@ -92,21 +92,6 @@ create a parallel experiment: print(result) -Viewing sub experiment data -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The experiment data returned from a batched experiment also contains -individual experiment data for each sub experiment which can be accessed -using ``child_data`` - -.. jupyter-execute:: - - # Print sub-experiment data - for i, sub_data in enumerate(parallel_data.child_data()): - print("Component experiment",i) - display(sub_data.figure(0)) - for result in sub_data.analysis_results(): - print(result) :math:`T_1` experiments with kerneled measurement --------------------------------------------------- @@ -164,6 +149,12 @@ that is close to a logical value '0'. for result in expdataT1_kerneled.analysis_results(): print(result) +Designing the optimal `T_1` experiment +-------------------------------------- + +Given a limited number of shots :math:`t`, what is the optimal selection of delay times +and the number of shots for each? + .. jupyter-execute:: import qiskit.tools.jupyter diff --git a/docs/tutorials/t2hahn_characterization.rst b/docs/guides/t2hahn_characterization.rst similarity index 100% rename from docs/tutorials/t2hahn_characterization.rst rename to docs/guides/t2hahn_characterization.rst diff --git a/docs/tutorials/t2ramsey_characterization.rst b/docs/guides/t2ramsey_characterization.rst similarity index 100% rename from docs/tutorials/t2ramsey_characterization.rst rename to docs/guides/t2ramsey_characterization.rst diff --git a/docs/tutorials/tphi_characterization.rst b/docs/guides/tphi_characterization.rst similarity index 100% rename from docs/tutorials/tphi_characterization.rst rename to docs/guides/tphi_characterization.rst diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index ebde368514..9e2decd1a4 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -3,6 +3,7 @@ Experiments Tutorials ##################### + .. nbgallery:: :glob: diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst new file mode 100644 index 0000000000..5dad0486f6 --- /dev/null +++ b/docs/tutorials/intro.rst @@ -0,0 +1,9 @@ +Introduction +============ + +Qiskit Experiments is a tool for those interested in running quantum experiments on top +of Qiskit. + + +What Qiskit Experiments Can Do +------------------------------ \ No newline at end of file From f2dad3f60b552ea36f4a1d46d6e72edee8bb9094 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 08:57:16 -0400 Subject: [PATCH 04/64] added install --- docs/tutorials/Installation.rst | 29 +++++++++++++++++++++++++++++ docs/tutorials/index.rst | 17 ++++++++++++++--- releasenotes/config.yaml | 1 - 3 files changed, 43 insertions(+), 4 deletions(-) create mode 100644 docs/tutorials/Installation.rst diff --git a/docs/tutorials/Installation.rst b/docs/tutorials/Installation.rst new file mode 100644 index 0000000000..e8b7721fc0 --- /dev/null +++ b/docs/tutorials/Installation.rst @@ -0,0 +1,29 @@ +Installation +============= + +Official Qiskit Experiments releases can be installed via the python package manager +`pip`. + +.. code-block:: + python -m pip install qiskit-experiments + +If you want to install the most up-to-date version instead (may not be stable), you can +install the latest main branch: + +.. code-block:: + python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git + +If you want to develop the package, you can install Qiskit Experiments from source by +cloning the repository: + +.. code-block:: + git clone https://github.com/Qiskit/qiskit-experiments.git + python -m pip install -e qiskit-experiments + +The `-e` option will keep your installed package up to date as you make or pull new +changes. + +Running Your First Experiment +============================= + +Let's run a T1 experiment: \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 9e2decd1a4..a2af1f38e8 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -2,12 +2,23 @@ Experiments Tutorials ##################### +Introduction +=============== +.. toctree:: + :maxdepth: 2 -.. nbgallery:: - :glob: + intro - * + +Getting Started +=============== + +.. toctree:: + :maxdepth: 2 + + installation + .. Hiding - Indices and tables diff --git a/releasenotes/config.yaml b/releasenotes/config.yaml index cd18b4b856..76062aec91 100644 --- a/releasenotes/config.yaml +++ b/releasenotes/config.yaml @@ -84,4 +84,3 @@ template: | needs to be worded so that it does not depend on any information only available in another section, such as the prelude. This may mean repeating some details. -earliest_version: 0.1.0 From 88f0c8ce98068f0cd8a6eaf8f9f93f73f4ba04cc Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 09:49:08 -0400 Subject: [PATCH 05/64] added files --- docs/howtos/index.rst | 19 +++++++++++++++++++ docs/howtos/newexperimentdata.rst | 18 ++++++++++++++++++ docs/index.rst | 12 ++++++++++-- docs/tutorials/images/qiskitarch.png | Bin 0 -> 154276 bytes docs/tutorials/intro.rst | 24 +++++++++++++++++++++++- 5 files changed, 70 insertions(+), 3 deletions(-) create mode 100644 docs/howtos/index.rst create mode 100644 docs/howtos/newexperimentdata.rst create mode 100644 docs/tutorials/images/qiskitarch.png diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst new file mode 100644 index 0000000000..a41de359c4 --- /dev/null +++ b/docs/howtos/index.rst @@ -0,0 +1,19 @@ +##################### +How-To Recipes +##################### + +How to... + +.. toctree:: + :maxdepth: 2 + :glob: + + * + + + + +.. Hiding - Indices and tables + :ref:`genindex` + :ref:`modindex` + :ref:`search` diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst new file mode 100644 index 0000000000..f9e28d37f3 --- /dev/null +++ b/docs/howtos/newexperimentdata.rst @@ -0,0 +1,18 @@ +How to instantiate a new experiment data object +============ + +If you want to instantiate a new experiment data object from an existing experiment and +jobs that finished execution successfully: + +.. code-block:: + from qiskit_experiments.framework import ExperimentData + + # The same experiment that you ran + experiment = Experiment(**opts) + + # List of job IDs for the experiment + data = ExperimentData(job_ids=job_ids) + experiment.analysis.run(data) + data.block_for_results() + +`data` will be the new experiment data class. \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 0d583aaf12..3edd12cfaf 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -25,13 +25,21 @@ backends can be stored and retrieved from an online experiment Table of Contents ================= +We've divided up the documentation into areas by purpose. The tutorials are +learning-based documentation suitable for getting started. The how-to recipes +are short and direct instructions for solving specific problems. The experiment +guides have in-depth explanations for key experiments in the package, and discuss +advanced usage and options that would be of interest to experimentalists and +researchers. + .. toctree:: :maxdepth: 2 - API References - Experiment Library Tutorials + How-To Recipes Experiment Guides + API References + Experiment Library Release Notes .. 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docs/guides/experiment_cloud_service.rst | 245 ----------------------- docs/guides/quantum_volume.rst | 189 ----------------- docs/guides/readout_mitigation.rst | 179 ----------------- docs/guides/state_tomography.rst | 187 ----------------- 4 files changed, 800 deletions(-) delete mode 100644 docs/guides/experiment_cloud_service.rst delete mode 100644 docs/guides/quantum_volume.rst delete mode 100644 docs/guides/readout_mitigation.rst delete mode 100644 docs/guides/state_tomography.rst diff --git a/docs/guides/experiment_cloud_service.rst b/docs/guides/experiment_cloud_service.rst deleted file mode 100644 index 345abcce44..0000000000 --- a/docs/guides/experiment_cloud_service.rst +++ /dev/null @@ -1,245 +0,0 @@ -Saving Experiment Data to the Cloud -=================================== - -Qiskit Experiments is designed to work with Qiskit’s `online experiment -database `__, where you -can view and share results of experiments you’ve run as ``ExperimentData`` -objects. This tutorial shows how to save your experimental results to the -database. You will need to have ``qiskit-ibmq-provider`` installed locally -and an account in the Qiskit cloud service. We will use the ``ibmq_lima`` backend -which is open and available to everyone. - -:math:`T_1` Experiment ----------------------- - -Let’s run a :math:`T_1` experiment and save the results to the -experiment database. - -.. jupyter-execute:: - - from qiskit_experiments.library.characterization import T1 - import numpy as np - - t1_delays = np.arange(1e-6, 600e-6, 50e-6) - - # Create an experiment for qubit 0, - # setting the unit to microseconds, - # with the specified time intervals - exp = T1(qubit=0, delays=t1_delays) - print(exp.circuits()[0]) - -Now we run the experiment. ``block_for_results()`` blocks execution -until the experiment is complete, then ``save()`` is called to save the -data to ResultsDB. - -.. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - - -.. jupyter-execute:: - - from qiskit import IBMQ - IBMQ.load_account() - provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main") - backend = provider.get_backend("ibmq_lima") - - t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() - t1_expdata.save() - -.. jupyter-execute:: - :hide-code: - - print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") - -Note that calling ``save()`` before the experiment is complete will -instantiate an experiment entry in the database, but it will not have -complete data. To fix this, you can call ``save()`` again once the -experiment is done running. - -Our :math:`T_1` figure and analysis results: - -.. jupyter-execute:: - - display(t1_expdata.figure(0)) - for result in t1_expdata.analysis_results(): - print(result) - - -You can also view the results at the `IBM Quantum Experiments -pane `__ -on the cloud. - -By default, the interface displays all experiments you have privilege to -see, but this link shows your own experiments. You can change that -setting by clicking on the All Experiments dropdown. You can also filter -by device, date, provider, and result by clicking on the filter icon. - -.. image:: ./experiment_cloud_service/filter.png - -Individual experiment pages show the plot, and one or more important -analysis results, which for the :math:`T_1` experiment is the fitted -:math:`T_1` value. - -.. image:: ./experiment_cloud_service/t1_experiment.png - -The metadata field shows experiment metadata included in the ``ExperimentData`` object. - -.. image:: ./experiment_cloud_service/metadata.png - -You can change the quality and verify/unverify the results upon -selection of an analysis result. Quality is an automatic parameter -generated by the experiment analysis based on pre-set criteria. The verification field is for a -human to determine whether the result is acceptable. - -.. image:: ./experiment_cloud_service/verify_experiment.png - -Loading an experiment from the database -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can also retrieve the full ``ExperimentData`` object from the database service. -Let’s load a `previous T1 -experiment `__, -which we’ve made public by editing the ``Share level`` field: - -.. jupyter-execute:: - :hide-output: - :raises: - - from qiskit_experiments.framework.experiment_data import ExperimentData - service = ExperimentData.get_service_from_backend(backend) - load_expdata = ExperimentData.load("9640736e-d797-4321-b063-d503f8e98571", service) - -To display the figure, which is serialized into a string, we need the -``SVG`` library: - -.. jupyter-execute:: - :hide-output: - :raises: - - from IPython.display import SVG - SVG(load_expdata.figure(0).figure) - -.. image:: ./experiment_cloud_service/t1_loaded.png - -The analysis results have been retrieved as well: - -.. jupyter-execute:: - :hide-output: - :raises: - - for result in load_expdata.analysis_results(): - print(result) - -.. jupyter-execute:: - :hide-code: - - print("""AnalysisResult - - name: T1 - - value: 0.0001040+/-0.0000028 - - χ²: 0.8523786276663019 - - quality: good - - extra: <1 items> - - device_components: ['Q0'] - - verified: False - AnalysisResult - - name: @Parameters_T1Analysis - - value: CurveFitResult: - - fitting method: least_squares - - number of sub-models: 1 - * F_exp_decay(x) = amp * exp(-x/tau) + base - - success: True - - number of function evals: 9 - - degree of freedom: 9 - - chi-square: 7.671407648996717 - - reduced chi-square: 0.8523786276663019 - - Akaike info crit.: 0.6311217041870707 - - Bayesian info crit.: 2.085841653551072 - - init params: - * amp = 0.923076923076923 - * tau = 0.00016946294665316433 - * base = 0.033466533466533464 - - fit params: - * amp = 0.9266620487665083 ± 0.007096409569790425 - * tau = 0.00010401411623191737 ± 2.767679521974391e-06 - * base = 0.036302726197354626 ± 0.0037184540724124844 - - correlations: - * (tau, base) = -0.6740808746060173 - * (amp, base) = -0.4231810882291163 - * (amp, tau) = 0.09302612202500576 - - quality: good - - device_components: ['Q0'] - - verified: False""") - -Auto-saving an experiment -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The ``auto_save`` feature automatically saves changes to the `ExperimentData` object to -the cloud service whenever it's updated. - -.. jupyter-execute:: - :hide-output: - - exp = T1(qubit=0, delays=t1_delays) - - t1_expdata = exp.run(backend=backend, shots=1000) - t1_expdata.auto_save = True - t1_expdata.block_for_results() - -.. jupyter-execute:: - :hide-code: - - print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/cdaff3fa-f621-4915-a4d8-812d05d9a9ca") - print("") - -Deleting an experiment -~~~~~~~~~~~~~~~~~~~~~~ - -Both figures and analysis results can be deleted. Note that unless you -have auto save on, the update has to be manually saved to the remote -database by calling ``save()``. Because there are two analysis results, one for -the T1 parameter and one for the curve fitting results, we delete twice. - -.. jupyter-execute:: - :hide-output: - - t1_expdata.delete_figure(0) - t1_expdata.delete_analysis_result(0) - t1_expdata.delete_analysis_result(0) - -.. jupyter-execute:: - :hide-code: - - print("Are you sure you want to delete the experiment plot? [y/N]: y") - print("Are you sure you want to delete the analysis result? [y/N]: y") - print("Are you sure you want to delete the analysis result? [y/N]: y") - -The web interface shows that both the figure and analysis result have been -deleted: |t1_deleted.png| - -.. |t1_deleted.png| image:: ./experiment_cloud_service/t1_deleted.png - -Tagging and sharing experiments -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Tags and notes can be added to experiments to help identify specific experiments in the interface. -For example, an experiment can be tagged and made public with the following code. - -.. jupyter-execute:: - - t1_expdata.tags = ['tag1', 'tag2'] - t1_expdata.share_level = "public" - t1_expdata.notes = "Example note." - -These fields can also be updated in the web interface. For more information about -using the interface, consult its -`documentation `__. - -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright - diff --git a/docs/guides/quantum_volume.rst b/docs/guides/quantum_volume.rst deleted file mode 100644 index a180826992..0000000000 --- a/docs/guides/quantum_volume.rst +++ /dev/null @@ -1,189 +0,0 @@ -Quantum Volume -============== - -Quantum Volume (QV) is a single-number metric that can be measured using -a concrete protocol on near-term quantum computers of modest size. The -QV method quantifies the largest random circuit of equal width and depth -that the computer successfully implements. Quantum computing systems -with high-fidelity operations, high connectivity, large calibrated gate -sets, and circuit rewriting toolchains are expected to have higher -quantum volumes. See `Qiskit -Textbook `__ -for an explanation on the QV method, which is described in Ref. [1, 2]. - -The Quantum Volume is determined by the largest successful circuit depth -:math:`d_{max}`, and equals to :math:`2^{d_{max}}`. In the QV experiment -we generate `QV -circuits `__ -on :math:`d` qubits, which contain :math:`d` layers, where each layer -consists of random 2-qubit unitary gates from :math:`SU(4)`, followed by -a random permutation on the :math:`d` qubit. Then these circuits run on -the quantum backend and on an ideal simulator (either ``AerSimulator`` -or ``qiskit.quantum_info.Statevector``). - -A depth :math:`d` QV circuit is successful if it has ‘mean heavy-output -probability’ > 2/3 with confidence level > 0.977 (corresponding to -z_value = 2), and at least 100 trials have been ran. - -.. jupyter-execute:: - - from qiskit_experiments.framework import BatchExperiment - from qiskit_experiments.library import QuantumVolume - from qiskit import Aer - from qiskit_aer import AerSimulator - - # For simulation - from qiskit.providers.fake_provider import FakeSydney - - backend = AerSimulator.from_backend(FakeSydney()) - -QV experiment -------------- - -To run the QV experiment we need need to provide the following QV -parameters, in order to generate the QV circuits and run them on a -backend and on an ideal simulator: - -- ``qubits``: The number of qubits or list of physical qubits for the - experiment. - -- ``trials``: The number of trials to run the quantum volume circuit - (the default is 100). - -- ``seed``: Seed or generator object for random number generation. If - ``None`` then ``default_rng`` will be used. - -- ``simulation_backend``: The simulator backend to use to generate the - expected results. the simulator must have a ``save_probabilities`` - method. If None ``AerSimulator`` simulator will be used (in case - ``AerSimulator`` is not installed ``qiskit.quantum_info.Statevector`` - will be used). - -**Note:** In some cases, 100 trials are not enough to obtain a QV -greater than 1 for the specified number of qubits. In this case, adding -more trials may reduce the error bars to allow passing the threshold. - -The analysis results of the QV Experiment are: - -- The mean heavy output probabilities (HOP) and standard deviation - -- The calculated quantum volume, which will be None if the experiment - does not pass the threshold - -Extra data included in the analysis results includes - -- The heavy HOPs for each trial - -- Confidence level (should be greater than 0.977) - -- The number of trials and depth of the QV circuits - -- Whether the QV circuit was successful - -.. jupyter-execute:: - - qubits = tuple(range(4)) # Can use specific qubits. for example [2, 4, 7, 10] - - qv_exp = QuantumVolume(qubits, seed=42) - # Transpile options like optimization_level affect only the real device run and not the simulation run - # Run options affect both simulation and real device runs - qv_exp.set_transpile_options(optimization_level=3) - - # Run experiment - expdata = qv_exp.run(backend).block_for_results() - -.. jupyter-execute:: - - # View result data - display(expdata.figure(0)) - - for result in expdata.analysis_results(): - print(result) - - -.. jupyter-execute:: - - # Print extra data - for result in expdata.analysis_results(): - print(f"\n{result.name} extra:") - for key, val in result.extra.items(): - print(f"- {key}: {val}") - - -Adding trials -------------- - -Adding more trials may reduce the error bars to allow passing the -threshold (beside the error bars - QV experiment must have at least 100 -trials to be considered successful). In case you want to add less than -100 additional trials, you can modify the amount of trials added before -re-running the experiment. - -.. jupyter-execute:: - - qv_exp.set_experiment_options(trials=60) - expdata2 = qv_exp.run(backend, analysis=None).block_for_results() - expdata2.add_data(expdata.data()) - qv_exp.analysis.run(expdata2).block_for_results() - - # View result data - display(expdata2.figure(0)) - for result in expdata2.analysis_results(): - print(result) - - -Calculating Quantum Volume using a batch experiment ---------------------------------------------------- - -Run the QV experiment with an increasing number of qubits to check what -is the maximum Quantum Volume for the specific device. To reach the real -system’s Quantum Volume, one must run more trials and additional -enhancements might be required (See Ref. [2] for details). - -.. jupyter-execute:: - - exps = [QuantumVolume(tuple(range(i)), trials=200) for i in range(3, 6)] - - batch_exp = BatchExperiment(exps) - batch_exp.set_transpile_options(optimization_level=3) - - # Run - batch_expdata = batch_exp.run(backend).block_for_results() - -Extracting the maximum Quantum Volume. - -.. jupyter-execute:: - - qv_values = [ - batch_expdata.child_data(i).analysis_results("quantum_volume").value - for i in range(batch_exp.num_experiments) - ] - - print(f"Max quantum volume is: {max(qv_values)}") - - -.. jupyter-execute:: - - for i in range(batch_exp.num_experiments): - print(f"\nComponent experiment {i}") - sub_data = batch_expdata.child_data(i) - display(sub_data.figure(0)) - for result in sub_data.analysis_results(): - print(result) - -References ----------- - -[1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and -Jay M. Gambetta, Validating quantum computers using randomized model -circuits, Phys. Rev. A 100, 032328 (2019). -https://arxiv.org/pdf/1811.12926 - -[2] Petar Jurcevic et. al. Demonstration of quantum volume 64 on -a superconducting quantum computing system, -https://arxiv.org/pdf/2008.08571 - -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/readout_mitigation.rst deleted file mode 100644 index d7f518f11d..0000000000 --- a/docs/guides/readout_mitigation.rst +++ /dev/null @@ -1,179 +0,0 @@ -Readout Mitigation -================== - -Readout errors affect quantum computation during the measurement of the -qubits in a quantum device. By characterizing the readout errors, it is -possible to construct a *readout error mitigator* that is used both to -obtain a more accurate distribution of the outputs, and more accurate -measurements of expectation value for measurables. - -The readout mitigator is generated from an *assignment matrix*: a -:math:`2^n \times 2^n` matrix :math:`A` such that :math:`A_{y,x}` is the -probability to observe :math:`y` given the true outcome should be -:math:`x`. The assignment matrix is used to compute the *mitigation -matrix* used in the readout error mitigation process itself. - -A *Local readout mitigator* works under the assumption that readout -errors are mostly *local*, meaning readout errors for different qubits -are independent of each other. In this case, the assignment matrix is -the tensor product of :math:`n` :math:`2 \times 2` matrices, one for -each qubit, making it practical to store the assignment matrix in -implicit form, by storing the individual :math:`2 \times 2` assignment -matrices. The corresponding class in Qiskit is the `Local readout -mitigator `__ -in ``qiskit-terra``. - -A *Correlated readout mitigator* uses the full :math:`2^n \times 2^n` -assignment matrix, meaning it can only be used for small values of -:math:`n`. The corresponding class in Qiskit is the `Correlated readout -mitigator `__ -in ``qiskit-terra``. - -This notebook demonstrates the usage of both the local and correlated -experiments to generate the corresponding mitigators. - -.. jupyter-execute:: - - import numpy as np - import matplotlib.pyplot as plt - from qiskit import QuantumCircuit - from qiskit.visualization import plot_histogram - from qiskit_experiments.library import LocalReadoutError, CorrelatedReadoutError - # For simulation - from qiskit_aer import AerSimulator - from qiskit.providers.fake_provider import FakeParis - - from qiskit.result.mitigation.utils import ( - expval_with_stddev, - str2diag, - counts_probability_vector - ) - - backend = AerSimulator.from_backend(FakeParis()) - -.. jupyter-execute:: - - SHOTS = 1024 - qubits = [0,1,2,3] - num_qubits = len(qubits) - -Standard mitigation experiment -============================== - -The default mitigation experiment is *local*, meaning error probability -is measured individually for each qubit. The experiment generates two -circuits, one for all “0” and one for all “1” results. - -.. jupyter-execute:: - - exp = LocalReadoutError(qubits) - for c in exp.circuits(): - print(c) - - -.. jupyter-execute:: - - exp.analysis.set_options(plot=True) - result = exp.run(backend) - mitigator = result.analysis_results(0).value - -The resulting measurement matrix can be illustrated by comparing it to -the identity. - -.. jupyter-execute:: - - result.figure(0) - - -Mitigation matrices -------------------- - -The individual mitigation matrices can be read off the mitigator. - -.. jupyter-execute:: - - for m in mitigator._mitigation_mats: - print(m) - print() - - -Mitigation Example ------------------- - -.. jupyter-execute:: - - qc = QuantumCircuit(num_qubits) - qc.h(0) - for i in range(1, num_qubits): - qc.cx(i - 1, i) - qc.measure_all() - -.. jupyter-execute:: - - counts = backend.run(qc, shots=SHOTS, seed_simulator=42, method="density_matrix").result().get_counts() - unmitigated_probs = {label: count / SHOTS for label, count in counts.items()} - -.. jupyter-execute:: - - mitigated_quasi_probs = mitigator.quasi_probabilities(counts) - mitigated_stddev = mitigated_quasi_probs._stddev_upper_bound - mitigated_probs = (mitigated_quasi_probs.nearest_probability_distribution().binary_probabilities()) - -Probabilities -~~~~~~~~~~~~~ - -.. jupyter-execute:: - - legend = ['Mitigated Probabilities', 'Unmitigated Probabilities'] - plot_histogram([mitigated_probs, unmitigated_probs], legend=legend, sort="value_desc", bar_labels=False) - - -Expectation value ------------------ - -.. jupyter-execute:: - - diagonal_labels = ["ZZZZ", "ZIZI", "IZII", "1ZZ0"] - ideal_expectation = [] - diagonals = [str2diag(d) for d in diagonal_labels] - qubit_index = {i: i for i in range(num_qubits)} - unmitigated_probs_vector, _ = counts_probability_vector(unmitigated_probs, qubit_index=qubit_index) - unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, SHOTS) for d in diagonals] - mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals] - -.. jupyter-execute:: - - mitigated_expectation_values, mitigated_stddev = zip(*mitigated_expectation) - unmitigated_expectation_values, unmitigated_stddev = zip(*unmitigated_expectation) - legend = ['Mitigated Expectation', 'Unmitigated Expectation'] - fig, ax = plt.subplots() - X = np.arange(4) - ax.bar(X + 0.00, mitigated_expectation_values, yerr=mitigated_stddev, color='b', width = 0.25, label="Mitigated Expectation") - ax.bar(X + 0.25, unmitigated_expectation_values, yerr=unmitigated_stddev, color='g', width = 0.25, label="Unmitigated Expectation") - ax.set_xticks([0.125 + i for i in range(len(diagonals))]) - ax.set_xticklabels(diagonal_labels) - ax.legend() - -Correlated readout mitigation ------------------------------ - -In correlated readout mitigation on :math:`n` qubits, a circuit is -generated for each of the possible :math:`2^n` combinations of “0” and -“1”. This results in more accurate mitigation in the case where the -readout errors are correlated and not independent, but requires a large -amount of circuits and storage space, and so is infeasible for more than -a few qubits. - -.. jupyter-execute:: - - qubits = [0,3] - num_qubits = len(qubits) - exp = CorrelatedReadoutError(qubits) - for c in exp.circuits(): - print(c) - - -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright diff --git a/docs/guides/state_tomography.rst b/docs/guides/state_tomography.rst deleted file mode 100644 index 7b7c1cd368..0000000000 --- a/docs/guides/state_tomography.rst +++ /dev/null @@ -1,187 +0,0 @@ -Quantum State Tomography -======================== - -.. jupyter-execute:: - - import qiskit - from qiskit_experiments.framework import ParallelExperiment - from qiskit_experiments.library import StateTomography - - # For simulation - from qiskit_aer import AerSimulator - from qiskit.providers.fake_provider import FakeParis - - # Noisy simulator backend - backend = AerSimulator.from_backend(FakeParis()) - -State Tomography Experiment ---------------------------- - -To run a state tomography experiment we initialize the experiment with a -circuit to prepare the state to be measured. We can also pass in an -``Operator``, or a ``Statevector`` to describe the preparation circuit. - -.. jupyter-execute:: - - # Run experiments - - # GHZ State preparation circuit - nq = 2 - qc_ghz = qiskit.QuantumCircuit(nq) - qc_ghz.h(0) - qc_ghz.s(0) - for i in range(1, nq): - qc_ghz.cx(0, i) - - # QST Experiment - qstexp1 = StateTomography(qc_ghz) - qstdata1 = qstexp1.run(backend, seed_simulation=100).block_for_results() - - # Print results - for result in qstdata1.analysis_results(): - print(result) - - - -Tomography Results -~~~~~~~~~~~~~~~~~~ - -The main result for tomography is the fitted state, which is stored as a -``DensityMatrix`` object: - -.. jupyter-execute:: - - state_result = qstdata1.analysis_results("state") - print(state_result.value) - - - - -The state fidelity of the fitted state with the ideal state prepared by -the input circuit is stored in the ``"state_fidelity"`` result field. -Note that if the input circuit contained any measurements the ideal -state cannot be automatically generated and this field will be set to -``None``. - -.. jupyter-execute:: - - fid_result = qstdata1.analysis_results("state_fidelity") - print("State Fidelity = {:.5f}".format(fid_result.value)) - - - -Additional state metadata -^^^^^^^^^^^^^^^^^^^^^^^^^ - -Additional data is stored in the tomography under the -``"state_metadata"`` field. This includes - ``eigvals``: the eigenvalues -of the fitted state - ``trace``: the trace of the fitted state - -``positive``: Whether the eigenvalues are all non-negative - -``positive_delta``: the deviation from positivity given by 1-norm of -negative eigenvalues. - -If trace rescaling was performed this dictionary will also contain a -``raw_trace`` field containing the trace before rescaling. Futhermore, -if the state was rescaled to be positive or trace 1 an additional field -``raw_eigvals`` will contain the state eigenvalues before rescaling was -performed. - -.. jupyter-execute:: - - state_result.extra - -To see the effect of rescaling we can perform a “bad” fit with very low -counts - -.. jupyter-execute:: - - # QST Experiment - bad_data = qstexp1.run(backend, shots=10, seed_simulation=100).block_for_results() - bad_state_result = bad_data.analysis_results("state") - - # Print result - print(bad_state_result) - - # Show extra data - bad_state_result.extra - - - -Tomography Fitters ------------------- - -The default fitters is ``linear_inversion``, which reconstructs the -state using *dual basis* of the tomography basis. This will typically -result in a non-positive reconstructed state. This state is rescaled to -be positive-semidefinite (PSD) by computing its eigen-decomposition and -rescaling its eigenvalues using the approach from \*J Smolin, JM -Gambetta, G Smith, Phys. Rev. Lett. 108, 070502 (2012), `open -access `__. - -There are several other fitters are included (See API documentation for -details). For example if ``cvxpy`` is installed we can use the -``cvxpy_gaussian_lstsq`` fitter which allows constraining the fit to be -PSD without requiring rescaling. - -.. jupyter-execute:: - - try: - import cvxpy - - # Set analysis option for cvxpy fitter - qstexp1.analysis.set_options(fitter='cvxpy_gaussian_lstsq') - - # Re-run experiment - qstdata2 = qstexp1.run(backend, seed_simulation=100).block_for_results() - - state_result2 = qstdata2.analysis_results("state") - print(state_result2) - print("\nextra:") - for key, val in state_result2.extra.items(): - print(f"- {key}: {val}") - - except ModuleNotFoundError: - print("CVXPY is not installed") - -Parallel Tomography Experiment ------------------------------- - -We can also use the ``qiskit_experiments.ParallelExperiment`` class to -run subsystem tomography on multiple qubits in parallel. - -For example if we want to perform 1-qubit QST on several qubits at once: - -.. jupyter-execute:: - - from math import pi - num_qubits = 5 - gates = [qiskit.circuit.library.RXGate(i * pi / (num_qubits - 1)) - for i in range(num_qubits)] - - subexps = [ - StateTomography(gate, qubits=[i]) - for i, gate in enumerate(gates) - ] - parexp = ParallelExperiment(subexps) - pardata = parexp.run(backend, seed_simulation=100).block_for_results() - - for result in pardata.analysis_results(): - print(result) - -View component experiment analysis results - -.. jupyter-execute:: - - for i, expdata in enumerate(pardata.child_data()): - state_result_i = expdata.analysis_results("state") - fid_result_i = expdata.analysis_results("state_fidelity") - - print(f'\nPARALLEL EXP {i}') - print("State Fidelity: {:.5f}".format(fid_result_i.value)) - print("State: {}".format(state_result_i.value)) - - -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright From 7ddb781d71ecc73b2b431c3f5c7e4dabf413f7c5 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 10:14:14 -0400 Subject: [PATCH 07/64] add files --- .github/workflows/neko.yml | 16 ---------------- docs/howtos/index.rst | 3 +++ docs/tutorials/Installation.rst | 27 ++++++++++++++++++++++++++- docs/tutorials/index.rst | 4 ++-- 4 files changed, 31 insertions(+), 19 deletions(-) delete mode 100644 .github/workflows/neko.yml diff --git a/.github/workflows/neko.yml b/.github/workflows/neko.yml deleted file mode 100644 index f8cfdca16e..0000000000 --- a/.github/workflows/neko.yml +++ /dev/null @@ -1,16 +0,0 @@ -name: Qiskit Neko Integration Tests -on: - push: - pull_request: - branches: ['main'] -concurrency: - group: ${{ github.repository }}-${{ github.ref }}-${{ github.head_ref }}-${{ github.workflow }} - cancel-in-progress: true -jobs: - neko: - name: Qiskit Neko Integration Tests - runs-on: ubuntu-latest - steps: - - uses: Qiskit/qiskit-neko@main - with: - test_selection: experiment diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index a41de359c4..a6723079da 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -2,6 +2,9 @@ How-To Recipes ##################### +This section of the documentation provides concrete recipes for how to do specific +useful actions in Qiskit Experiments. + How to... .. toctree:: diff --git a/docs/tutorials/Installation.rst b/docs/tutorials/Installation.rst index e8b7721fc0..93d7267d51 100644 --- a/docs/tutorials/Installation.rst +++ b/docs/tutorials/Installation.rst @@ -26,4 +26,29 @@ changes. Running Your First Experiment ============================= -Let's run a T1 experiment: \ No newline at end of file +Let's run a T1 experiment. FIrst, we have to import the T1 experiment from the +Qiskit Experiments library: + +.. code-block:: + from qiskit_experiments.library import T1 + from qiskit_aer import AerSimulator + import numpy as np + +Instantiate the backend and the experiment: + +.. code-block:: + backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) + qubit0_t1 = backend.properties().t1(0) + + delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) + exp = T1(qubit=0, delays=delays)exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() + +Run and display results: + +.. code-block:: + exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() + + # Print the result + display(exp_data.figure(0)) + for result in exp_data.analysis_results(): + print(result) \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index a2af1f38e8..ed214e27a4 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -11,8 +11,8 @@ Introduction intro -Getting Started -=============== +Learning from the Ground Up +=========================== .. toctree:: :maxdepth: 2 From dbe65703a7ec5c2d15c2c0817e1c297f4b60713c Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 5 Oct 2022 10:17:36 -0400 Subject: [PATCH 08/64] fixed code blocks --- docs/howtos/newexperimentdata.rst | 3 ++- docs/tutorials/Installation.rst | 18 ++++++++++++------ 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst index f9e28d37f3..e968dcf144 100644 --- a/docs/howtos/newexperimentdata.rst +++ b/docs/howtos/newexperimentdata.rst @@ -4,7 +4,8 @@ How to instantiate a new experiment data object If you want to instantiate a new experiment data object from an existing experiment and jobs that finished execution successfully: -.. code-block:: +.. code-block:: python + from qiskit_experiments.framework import ExperimentData # The same experiment that you ran diff --git a/docs/tutorials/Installation.rst b/docs/tutorials/Installation.rst index 93d7267d51..d7624a75ff 100644 --- a/docs/tutorials/Installation.rst +++ b/docs/tutorials/Installation.rst @@ -4,19 +4,22 @@ Installation Official Qiskit Experiments releases can be installed via the python package manager `pip`. -.. code-block:: +.. code-block:: python + python -m pip install qiskit-experiments If you want to install the most up-to-date version instead (may not be stable), you can install the latest main branch: -.. code-block:: +.. code-block:: python + python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git If you want to develop the package, you can install Qiskit Experiments from source by cloning the repository: -.. code-block:: +.. code-block:: python + git clone https://github.com/Qiskit/qiskit-experiments.git python -m pip install -e qiskit-experiments @@ -29,14 +32,16 @@ Running Your First Experiment Let's run a T1 experiment. FIrst, we have to import the T1 experiment from the Qiskit Experiments library: -.. code-block:: +.. code-block:: python + from qiskit_experiments.library import T1 from qiskit_aer import AerSimulator import numpy as np Instantiate the backend and the experiment: -.. code-block:: +.. code-block:: python + backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) qubit0_t1 = backend.properties().t1(0) @@ -45,7 +50,8 @@ Instantiate the backend and the experiment: Run and display results: -.. code-block:: +.. code-block:: python + exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() # Print the result From b02ef454a4cb2a2746ae2262a62baef62e74a905 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 6 Oct 2022 01:38:37 -0400 Subject: [PATCH 09/64] updated index --- docs/howtos/newexperimentdata.rst | 2 +- docs/index.rst | 23 +++++++++++------------ docs/tutorials/Installation.rst | 4 ++-- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst index e968dcf144..83d8dd0bb6 100644 --- a/docs/howtos/newexperimentdata.rst +++ b/docs/howtos/newexperimentdata.rst @@ -16,4 +16,4 @@ jobs that finished execution successfully: experiment.analysis.run(data) data.block_for_results() -`data` will be the new experiment data class. \ No newline at end of file +``data`` will be the new experiment data class. \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 3edd12cfaf..fd318f7954 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -16,10 +16,10 @@ backends can be stored and retrieved from an online experiment .. warning:: - This package is still under active development and it is very likely - that there will be breaking API changes in future releases. - If you encounter any bugs please open an issue on - `Github `_ + This package is still under active development and it is very likely + that there will be breaking API changes in future releases. + If you encounter any bugs please open an issue on + `Github `_ Table of Contents @@ -32,15 +32,14 @@ guides have in-depth explanations for key experiments in the package, and discus advanced usage and options that would be of interest to experimentalists and researchers. -.. toctree:: - :maxdepth: 2 +.. nbgallery:: + :glob: - Tutorials - How-To Recipes - Experiment Guides - API References - Experiment Library - Release Notes + Tutorials + How-To Recipes + Experiment Guides + API References + Release Notes .. Hiding - Indices and tables :ref:`genindex` diff --git a/docs/tutorials/Installation.rst b/docs/tutorials/Installation.rst index d7624a75ff..c48e8da53a 100644 --- a/docs/tutorials/Installation.rst +++ b/docs/tutorials/Installation.rst @@ -2,7 +2,7 @@ Installation ============= Official Qiskit Experiments releases can be installed via the python package manager -`pip`. +``pip``. .. code-block:: python @@ -23,7 +23,7 @@ cloning the repository: git clone https://github.com/Qiskit/qiskit-experiments.git python -m pip install -e qiskit-experiments -The `-e` option will keep your installed package up to date as you make or pull new +The ``-e`` option will keep your installed package up to date as you make or pull new changes. Running Your First Experiment From 23afb578e78beab4184e489223c4e9ddefdef55c Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 23 Nov 2022 06:04:41 -0500 Subject: [PATCH 10/64] changed index layout and added files --- docs/_static/api.svg | 1 + docs/_static/guides.svg | 1 + docs/_static/howtos.svg | 1 + docs/_static/style.css | 15 +- docs/_static/tutorials.svg | 17 + docs/_templates/layout.html | 325 ------------------ docs/conf.py | 3 + docs/guides/quantum_volume.rst | 189 ++++++++++ docs/guides/readout_mitigation.rst | 179 ++++++++++ docs/guides/state_tomography.rst | 187 ++++++++++ docs/guides/t1_experiment.rst | 142 ++++++++ docs/howtos/cloud_service.rst | 253 ++++++++++++++ .../experiment_cloud_service/filter.png | Bin .../experiment_cloud_service/metadata.png | Bin .../experiment_cloud_service/t1_deleted.png | Bin .../t1_experiment.png | Bin .../experiment_cloud_service/t1_loaded.png | Bin .../verify_experiment.png | Bin .../web_tags_share.png | Bin docs/howtos/index.rst | 6 +- docs/howtos/newexperimentdata.rst | 30 +- docs/index.rst | 106 ++++-- docs/tutorials/Installation.rst | 60 ---- docs/tutorials/custom_experiment.rst | 22 ++ docs/tutorials/data_processor.rst | 28 ++ docs/tutorials/images/experimentarch.png | Bin 0 -> 221072 bytes docs/tutorials/images/qiskitarch.png | Bin 154276 -> 0 bytes docs/tutorials/index.rst | 29 +- docs/tutorials/intro.rst | 54 ++- docs/tutorials/learning.rst | 248 +++++++++++++ docs/tutorials/single_qubit_cals.rst | 294 ++++++++++++++++ qiskit_experiments/framework/backend_data.py | 4 +- .../framework/experiment_data.py | 4 +- qiskit_experiments/test/__init__.py | 4 +- requirements-dev.txt | 5 +- 35 files changed, 1756 insertions(+), 451 deletions(-) create mode 100644 docs/_static/api.svg create mode 100644 docs/_static/guides.svg create mode 100644 docs/_static/howtos.svg create mode 100644 docs/_static/tutorials.svg delete mode 100644 docs/_templates/layout.html create mode 100644 docs/guides/quantum_volume.rst create mode 100644 docs/guides/readout_mitigation.rst create mode 100644 docs/guides/state_tomography.rst create mode 100644 docs/guides/t1_experiment.rst create mode 100644 docs/howtos/cloud_service.rst rename docs/{guides => howtos}/experiment_cloud_service/filter.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/metadata.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/t1_deleted.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/t1_experiment.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/t1_loaded.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/verify_experiment.png (100%) rename docs/{guides => howtos}/experiment_cloud_service/web_tags_share.png (100%) delete mode 100644 docs/tutorials/Installation.rst create mode 100644 docs/tutorials/custom_experiment.rst create mode 100644 docs/tutorials/data_processor.rst create mode 100644 docs/tutorials/images/experimentarch.png delete mode 100644 docs/tutorials/images/qiskitarch.png create mode 100644 docs/tutorials/learning.rst create mode 100644 docs/tutorials/single_qubit_cals.rst diff --git a/docs/_static/api.svg b/docs/_static/api.svg new file mode 100644 index 0000000000..c933b1975f --- /dev/null +++ b/docs/_static/api.svg @@ -0,0 +1 @@ +code \ No newline at end of file diff --git a/docs/_static/guides.svg b/docs/_static/guides.svg new file mode 100644 index 0000000000..8f02550a27 --- /dev/null +++ b/docs/_static/guides.svg @@ -0,0 +1 @@ +chemistry \ No newline at end of file diff --git a/docs/_static/howtos.svg b/docs/_static/howtos.svg new file mode 100644 index 0000000000..f6ba2e595b --- /dev/null +++ b/docs/_static/howtos.svg @@ -0,0 +1 @@ +restaurant \ No newline at end of file diff --git a/docs/_static/style.css b/docs/_static/style.css index 84980a3a4a..da501bd092 100644 --- a/docs/_static/style.css +++ b/docs/_static/style.css @@ -3,10 +3,17 @@ } .wy-side-scroll { - background:#8c8c8c; + background: #8c8c8c; } -.pre -{ -color:#BE8184; +.pre { + color: #BE8184; } + +.sd-card-img-top, +article.pytorch-article img { + height: 40%; + width: 40%; + margin-left: auto; + margin-right: auto; +} \ No newline at end of file diff --git a/docs/_static/tutorials.svg b/docs/_static/tutorials.svg new file mode 100644 index 0000000000..ccb2c06d9f --- /dev/null +++ b/docs/_static/tutorials.svg @@ -0,0 +1,17 @@ + + + + + + + + + + + + + diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html deleted file mode 100644 index 3c4fb7294b..0000000000 --- a/docs/_templates/layout.html +++ /dev/null @@ -1,325 +0,0 @@ -{# TEMPLATE VAR SETTINGS #} -{%- set url_root = pathto('', 1) %} -{%- if url_root == '#' %}{% set url_root = '' %}{% endif %} -{%- if not embedded and docstitle %} - {%- set titlesuffix = " — "|safe + docstitle|e %} -{%- else %} - {%- set titlesuffix = "" %} -{%- endif %} -{%- set lang_attr = 'en' if language == None else (language | replace('_', '-')) %} -{% import 'theme_variables.jinja' as theme_variables %} - - - - - - - {{ metatags }} - - {% block htmltitle %} - {{ title|striptags|e }}{{ titlesuffix }} - {% endblock %} - - {# FAVICON #} - {% if favicon %} - - {% endif %} - {# CANONICAL URL #} - {% if theme_canonical_url %} - - {% endif %} - - {# CSS #} - - {# OPENSEARCH #} - {% if not embedded %} - {% if use_opensearch %} - - {% endif %} - - {% endif %} - - - - {%- for css in css_files %} - {%- if css|attr("rel") %} - - {%- else %} - - {%- endif %} - {%- endfor %} - {%- for cssfile in extra_css_files %} - - {%- endfor %} - - {%- block linktags %} - {%- if hasdoc('about') %} - - {%- endif %} - {%- if hasdoc('genindex') %} - - {%- endif %} - {%- if hasdoc('search') %} - - {%- endif %} - {%- if hasdoc('copyright') %} - - {%- endif %} - {%- if next %} - - {%- endif %} - {%- if prev %} - - {%- endif %} - {%- endblock %} - {%- block extrahead %} {% endblock %} - - {# Keep modernizr in head - http://modernizr.com/docs/#installing #} - - - - -
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- {%- endblock %} - {% include "footer.html" %} -
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- - {% if not embedded %} - - {% if sphinx_version >= "1.8.0" %} - - {%- for scriptfile in script_files %} - {{ js_tag(scriptfile) }} - {%- endfor %} - {% else %} - - {%- for scriptfile in script_files %} - - {%- endfor %} - {% endif %} - - {% endif %} - - - - - - - - -{%- block footer %} {% endblock %} - -
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- - - - - - - - \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index a2bf64b712..40f3d64898 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -95,6 +95,7 @@ "sphinx_autodoc_typehints", "reno.sphinxext", "sphinx_panels", + "sphinx_design", "sphinx.ext.intersphinx", "nbsphinx", "autoref", @@ -177,6 +178,8 @@ # html_theme = "qiskit_sphinx_theme" # use the theme in subdir 'theme' +html_context = {"analytics_enabled": True} # enable built-in analytics + # html_sidebars = {'**': ['globaltoc.html']} html_last_updated_fmt = "%Y/%m/%d" diff --git a/docs/guides/quantum_volume.rst b/docs/guides/quantum_volume.rst new file mode 100644 index 0000000000..a180826992 --- /dev/null +++ b/docs/guides/quantum_volume.rst @@ -0,0 +1,189 @@ +Quantum Volume +============== + +Quantum Volume (QV) is a single-number metric that can be measured using +a concrete protocol on near-term quantum computers of modest size. The +QV method quantifies the largest random circuit of equal width and depth +that the computer successfully implements. Quantum computing systems +with high-fidelity operations, high connectivity, large calibrated gate +sets, and circuit rewriting toolchains are expected to have higher +quantum volumes. See `Qiskit +Textbook `__ +for an explanation on the QV method, which is described in Ref. [1, 2]. + +The Quantum Volume is determined by the largest successful circuit depth +:math:`d_{max}`, and equals to :math:`2^{d_{max}}`. In the QV experiment +we generate `QV +circuits `__ +on :math:`d` qubits, which contain :math:`d` layers, where each layer +consists of random 2-qubit unitary gates from :math:`SU(4)`, followed by +a random permutation on the :math:`d` qubit. Then these circuits run on +the quantum backend and on an ideal simulator (either ``AerSimulator`` +or ``qiskit.quantum_info.Statevector``). + +A depth :math:`d` QV circuit is successful if it has ‘mean heavy-output +probability’ > 2/3 with confidence level > 0.977 (corresponding to +z_value = 2), and at least 100 trials have been ran. + +.. jupyter-execute:: + + from qiskit_experiments.framework import BatchExperiment + from qiskit_experiments.library import QuantumVolume + from qiskit import Aer + from qiskit_aer import AerSimulator + + # For simulation + from qiskit.providers.fake_provider import FakeSydney + + backend = AerSimulator.from_backend(FakeSydney()) + +QV experiment +------------- + +To run the QV experiment we need need to provide the following QV +parameters, in order to generate the QV circuits and run them on a +backend and on an ideal simulator: + +- ``qubits``: The number of qubits or list of physical qubits for the + experiment. + +- ``trials``: The number of trials to run the quantum volume circuit + (the default is 100). + +- ``seed``: Seed or generator object for random number generation. If + ``None`` then ``default_rng`` will be used. + +- ``simulation_backend``: The simulator backend to use to generate the + expected results. the simulator must have a ``save_probabilities`` + method. If None ``AerSimulator`` simulator will be used (in case + ``AerSimulator`` is not installed ``qiskit.quantum_info.Statevector`` + will be used). + +**Note:** In some cases, 100 trials are not enough to obtain a QV +greater than 1 for the specified number of qubits. In this case, adding +more trials may reduce the error bars to allow passing the threshold. + +The analysis results of the QV Experiment are: + +- The mean heavy output probabilities (HOP) and standard deviation + +- The calculated quantum volume, which will be None if the experiment + does not pass the threshold + +Extra data included in the analysis results includes + +- The heavy HOPs for each trial + +- Confidence level (should be greater than 0.977) + +- The number of trials and depth of the QV circuits + +- Whether the QV circuit was successful + +.. jupyter-execute:: + + qubits = tuple(range(4)) # Can use specific qubits. for example [2, 4, 7, 10] + + qv_exp = QuantumVolume(qubits, seed=42) + # Transpile options like optimization_level affect only the real device run and not the simulation run + # Run options affect both simulation and real device runs + qv_exp.set_transpile_options(optimization_level=3) + + # Run experiment + expdata = qv_exp.run(backend).block_for_results() + +.. jupyter-execute:: + + # View result data + display(expdata.figure(0)) + + for result in expdata.analysis_results(): + print(result) + + +.. jupyter-execute:: + + # Print extra data + for result in expdata.analysis_results(): + print(f"\n{result.name} extra:") + for key, val in result.extra.items(): + print(f"- {key}: {val}") + + +Adding trials +------------- + +Adding more trials may reduce the error bars to allow passing the +threshold (beside the error bars - QV experiment must have at least 100 +trials to be considered successful). In case you want to add less than +100 additional trials, you can modify the amount of trials added before +re-running the experiment. + +.. jupyter-execute:: + + qv_exp.set_experiment_options(trials=60) + expdata2 = qv_exp.run(backend, analysis=None).block_for_results() + expdata2.add_data(expdata.data()) + qv_exp.analysis.run(expdata2).block_for_results() + + # View result data + display(expdata2.figure(0)) + for result in expdata2.analysis_results(): + print(result) + + +Calculating Quantum Volume using a batch experiment +--------------------------------------------------- + +Run the QV experiment with an increasing number of qubits to check what +is the maximum Quantum Volume for the specific device. To reach the real +system’s Quantum Volume, one must run more trials and additional +enhancements might be required (See Ref. [2] for details). + +.. jupyter-execute:: + + exps = [QuantumVolume(tuple(range(i)), trials=200) for i in range(3, 6)] + + batch_exp = BatchExperiment(exps) + batch_exp.set_transpile_options(optimization_level=3) + + # Run + batch_expdata = batch_exp.run(backend).block_for_results() + +Extracting the maximum Quantum Volume. + +.. jupyter-execute:: + + qv_values = [ + batch_expdata.child_data(i).analysis_results("quantum_volume").value + for i in range(batch_exp.num_experiments) + ] + + print(f"Max quantum volume is: {max(qv_values)}") + + +.. jupyter-execute:: + + for i in range(batch_exp.num_experiments): + print(f"\nComponent experiment {i}") + sub_data = batch_expdata.child_data(i) + display(sub_data.figure(0)) + for result in sub_data.analysis_results(): + print(result) + +References +---------- + +[1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and +Jay M. Gambetta, Validating quantum computers using randomized model +circuits, Phys. Rev. A 100, 032328 (2019). +https://arxiv.org/pdf/1811.12926 + +[2] Petar Jurcevic et. al. Demonstration of quantum volume 64 on +a superconducting quantum computing system, +https://arxiv.org/pdf/2008.08571 + +.. jupyter-execute:: + + import qiskit.tools.jupyter + %qiskit_copyright diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/readout_mitigation.rst new file mode 100644 index 0000000000..d7f518f11d --- /dev/null +++ b/docs/guides/readout_mitigation.rst @@ -0,0 +1,179 @@ +Readout Mitigation +================== + +Readout errors affect quantum computation during the measurement of the +qubits in a quantum device. By characterizing the readout errors, it is +possible to construct a *readout error mitigator* that is used both to +obtain a more accurate distribution of the outputs, and more accurate +measurements of expectation value for measurables. + +The readout mitigator is generated from an *assignment matrix*: a +:math:`2^n \times 2^n` matrix :math:`A` such that :math:`A_{y,x}` is the +probability to observe :math:`y` given the true outcome should be +:math:`x`. The assignment matrix is used to compute the *mitigation +matrix* used in the readout error mitigation process itself. + +A *Local readout mitigator* works under the assumption that readout +errors are mostly *local*, meaning readout errors for different qubits +are independent of each other. In this case, the assignment matrix is +the tensor product of :math:`n` :math:`2 \times 2` matrices, one for +each qubit, making it practical to store the assignment matrix in +implicit form, by storing the individual :math:`2 \times 2` assignment +matrices. The corresponding class in Qiskit is the `Local readout +mitigator `__ +in ``qiskit-terra``. + +A *Correlated readout mitigator* uses the full :math:`2^n \times 2^n` +assignment matrix, meaning it can only be used for small values of +:math:`n`. The corresponding class in Qiskit is the `Correlated readout +mitigator `__ +in ``qiskit-terra``. + +This notebook demonstrates the usage of both the local and correlated +experiments to generate the corresponding mitigators. + +.. jupyter-execute:: + + import numpy as np + import matplotlib.pyplot as plt + from qiskit import QuantumCircuit + from qiskit.visualization import plot_histogram + from qiskit_experiments.library import LocalReadoutError, CorrelatedReadoutError + # For simulation + from qiskit_aer import AerSimulator + from qiskit.providers.fake_provider import FakeParis + + from qiskit.result.mitigation.utils import ( + expval_with_stddev, + str2diag, + counts_probability_vector + ) + + backend = AerSimulator.from_backend(FakeParis()) + +.. jupyter-execute:: + + SHOTS = 1024 + qubits = [0,1,2,3] + num_qubits = len(qubits) + +Standard mitigation experiment +============================== + +The default mitigation experiment is *local*, meaning error probability +is measured individually for each qubit. The experiment generates two +circuits, one for all “0” and one for all “1” results. + +.. jupyter-execute:: + + exp = LocalReadoutError(qubits) + for c in exp.circuits(): + print(c) + + +.. jupyter-execute:: + + exp.analysis.set_options(plot=True) + result = exp.run(backend) + mitigator = result.analysis_results(0).value + +The resulting measurement matrix can be illustrated by comparing it to +the identity. + +.. jupyter-execute:: + + result.figure(0) + + +Mitigation matrices +------------------- + +The individual mitigation matrices can be read off the mitigator. + +.. jupyter-execute:: + + for m in mitigator._mitigation_mats: + print(m) + print() + + +Mitigation Example +------------------ + +.. jupyter-execute:: + + qc = QuantumCircuit(num_qubits) + qc.h(0) + for i in range(1, num_qubits): + qc.cx(i - 1, i) + qc.measure_all() + +.. jupyter-execute:: + + counts = backend.run(qc, shots=SHOTS, seed_simulator=42, method="density_matrix").result().get_counts() + unmitigated_probs = {label: count / SHOTS for label, count in counts.items()} + +.. jupyter-execute:: + + mitigated_quasi_probs = mitigator.quasi_probabilities(counts) + mitigated_stddev = mitigated_quasi_probs._stddev_upper_bound + mitigated_probs = (mitigated_quasi_probs.nearest_probability_distribution().binary_probabilities()) + +Probabilities +~~~~~~~~~~~~~ + +.. jupyter-execute:: + + legend = ['Mitigated Probabilities', 'Unmitigated Probabilities'] + plot_histogram([mitigated_probs, unmitigated_probs], legend=legend, sort="value_desc", bar_labels=False) + + +Expectation value +----------------- + +.. jupyter-execute:: + + diagonal_labels = ["ZZZZ", "ZIZI", "IZII", "1ZZ0"] + ideal_expectation = [] + diagonals = [str2diag(d) for d in diagonal_labels] + qubit_index = {i: i for i in range(num_qubits)} + unmitigated_probs_vector, _ = counts_probability_vector(unmitigated_probs, qubit_index=qubit_index) + unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, SHOTS) for d in diagonals] + mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals] + +.. jupyter-execute:: + + mitigated_expectation_values, mitigated_stddev = zip(*mitigated_expectation) + unmitigated_expectation_values, unmitigated_stddev = zip(*unmitigated_expectation) + legend = ['Mitigated Expectation', 'Unmitigated Expectation'] + fig, ax = plt.subplots() + X = np.arange(4) + ax.bar(X + 0.00, mitigated_expectation_values, yerr=mitigated_stddev, color='b', width = 0.25, label="Mitigated Expectation") + ax.bar(X + 0.25, unmitigated_expectation_values, yerr=unmitigated_stddev, color='g', width = 0.25, label="Unmitigated Expectation") + ax.set_xticks([0.125 + i for i in range(len(diagonals))]) + ax.set_xticklabels(diagonal_labels) + ax.legend() + +Correlated readout mitigation +----------------------------- + +In correlated readout mitigation on :math:`n` qubits, a circuit is +generated for each of the possible :math:`2^n` combinations of “0” and +“1”. This results in more accurate mitigation in the case where the +readout errors are correlated and not independent, but requires a large +amount of circuits and storage space, and so is infeasible for more than +a few qubits. + +.. jupyter-execute:: + + qubits = [0,3] + num_qubits = len(qubits) + exp = CorrelatedReadoutError(qubits) + for c in exp.circuits(): + print(c) + + +.. jupyter-execute:: + + import qiskit.tools.jupyter + %qiskit_copyright diff --git a/docs/guides/state_tomography.rst b/docs/guides/state_tomography.rst new file mode 100644 index 0000000000..7b7c1cd368 --- /dev/null +++ b/docs/guides/state_tomography.rst @@ -0,0 +1,187 @@ +Quantum State Tomography +======================== + +.. jupyter-execute:: + + import qiskit + from qiskit_experiments.framework import ParallelExperiment + from qiskit_experiments.library import StateTomography + + # For simulation + from qiskit_aer import AerSimulator + from qiskit.providers.fake_provider import FakeParis + + # Noisy simulator backend + backend = AerSimulator.from_backend(FakeParis()) + +State Tomography Experiment +--------------------------- + +To run a state tomography experiment we initialize the experiment with a +circuit to prepare the state to be measured. We can also pass in an +``Operator``, or a ``Statevector`` to describe the preparation circuit. + +.. jupyter-execute:: + + # Run experiments + + # GHZ State preparation circuit + nq = 2 + qc_ghz = qiskit.QuantumCircuit(nq) + qc_ghz.h(0) + qc_ghz.s(0) + for i in range(1, nq): + qc_ghz.cx(0, i) + + # QST Experiment + qstexp1 = StateTomography(qc_ghz) + qstdata1 = qstexp1.run(backend, seed_simulation=100).block_for_results() + + # Print results + for result in qstdata1.analysis_results(): + print(result) + + + +Tomography Results +~~~~~~~~~~~~~~~~~~ + +The main result for tomography is the fitted state, which is stored as a +``DensityMatrix`` object: + +.. jupyter-execute:: + + state_result = qstdata1.analysis_results("state") + print(state_result.value) + + + + +The state fidelity of the fitted state with the ideal state prepared by +the input circuit is stored in the ``"state_fidelity"`` result field. +Note that if the input circuit contained any measurements the ideal +state cannot be automatically generated and this field will be set to +``None``. + +.. jupyter-execute:: + + fid_result = qstdata1.analysis_results("state_fidelity") + print("State Fidelity = {:.5f}".format(fid_result.value)) + + + +Additional state metadata +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Additional data is stored in the tomography under the +``"state_metadata"`` field. This includes - ``eigvals``: the eigenvalues +of the fitted state - ``trace``: the trace of the fitted state - +``positive``: Whether the eigenvalues are all non-negative - +``positive_delta``: the deviation from positivity given by 1-norm of +negative eigenvalues. + +If trace rescaling was performed this dictionary will also contain a +``raw_trace`` field containing the trace before rescaling. Futhermore, +if the state was rescaled to be positive or trace 1 an additional field +``raw_eigvals`` will contain the state eigenvalues before rescaling was +performed. + +.. jupyter-execute:: + + state_result.extra + +To see the effect of rescaling we can perform a “bad” fit with very low +counts + +.. jupyter-execute:: + + # QST Experiment + bad_data = qstexp1.run(backend, shots=10, seed_simulation=100).block_for_results() + bad_state_result = bad_data.analysis_results("state") + + # Print result + print(bad_state_result) + + # Show extra data + bad_state_result.extra + + + +Tomography Fitters +------------------ + +The default fitters is ``linear_inversion``, which reconstructs the +state using *dual basis* of the tomography basis. This will typically +result in a non-positive reconstructed state. This state is rescaled to +be positive-semidefinite (PSD) by computing its eigen-decomposition and +rescaling its eigenvalues using the approach from \*J Smolin, JM +Gambetta, G Smith, Phys. Rev. Lett. 108, 070502 (2012), `open +access `__. + +There are several other fitters are included (See API documentation for +details). For example if ``cvxpy`` is installed we can use the +``cvxpy_gaussian_lstsq`` fitter which allows constraining the fit to be +PSD without requiring rescaling. + +.. jupyter-execute:: + + try: + import cvxpy + + # Set analysis option for cvxpy fitter + qstexp1.analysis.set_options(fitter='cvxpy_gaussian_lstsq') + + # Re-run experiment + qstdata2 = qstexp1.run(backend, seed_simulation=100).block_for_results() + + state_result2 = qstdata2.analysis_results("state") + print(state_result2) + print("\nextra:") + for key, val in state_result2.extra.items(): + print(f"- {key}: {val}") + + except ModuleNotFoundError: + print("CVXPY is not installed") + +Parallel Tomography Experiment +------------------------------ + +We can also use the ``qiskit_experiments.ParallelExperiment`` class to +run subsystem tomography on multiple qubits in parallel. + +For example if we want to perform 1-qubit QST on several qubits at once: + +.. jupyter-execute:: + + from math import pi + num_qubits = 5 + gates = [qiskit.circuit.library.RXGate(i * pi / (num_qubits - 1)) + for i in range(num_qubits)] + + subexps = [ + StateTomography(gate, qubits=[i]) + for i, gate in enumerate(gates) + ] + parexp = ParallelExperiment(subexps) + pardata = parexp.run(backend, seed_simulation=100).block_for_results() + + for result in pardata.analysis_results(): + print(result) + +View component experiment analysis results + +.. jupyter-execute:: + + for i, expdata in enumerate(pardata.child_data()): + state_result_i = expdata.analysis_results("state") + fid_result_i = expdata.analysis_results("state_fidelity") + + print(f'\nPARALLEL EXP {i}') + print("State Fidelity: {:.5f}".format(fid_result_i.value)) + print("State: {}".format(state_result_i.value)) + + +.. jupyter-execute:: + + import qiskit.tools.jupyter + %qiskit_copyright diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_experiment.rst new file mode 100644 index 0000000000..fed3538b63 --- /dev/null +++ b/docs/guides/t1_experiment.rst @@ -0,0 +1,142 @@ +The T1 Experiment +=========== + +Background +=========== + +In a :math:`T_1` experiment, we measure an excited qubit after a delay. +Due to decoherence processes (e.g. amplitude damping channel), it is +possible that, at the time of measurement, after the delay, the qubit +will not be excited anymore. The larger the delay time is, the more +likely is the qubit to fall to the ground state. The goal of the +experiment is to characterize the decay rate of the qubit towards the +ground state. + +We start by fixing a delay time :math:`t` and a number of shots +:math:`s`. Then, by repeating :math:`s` times the procedure of exciting +the qubit, waiting, and measuring, we estimate the probability to +measure :math:`|1\rangle` after the delay. We repeat this process for a +set of delay times, resulting in a set of probability estimates. + +In the absence of state preparation and measurement errors, the +probability to measure \|1> after time :math:`t` is :math:`e^{-t/T_1}`, +for a constant :math:`T_1` (the coherence time), which is our target +number. Since state preparation and measurement errors do exist, the +qubit’s decay towards the ground state assumes the form +:math:`Ae^{-t/T_1} + B`, for parameters :math:`A, T_1`, and :math:`B`, +which we deduce form the probability estimates. To this end, the +:math:`T_1` experiment internally calls the ``curve_fit`` method of +``scipy.optimize``. + +The following code demonstrates a basic run of a :math:`T_1` experiment +for qubit 0. + +.. jupyter-execute:: + + import numpy as np + from qiskit.qobj.utils import MeasLevel + from qiskit_experiments.framework import ParallelExperiment + from qiskit_experiments.library import T1 + from qiskit_experiments.library.characterization.analysis.t1_analysis import T1KerneledAnalysis + + # A T1 simulator + from qiskit.providers.fake_provider import FakeVigo + from qiskit_aer import AerSimulator + from qiskit_aer.noise import NoiseModel + + # A kerneled data simulator + from qiskit_experiments.test.mock_iq_backend import MockIQBackend + from qiskit_experiments.test.mock_iq_helpers import MockIQT1Helper + + # Create a pure relaxation noise model for AerSimulator + noise_model = NoiseModel.from_backend( + FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False + ) + + # Create a fake backend simulator + backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) + + # Look up target T1 of qubit-0 from device properties + qubit0_t1 = backend.properties().t1(0) + + # Time intervals to wait before measurement + delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) + # Create an experiment for qubit 0 + # with the specified time intervals + exp = T1(qubit=0, delays=delays) + + # Set scheduling method so circuit is scheduled for delay noise simulation + exp.set_transpile_options(scheduling_method='asap') + + # Run the experiment circuits and analyze the result + exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() + + # Print the result + display(exp_data.figure(0)) + for result in exp_data.analysis_results(): + print(result) + + + + +:math:`T_1` experiments with kerneled measurement +============================================ + +:math:`T_1` experiments can also be done with kerneled measurements. +If we set the run option `meas_level=MeasLevel.KERNELED`, the job +will not discriminate the data and will not label it. In the T1 experiment, +since we know that :math:`P(1|t=0)=1`, we will add a circuit with delay=0, +and another circuit with a very large delay. In this configuration we know that the data starts from +a point [I,Q] that is close to a logical value '1' and ends at a point [I,Q] +that is close to a logical value '0'. + + +.. jupyter-execute:: + + # Experiment + ns = 1e-9 + mu = 1e-6 + + # qubit properties + t1 = [45 * mu, 45 * mu] + t2 = [value/2 for value in t1] + + # we will guess that our guess is 10% off the exact value of t1 for qubit 0. + t1_estimated_shift = t1[0]/10 + + # We use log space for the delays because of the noise properties + delays = np.logspace(1, 11, num=23, base=np.exp(1)) + delays *= ns + + # Adding circuits with delay=0 and long delays so the centers in the IQ plane won't be misplaced. + # Without this, the fitting can provide wrong results. + delays = np.insert(delays, 0, 0) + delays = np.append(delays, [t1[0]*3]) + + num_qubits = 2 + num_shots = 2048 + + backend = MockIQBackend( + MockIQT1Helper(t1=t1, iq_cluster_centers=[((-5.0, -4.0), (-5.0, 4.0)), ((3.0, 1.0), (5.0, -3.0))] + , iq_cluster_width=[1.0, 2.0]) + ) + + # Creating a T1 experiment + expT1_kerneled = T1(0, delays) + expT1_kerneled.analysis = T1KerneledAnalysis() + expT1_kerneled.analysis.set_options(p0={"amp": 1, "tau": t1[0] + t1_estimated_shift, "base": 0}) + + # Running the experiment + expdataT1_kerneled = expT1_kerneled.run(backend=backend, meas_return="avg", + meas_level=MeasLevel.KERNELED, + shots=num_shots).block_for_results() + + # Displaying results + display(expdataT1_kerneled.figure(0)) + for result in expdataT1_kerneled.analysis_results(): + print(result) + +.. jupyter-execute:: + + import qiskit.tools.jupyter + %qiskit_copyright diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst new file mode 100644 index 0000000000..e10f5c83d9 --- /dev/null +++ b/docs/howtos/cloud_service.rst @@ -0,0 +1,253 @@ +How to save and load experiment data with the cloud service +=========================================================== + +.. note:: + This recipe is only for those who have access to the cloud service. You can + check whether you have access by logging into the Quantum Cloud interface + and seeing whether you can access the `database `__. + +Problem +------- + +You want to save and retrieve experiment data from the cloud service. + +Solution +-------- + +Saving +~~~~~~ + +You will need to have :mod:`qiskit-ibmq-provider` installed locally +and an account in the Qiskit cloud service. You must run the experiment on a real IBM +backend to be able to save it. You can save the experiment data by calling ``save()``: + +.. jupyter-execute:: + :hide-code: + :hide-output: + + from qiskit.test.ibmq_mock import mock_get_backend + backend = mock_get_backend('FakeLima') + +.. jupyter-execute:: + + from qiskit import IBMQ + IBMQ.load_account() + provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main") + backend = provider.get_backend("ibmq_lima") + + from qiskit_experiments.library.characterization import T1 + import numpy as np + + t1_delays = np.arange(1e-6, 600e-6, 50e-6) + + exp = T1(qubit=0, delays=t1_delays) + + t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() + t1_expdata.save() + +.. jupyter-execute:: + :hide-code: + + print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") + +Loading +~~~~~~~ + +Let’s load a `previous T1 +experiment `__, +which we’ve made public by editing the ``Share level`` field: + +.. jupyter-execute:: + :hide-output: + :raises: + + from qiskit_experiments.framework.experiment_data import ExperimentData + service = ExperimentData.get_service_from_backend(backend) + load_expdata = ExperimentData.load("9640736e-d797-4321-b063-d503f8e98571", service) + +To display the figure, which is serialized into a string, we need the +``SVG`` library: + +.. jupyter-execute:: + :hide-output: + :raises: + + from IPython.display import SVG + SVG(load_expdata.figure(0).figure) + +.. image:: ./experiment_cloud_service/t1_loaded.png + +The analysis results have been retrieved as well: + +.. jupyter-execute:: + :hide-output: + :raises: + + for result in load_expdata.analysis_results(): + print(result) + +.. jupyter-execute:: + :hide-code: + + print("""AnalysisResult + - name: T1 + - value: 0.0001040+/-0.0000028 + - χ²: 0.8523786276663019 + - quality: good + - extra: <1 items> + - device_components: ['Q0'] + - verified: False + AnalysisResult + - name: @Parameters_T1Analysis + - value: CurveFitResult: + - fitting method: least_squares + - number of sub-models: 1 + * F_exp_decay(x) = amp * exp(-x/tau) + base + - success: True + - number of function evals: 9 + - degree of freedom: 9 + - chi-square: 7.671407648996717 + - reduced chi-square: 0.8523786276663019 + - Akaike info crit.: 0.6311217041870707 + - Bayesian info crit.: 2.085841653551072 + - init params: + * amp = 0.923076923076923 + * tau = 0.00016946294665316433 + * base = 0.033466533466533464 + - fit params: + * amp = 0.9266620487665083 ± 0.007096409569790425 + * tau = 0.00010401411623191737 ± 2.767679521974391e-06 + * base = 0.036302726197354626 ± 0.0037184540724124844 + - correlations: + * (tau, base) = -0.6740808746060173 + * (amp, base) = -0.4231810882291163 + * (amp, tau) = 0.09302612202500576 + - quality: good + - device_components: ['Q0'] + - verified: False""") + +Discussion +---------- + +Calling ``save()`` before the experiment is complete will +instantiate an experiment entry in the database, but it will not have +complete data. To fix this, you can call ``save()`` again once the +experiment is done running. + +.. jupyter-execute:: + :hide-code: + :hide-output: + + from qiskit.test.ibmq_mock import mock_get_backend + backend = mock_get_backend('FakeLima') + + +.. jupyter-execute:: + + from qiskit import IBMQ + IBMQ.load_account() + provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main") + backend = provider.get_backend("ibmq_lima") + + t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() + t1_expdata.save() + +.. jupyter-execute:: + :hide-code: + + print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") + + +You can also view the results at the `IBM Quantum Experiments +pane `__ +on the cloud. + +By default, the interface displays all experiments you have privilege to +see, but this link shows your own experiments. You can change that +setting by clicking on the All Experiments dropdown. You can also filter +by device, date, provider, and result by clicking on the filter icon. + +.. image:: ./experiment_cloud_service/filter.png + +Individual experiment pages show the plot, and one or more important +analysis results, which for the :math:`T_1` experiment is the fitted +:math:`T_1` value. + +.. image:: ./experiment_cloud_service/t1_experiment.png + +The metadata field shows experiment metadata included in the ``ExperimentData`` object. + +.. image:: ./experiment_cloud_service/metadata.png + +You can change the quality and verify/unverify the results upon +selection of an analysis result. Quality is an automatic parameter +generated by the experiment analysis based on pre-set criteria. The verification field is for a +human to determine whether the result is acceptable. + +.. image:: ./experiment_cloud_service/verify_experiment.png + + +Auto-saving an experiment +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``auto_save`` feature automatically saves changes to the `ExperimentData` object to +the cloud service whenever it's updated. + +.. jupyter-execute:: + :hide-output: + + exp = T1(qubit=0, delays=t1_delays) + + t1_expdata = exp.run(backend=backend, shots=1000) + t1_expdata.auto_save = True + t1_expdata.block_for_results() + +.. jupyter-execute:: + :hide-code: + + print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/cdaff3fa-f621-4915-a4d8-812d05d9a9ca") + print("") + +Deleting an experiment +~~~~~~~~~~~~~~~~~~~~~~ + +Both figures and analysis results can be deleted. Note that unless you +have auto save on, the update has to be manually saved to the remote +database by calling ``save()``. Because there are two analysis results, one for +the T1 parameter and one for the curve fitting results, we delete twice. + +.. jupyter-execute:: + :hide-output: + + t1_expdata.delete_figure(0) + t1_expdata.delete_analysis_result(0) + t1_expdata.delete_analysis_result(0) + +.. jupyter-execute:: + :hide-code: + + print("Are you sure you want to delete the experiment plot? [y/N]: y") + print("Are you sure you want to delete the analysis result? [y/N]: y") + print("Are you sure you want to delete the analysis result? [y/N]: y") + +The web interface shows that both the figure and analysis result have been +deleted: |t1_deleted.png| + +.. |t1_deleted.png| image:: ./experiment_cloud_service/t1_deleted.png + +Tagging and sharing experiments +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Tags and notes can be added to experiments to help identify specific experiments in the interface. +For example, an experiment can be tagged and made public with the following code. + +.. jupyter-execute:: + + t1_expdata.tags = ['tag1', 'tag2'] + t1_expdata.share_level = "public" + t1_expdata.notes = "Example note." + +These fields can also be updated in the web interface. For more information about +using the interface, consult its +`documentation `__. + diff --git a/docs/guides/experiment_cloud_service/filter.png b/docs/howtos/experiment_cloud_service/filter.png similarity index 100% rename from docs/guides/experiment_cloud_service/filter.png rename to docs/howtos/experiment_cloud_service/filter.png diff --git a/docs/guides/experiment_cloud_service/metadata.png b/docs/howtos/experiment_cloud_service/metadata.png similarity index 100% rename from docs/guides/experiment_cloud_service/metadata.png rename to docs/howtos/experiment_cloud_service/metadata.png diff --git a/docs/guides/experiment_cloud_service/t1_deleted.png b/docs/howtos/experiment_cloud_service/t1_deleted.png similarity index 100% rename from docs/guides/experiment_cloud_service/t1_deleted.png rename to docs/howtos/experiment_cloud_service/t1_deleted.png diff --git a/docs/guides/experiment_cloud_service/t1_experiment.png b/docs/howtos/experiment_cloud_service/t1_experiment.png similarity index 100% rename from docs/guides/experiment_cloud_service/t1_experiment.png rename to docs/howtos/experiment_cloud_service/t1_experiment.png diff --git a/docs/guides/experiment_cloud_service/t1_loaded.png b/docs/howtos/experiment_cloud_service/t1_loaded.png similarity index 100% rename from docs/guides/experiment_cloud_service/t1_loaded.png rename to docs/howtos/experiment_cloud_service/t1_loaded.png diff --git a/docs/guides/experiment_cloud_service/verify_experiment.png b/docs/howtos/experiment_cloud_service/verify_experiment.png similarity index 100% rename from docs/guides/experiment_cloud_service/verify_experiment.png rename to docs/howtos/experiment_cloud_service/verify_experiment.png diff --git a/docs/guides/experiment_cloud_service/web_tags_share.png b/docs/howtos/experiment_cloud_service/web_tags_share.png similarity index 100% rename from docs/guides/experiment_cloud_service/web_tags_share.png rename to docs/howtos/experiment_cloud_service/web_tags_share.png diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index a6723079da..d27a1a70e1 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -1,6 +1,6 @@ -##################### +############## How-To Recipes -##################### +############## This section of the documentation provides concrete recipes for how to do specific useful actions in Qiskit Experiments. @@ -8,7 +8,7 @@ useful actions in Qiskit Experiments. How to... .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :glob: * diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst index 83d8dd0bb6..f48469417f 100644 --- a/docs/howtos/newexperimentdata.rst +++ b/docs/howtos/newexperimentdata.rst @@ -1,8 +1,20 @@ How to instantiate a new experiment data object -============ +=============================================== + +Problem +------- + +Sometimes due to events like a lost connection, the :class:`.ExperimentData` class that is returned +upon execution completion of an experiment may not contain correct results. There +are also times when you would like to rerun the analysis for existing experiments with +previously run jobs. + +Solution +-------- If you want to instantiate a new experiment data object from an existing experiment and -jobs that finished execution successfully: +jobs that finished execution successfully, you need to know the exact experiment you +ran and its options, as well as the IDs of the jobs that were executed. .. code-block:: python @@ -16,4 +28,16 @@ jobs that finished execution successfully: experiment.analysis.run(data) data.block_for_results() -``data`` will be the new experiment data class. \ No newline at end of file +``data`` will be the new experiment data class. + +Discussion +---------- + +The job IDs can be retrieved from the original experiment data object using the +``job_ids`` attribute. + +See Also +-------- + +* `Saving and loading experiment data locally `_ +* `Saving and loading experiment data with the cloud service `_ diff --git a/docs/index.rst b/docs/index.rst index fd318f7954..04307bd9e7 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,47 +1,101 @@ ################################ -Qiskit Experiments Documentation +Qiskit Experiments documentation ################################ -About Qiskit Experiments -======================== +.. warning:: + + This package is still under active development and it is very likely + that there will be breaking API changes in future releases. + If you encounter any bugs, please open an issue on + `Github `_. Qiskit Experiments provides both a :doc:`library ` of standard quantum characterization, calibration, and verification experiments, and a general :doc:`framework ` for implementing custom experiments which can be run on quantum devices through Qiskit. -Experiments run on `IBMQ Provider `_ -backends can be stored and retrieved from an online experiment -:doc:`database `. +We've divided up the documentation into four sections with different purposes: -.. warning:: +.. grid:: 2 + :gutter: 3 - This package is still under active development and it is very likely - that there will be breaking API changes in future releases. - If you encounter any bugs please open an issue on - `Github `_ + .. grid-item-card:: + :img-top: _static/tutorials.svg + + Learning Tutorials + ^^^^^^^^^^^^^^^^^^ + + These step-by-step tutorials teach the fundamentals of the packaage and + are suitable for getting started. + + +++ + + .. button-ref:: tutorials/index + :expand: + :color: secondary + :click-parent: + + To the learning tutorials + + .. grid-item-card:: + :img-top: _static/howtos.svg + + How-To Recipes + ^^^^^^^^^^^^^^ + These individual recipes provide short and direct solutions to some commonly + asked questions for Qiskit Experiments users. -Table of Contents -================= + +++ -We've divided up the documentation into areas by purpose. The tutorials are -learning-based documentation suitable for getting started. The how-to recipes -are short and direct instructions for solving specific problems. The experiment -guides have in-depth explanations for key experiments in the package, and discuss -advanced usage and options that would be of interest to experimentalists and -researchers. + .. button-ref:: howtos/index + :expand: + :color: secondary + :click-parent: -.. nbgallery:: - :glob: + To the how-to recipes + + .. grid-item-card:: + :img-top: _static/guides.svg + + Experiment Guides + ^^^^^^^^^^^^^^^^^ + + These are in-depth guides to key experiments in the package, describing + their background, principle, and how to run them in Qiskit Experiments. + + +++ + + .. button-ref:: guides/index + :expand: + :color: secondary + :click-parent: + + To the experiment guides + + + .. grid-item-card:: + :img-top: _static/api.svg + + API Reference + ^^^^^^^^^^^^^ + + This is a detailed description of every module, method, and function in + Qiskit Experiments and how to use them, suitable for those working closely + with specific parts of the package or writing your custom code. + + +++ + + .. button-ref:: apidocs/index + :expand: + :color: secondary + :click-parent: + + To the API reference - Tutorials - How-To Recipes - Experiment Guides - API References - Release Notes .. Hiding - Indices and tables :ref:`genindex` :ref:`modindex` :ref:`search` + diff --git a/docs/tutorials/Installation.rst b/docs/tutorials/Installation.rst deleted file mode 100644 index c48e8da53a..0000000000 --- a/docs/tutorials/Installation.rst +++ /dev/null @@ -1,60 +0,0 @@ -Installation -============= - -Official Qiskit Experiments releases can be installed via the python package manager -``pip``. - -.. code-block:: python - - python -m pip install qiskit-experiments - -If you want to install the most up-to-date version instead (may not be stable), you can -install the latest main branch: - -.. code-block:: python - - python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git - -If you want to develop the package, you can install Qiskit Experiments from source by -cloning the repository: - -.. code-block:: python - - git clone https://github.com/Qiskit/qiskit-experiments.git - python -m pip install -e qiskit-experiments - -The ``-e`` option will keep your installed package up to date as you make or pull new -changes. - -Running Your First Experiment -============================= - -Let's run a T1 experiment. FIrst, we have to import the T1 experiment from the -Qiskit Experiments library: - -.. code-block:: python - - from qiskit_experiments.library import T1 - from qiskit_aer import AerSimulator - import numpy as np - -Instantiate the backend and the experiment: - -.. code-block:: python - - backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) - qubit0_t1 = backend.properties().t1(0) - - delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) - exp = T1(qubit=0, delays=delays)exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() - -Run and display results: - -.. code-block:: python - - exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() - - # Print the result - display(exp_data.figure(0)) - for result in exp_data.analysis_results(): - print(result) \ No newline at end of file diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst new file mode 100644 index 0000000000..5b4b231041 --- /dev/null +++ b/docs/tutorials/custom_experiment.rst @@ -0,0 +1,22 @@ +Writing a custom experiment +=========================== + +In this tutorial, we'll use what we've learned so far to make a full experiment from +the :class:`.BaseExperiment` template. + +A randomized measurement experiment +=================================== + + +This experiment creates a list of copies of an input circuit +and randomly samples an N-qubit Paulis to apply to each one before +a final N-qubit Z-basis measurement to randomized the expected +ideal output bitstring in the measured. + +The analysis uses the applied Pauli frame of a randomized +measurement experiment to de-randomize the measured counts +and combine across samples to return a single counts dictionary +the original circuit. + +This has the effect of Pauli-twirling and symmetrizing the +measurement readout error. \ No newline at end of file diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst new file mode 100644 index 0000000000..0941b9b2d2 --- /dev/null +++ b/docs/tutorials/data_processor.rst @@ -0,0 +1,28 @@ +The Data Processor +================== + +Data processing is the act of taking the data returned by the backend and +converting it into a format that can be analyzed. +It is implemented as a chain of data processing steps that transform various input data, +e.g. IQ data, into a desired format, e.g. population, which can be analyzed. + +These data transformations may consist of multiple steps, such as kerneling and discrimination. +Each step is implemented by a :class:`~qiskit_experiments.data_processing.data_action.DataAction` +also called a `node`. + +The data processor implements the :meth:`__call__` method. Once initialized, it +can thus be used as a standard python function: + +.. code-block:: python + + processor = DataProcessor(input_key="memory", [Node1(), Node2(), ...]) + out_data = processor(in_data) + +The data input to the processor is a sequence of dictionaries each representing the result +of a single circuit. The output of the processor is a numpy array whose shape and data type +depend on the combination of the nodes in the data processor. + +Uncertainties that arise from quantum measurements or finite sampling can be taken into account +in the nodes: a standard error can be generated in a node and can be propagated +through the subsequent nodes in the data processor. +Correlation between computed values is also considered. diff --git a/docs/tutorials/images/experimentarch.png b/docs/tutorials/images/experimentarch.png new file mode 100644 index 0000000000000000000000000000000000000000..de36d1b7022995827bf1de1db3c6ec89978c3bd3 GIT binary patch literal 221072 zcmeFZcT`hb*EgyNM-&AS0i^{61gRF9bPpmT0xHs*iu5MZ2@oSTY7hhjq$mPXL+?pY zgb1N1C3GUa69^@c`o(kfobVjq$M28(j(f+rXE-$5z1Lo4t~r0Rt{rwwU;E&Gp8dOa z?K-G)`QnXTyY{y1+Qp*A{2T2~o?FNW?Ps^w4ej4|6?O2<(f;$u&Q!->l?epzPDl6wd<+VF2?U|%xS;hF0Xw1=da&A z8N2`PF@xbp>%A=*dwyKAsC|2nGeXy$_OtKaWizi`yUsRz`@380uKCceT^hS|E?&6l 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other than the 0 <-> 1 transition for the :math:`\pi` rotation. +In order to do this, we need to create a subclass, shown below. + +.. code-block:: + + class HigherOrderFineXAmplitude(FineXAmplitude): + def _pre_circuit(self) -> QuantumCircuit: + """Return a preparation circuit. + + This method can be overridden by subclasses e.g. to calibrate schedules on + transitions other than the 0 <-> 1 transition. + """ + circuit = QuantumCircuit(1) + + circuit.x(0) + + if self.experiment_options.add_sx: + circuit.sx(0) + + if self.experiment_options.sx_schedule is not None: + sx_schedule = self.experiment_options.sx_schedule + circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) + circuit.barrier() + + return circuit + +In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate on higher energy transitions by using an initial X gate to populate the first excited state. + +Using the Subclass +================== + +Now, we can use our new subclass as we would the original parent class. +Pictured below are the results from following the Fine amplitude calibration tutorial for detecting an over-rotated pulse using our new ``HigherOrderFineXAmplitude`` class in place of the original ``FineXAmplitude`` class. +You can try this for yourself and verify that your results are similar. + +.. code-block:: + + DbAnalysisResultV1 + - name: d_theta + - value: -0.020710672666906425 ± 0.0012903658449026907 + - χ²: 0.7819653845899581 + - quality: good + - device_components: ['Q0'] + - verified: False \ No newline at end of file diff --git a/docs/tutorials/single_qubit_cals.rst b/docs/tutorials/single_qubit_cals.rst new file mode 100644 index 0000000000..9ba6aaf600 --- /dev/null +++ b/docs/tutorials/single_qubit_cals.rst @@ -0,0 +1,294 @@ +######################################### +Run a Single-Qubit Calibration Experiment +######################################### + +To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). + +Each experiment usually provides additional information about the system used in subsequent experiments. + +.. jupyter-execute:: + + import numpy as np + + import qiskit.pulse as pulse + from qiskit.circuit import Parameter + + from qiskit_experiments.calibration_management import BackendCalibrations + +On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. + +.. jupyter-execute:: + + # from qiskit import IBMQ + # IBMQ.load_account() + # provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') + # backend = provider.get_backend('ibmq_armonk') + +We can verify whether the backend supports Pulse features by checking the backend configuration. + +.. jupyter-execute:: + + # backend_config = backend.configuration() + # assert backend_config.open_pulse, "Backend doesn't support Pulse" + +On the other hand we can also use a mock backend in case no IBM Quantum Experience credentials found. For this tutorial, we will use mock backends prepared for each experiment. + +To use in the experiments we first need to define template schedule to calibrate for `x` pulse. + +.. jupyter-execute:: + + def setup_cals(backend) -> BackendCalibrations: + """A function to instantiate calibrations and add a couple of template schedules.""" + cals = BackendCalibrations(backend) + + dur = Parameter("dur") + amp = Parameter("amp") + sigma = Parameter("σ") + beta = Parameter("β") + drive = pulse.DriveChannel(Parameter("ch0")) + + # Define and add template schedules. + with pulse.build(name="x") as x: + pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) + + cals.add_schedule(x, num_qubits=1) + + return cals + + def add_parameter_guesses(cals: BackendCalibrations): + + """Add guesses for the parameter values to the calibrations.""" + for sched in ["x"]: + print(sched) + cals.add_parameter_value(80, "σ", schedule=sched) + cals.add_parameter_value(0.5, "β", schedule=sched) + cals.add_parameter_value(320, "dur", schedule=sched) + cals.add_parameter_value(0.5, "amp", schedule=sched) + +=================================== +1. Finding qubits with spectroscopy +=================================== +Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. + +We start with a mock backend. + +.. jupyter-execute:: + + from qiskit_experiments.test.test_qubit_spectroscopy import SpectroscopyBackend + spec_backend = SpectroscopyBackend() + +We then setup calibrations for the backend. + +.. jupyter-execute:: + + cals = setup_cals(spec_backend) # Block until our job and its post processing finish. + add_parameter_guesses(cals) + +We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal + + qubit = 0 + freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] + frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) + spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) + +.. jupyter-execute:: + + circuit = spec.circuits()[0] + circuit.draw() + +We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. + +.. jupyter-execute:: + + spec_data = spec.run().block_for_results() + spec_data.figure(0) + +We can see the analysis results + +.. jupyter-execute:: + + print(spec_data.analysis_results("f01")) + +======================================================== +2. Calibrating the pulse amplitudes with Rabi experiment +======================================================== +We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. + +First we define the mock backend. + +.. jupyter-execute:: + + from qiskit_experiments.test.mock_iq_backend import RabiBackend + rabi_backend = RabiBackend() + +We then setup calibrations for the backend. + +.. jupyter-execute:: + + cals = setup_cals(rabi_backend) + add_parameter_guesses(cals) + +We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. + +We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + +If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration import RoughAmplitudeCal + + qubit = 0 + + rabi = RoughAmplitudeCal(qubit, cals) + +The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. + +.. jupyter-execute:: + + rabi.circuits()[0].draw() + +After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. + +.. jupyter-execute:: + + rabi_data = rabi.run(rabi_backend) + rabi_data.block_for_results() # Block until our job and its post processing finish. + print(rabi_data) + +.. jupyter-execute:: + + rabi_data.figure(0) + +In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. + +.. jupyter-execute:: + + pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 + print(pi_pulse_amplitude) + +================================== +3. Saving and loading calibrations +================================== + +The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. + +.. code-block:: python + + cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") + +After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. + +.. code-block:: python + + from qiskit_experiments.test.mock_iq_backend import RabiBackend + rabi_backend = RabiBackend() + cals = BackendCalibrations(rabi_backend) + cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") + +======================================================= +4. Using the Calibrated Amplitude in Another Experiment +======================================================= +------------------------------------------------------ +4.1. Calibrating the value of the DRAG coefficient +------------------------------------------------------ + +A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage +to a neighbouring transition. It is a standard pulse with an additional derivative +component. It is designed to reduce the frequency spectrum of a normal pulse near +the $|1\rangle$ - $|2\rangle$ transition, reducing the chance of leakage +to the $|2\rangle$ state. The optimal value of the DRAG parameter is chosen to +minimize both leakage and phase errors resulting from the AC Stark shift. +The pulse envelope is $f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)$. +Here, $\Omega_x$ is the envelop of the in-phase component of the pulse and +$\beta$ is the strength of the quadrature which we refer to as the DRAG +parameter and seek to calibrate in this experiment. +The DRAG calibration will run +several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated +$N$ times. Here, Rp is a rotation with a positive angle and Rm is the same rotation +with a negative amplitude. + +We use a mock backend in case no IBM credentials found. + +.. jupyter-execute:: + + from qiskit_experiments.test.mock_iq_backend import DragBackend + drag_backend = DragBackend(gate_name="Drag(x)") + +We define the template schedule for `x` pulse using previous methods. + +Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. + +.. jupyter-execute:: + + cals = setup_cals(drag_backend) + add_parameter_guesses(cals) + +We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + +If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. + +.. jupyter-execute:: + + from qiskit_experiments.library import RoughDragCal + drag = RoughDragCal(qubit, cals) + +.. jupyter-execute:: + + drag_data = drag.run(drag_backend) + drag_data.block_for_results() + +.. jupyter-execute:: + + drag_data.figure(0) + +================== +5. Miscalibrations +================== + +In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. + +Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. + +We first define a simple circuit that contains an X gate and measurement. + +.. jupyter-execute:: + + from qiskit import QuantumCircuit + + circ = QuantumCircuit(1, 1) + circ.x(0) + circ.measure(0, 0) + circ.draw() + +Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. + +.. jupyter-execute:: + + from qiskit import pulse, transpile + from qiskit.test.mock import FakeArmonk + from qiskit.pulse.library import Constant + backend = FakeArmonk() + + # build a simple circuit that only contain one x gate and measurement + circ = QuantumCircuit(1, 1) + circ.x(0) + circ.measure(0, 0) + with pulse.build(backend) as my_schedule: + pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse + + circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule + circ = transpile(circ, backend) + circ.draw(idle_wires=False) + +Execute our circuit + +.. jupyter-execute:: + + result = backend.run(transpile(circ, backend), shots=1000).result() + counts = result.get_counts(circ) + print(counts) \ No newline at end of file diff --git a/qiskit_experiments/framework/backend_data.py b/qiskit_experiments/framework/backend_data.py index 9c242247db..f80d2962e0 100644 --- a/qiskit_experiments/framework/backend_data.py +++ b/qiskit_experiments/framework/backend_data.py @@ -246,8 +246,10 @@ def num_qubits(self): @property def is_simulator(self): """Returns True given an indication the backend is a simulator + .. note:: - Note: for `BackendV2` we sometimes cannot be sure, because it lacks + + For `BackendV2` we sometimes cannot be sure, because it lacks a `simulator` field, as was present in `BackendV1`'s configuration. We still check whether the backend inherits `FakeBackendV2`, for either of its existing implementations in Terra. diff --git a/qiskit_experiments/framework/experiment_data.py b/qiskit_experiments/framework/experiment_data.py index 477018d3d8..8da1056140 100644 --- a/qiskit_experiments/framework/experiment_data.py +++ b/qiskit_experiments/framework/experiment_data.py @@ -148,8 +148,10 @@ class ExperimentData: This class handles the following: 1. Storing the data related to an experiment - the experiment's metadata, - the analysis results and the figures + the analysis results and the figures + 2. Managing jobs and adding data from jobs automatically + 3. Saving/Loading data from the result database The field `db_data` is a dataclass (`ExperimentDataclass`) containing diff --git a/qiskit_experiments/test/__init__.py b/qiskit_experiments/test/__init__.py index 9d48bbc1c8..4ed8c150c1 100644 --- a/qiskit_experiments/test/__init__.py +++ b/qiskit_experiments/test/__init__.py @@ -11,9 +11,9 @@ # that they have been altered from the originals. """ -================================================================= +================================================================== Qiskit Experiments Test Utilities (:mod:`qiskit_experiments.test`) -================================================================= +================================================================== .. currentmodule:: qiskit_experiments.test diff --git a/requirements-dev.txt b/requirements-dev.txt index d294ea041f..5c0e83e4aa 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -4,8 +4,9 @@ astroid==2.5 pylint==2.7.1 jinja2==3.0.3 Sphinx>=1.8.3 -qiskit-sphinx-theme>=1.6 -sphinx-autodoc-typehints<=1.20.2 +qiskit-sphinx-theme>=1.10 +sphinx-autodoc-typehints +sphinx-design pygments>=2.4 reno>=3.4.0 sphinx-panels From acbd986f2014e15ddac27c4666198392f76c4a39 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 24 Nov 2022 13:44:27 -0500 Subject: [PATCH 11/64] try pinning sphinx version --- docs/_ext/autodoc_experiment.py | 5 +- docs/_static/style.css | 10 +- docs/guides/t2hahn_characterization.rst | 15 +- docs/guides/tphi_characterization.rst | 4 +- docs/index.rst | 12 +- docs/tutorials/curve_analysis.rst | 442 ++++++++++++++++++++++++ docs/tutorials/index.rst | 17 +- docs/tutorials/intro.rst | 20 +- docs/tutorials/learning.rst | 179 +++------- docs/tutorials/visualization.rst | 82 +++++ requirements-dev.txt | 3 +- 11 files changed, 620 insertions(+), 169 deletions(-) create mode 100644 docs/tutorials/curve_analysis.rst create mode 100644 docs/tutorials/visualization.rst diff --git a/docs/_ext/autodoc_experiment.py b/docs/_ext/autodoc_experiment.py index a243b7cde9..978b52dc24 100644 --- a/docs/_ext/autodoc_experiment.py +++ b/docs/_ext/autodoc_experiment.py @@ -39,7 +39,10 @@ def add_content(self, more_content: Any, no_docstring: bool = False) -> None: sourcename = self.get_sourcename() try: - class_doc, init_doc = self.get_doc() + if self.get_doc() is not None: + class_doc, init_doc = self.get_doc() + else: + return except ValueError: raise QiskitError( f"Documentation of {self.name} doesn't match with the expected format." diff --git a/docs/_static/style.css b/docs/_static/style.css index da501bd092..99558dde89 100644 --- a/docs/_static/style.css +++ b/docs/_static/style.css @@ -11,9 +11,13 @@ } .sd-card-img-top, -article.pytorch-article img { - height: 40%; - width: 40%; +.sd-card img { + height: 20%; + width: 20%; margin-left: auto; margin-right: auto; +} + +.sd-card-header .sd-card-text { + font-weight: bold; } \ No newline at end of file diff --git a/docs/guides/t2hahn_characterization.rst b/docs/guides/t2hahn_characterization.rst index 0d2256b1a3..0b420cd7ae 100644 --- a/docs/guides/t2hahn_characterization.rst +++ b/docs/guides/t2hahn_characterization.rst @@ -42,12 +42,13 @@ for the qubit frequency estimation. The circuit used for an experiment with :math:`N` echoes comprises the following components: -  1.\ :math:`Rx\left(\frac{\pi}{2} \right)` gate   2. :math:`N` times -Echo sequence :     (a) :math:`Delay \left(t_{0} \right)` gate     (b) -:math:`Rx \left(\pi \right)` gate     (c) -:math:`Delay \left(t_{0} \right)` gate   3. -:math:`Rx \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the -number of echoes)   4. Measurement gate +1. :math:`Rx\left(\frac{\pi}{2} \right)` gate +2. :math:`N` times echo sequence: + (a) :math:`Delay \left(t_{0} \right)` gate + (b) :math:`Rx \left(\pi \right)` gate + (c) :math:`Delay \left(t_{0} \right)` gate +3. :math:`Rx \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the number of echoes) +4. Measurement gate The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the @@ -108,7 +109,7 @@ The resulting graph will have the form: print(result) -2. Providing initial user estimates +1. Providing initial user estimates ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The user can provide initial estimates for the parameters to help the diff --git a/docs/guides/tphi_characterization.rst b/docs/guides/tphi_characterization.rst index 63a13313c1..3c84dcfeab 100644 --- a/docs/guides/tphi_characterization.rst +++ b/docs/guides/tphi_characterization.rst @@ -1,5 +1,5 @@ -Experiment: :math:`T_\varphi` characterization -============================================== +Tφ Characterization +=================== :math:`\Gamma_\varphi` is defined as the rate of pure dephasing or depolarization in the :math:`x - y` plane. We compute diff --git a/docs/index.rst b/docs/index.rst index 04307bd9e7..94cf2b3bcc 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -17,7 +17,7 @@ which can be run on quantum devices through Qiskit. We've divided up the documentation into four sections with different purposes: .. grid:: 2 - :gutter: 3 + :gutter: 5 .. grid-item-card:: :img-top: _static/tutorials.svg @@ -25,7 +25,7 @@ We've divided up the documentation into four sections with different purposes: Learning Tutorials ^^^^^^^^^^^^^^^^^^ - These step-by-step tutorials teach the fundamentals of the packaage and + These step-by-step tutorials teach the fundamentals of the package and are suitable for getting started. +++ @@ -93,6 +93,14 @@ We've divided up the documentation into four sections with different purposes: To the API reference +.. toctree:: + :hidden: + + tutorials/index + howtos/index + guides/index + apidocs/index + release_notes .. Hiding - Indices and tables :ref:`genindex` diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst new file mode 100644 index 0000000000..9c97129b9a --- /dev/null +++ b/docs/tutorials/curve_analysis.rst @@ -0,0 +1,442 @@ +========================================================= +Curve Analysis +========================================================= + +.. currentmodule:: qiskit_experiments.curve_analysis + +Curve analysis provides the analysis base class for a variety of experiments with +a single experimental parameter sweep. This analysis subclasses can override +several class attributes to customize the behavior from data processing to post-processing, +including providing systematic initial guess for parameters tailored to the experiment. +Here we describe how code developers can create new analysis inheriting from the base class. + + +.. _curve_analysis_overview: + +Curve Analysis Overview +======================= + +The base class :class:`CurveAnalysis` implements the multi-objective optimization on +different sets of experiment results. A single experiment can define sub-experiments +consisting of multiple circuits which are tagged with common metadata, +and curve analysis sorts the experiment results based on the circuit metadata. + +This is an example of showing the abstract data structure of typical curve analysis experiment: + +.. code-block:: none + :emphasize-lines: 1,10,19 + + "experiment" + - circuits[0] (x=x1_A, "series_A") + - circuits[1] (x=x1_B, "series_B") + - circuits[2] (x=x2_A, "series_A") + - circuits[3] (x=x2_B, "series_B") + - circuits[4] (x=x3_A, "series_A") + - circuits[5] (x=x3_B, "series_B") + - ... + + "experiment data" + - data[0] (y1_A, "series_A") + - data[1] (y1_B, "series_B") + - data[2] (y2_A, "series_A") + - data[3] (y2_B, "series_B") + - data[4] (y3_A, "series_A") + - data[5] (y3_B, "series_B") + - ... + + "analysis" + - "series_A": y_A = f_A(x_A; p0, p1, p2) + - "series_B": y_B = f_B(x_B; p0, p1, p2) + - fixed parameters {p1: v} + +Here the experiment runs two subset of experiments, namely, series A and series B. +The analysis defines corresponding fit models :math:`f_A(x_A)` and :math:`f_B(x_B)`. +Data extraction function in the analysis creates two datasets, :math:`(x_A, y_A)` +for the series A and :math:`(x_B, y_B)` for the series B, from the experiment data. +Optionally, the curve analysis can fix certain parameters during the fitting. +In this example, :math:`p_1 = v` remains unchanged during the fitting. + +The curve analysis aims at solving the following optimization problem: + +.. math:: + + \Theta_{\mbox{opt}} = \arg\min_{\Theta_{\rm fit}} \sigma^{-2} (F(X, \Theta)-Y)^2, + +where :math:`F` is the composite objective function defined on the full experiment data +:math:`(X, Y)`, where :math:`X = x_A \oplus x_B` and :math:`Y = y_A \oplus y_B`. +This objective function can be described by two fit functions as follows. + +.. math:: + + F(X, \Theta) = f_A(x_A, \theta_A) \oplus f_B(x_B, \theta_B). + +The solver conducts the least square curve fitting against this objective function +and returns the estimated parameters :math:`\Theta_{\mbox{opt}}` +that minimizes the reduced chi-squared value. +The parameters to be evaluated are :math:`\Theta = \Theta_{\rm fit} \cup \Theta_{\rm fix}`, +where :math:`\Theta_{\rm fit} = \theta_A \cup \theta_B`. +Since series A and B share the parameters in this example, :math:`\Theta_{\rm fit} = \{p_0, p_2\}`, +and the fixed parameters are :math:`\Theta_{\rm fix} = \{ p_1 \}` as mentioned. +Thus, :math:`\Theta = \{ p_0, p_1, p_2 \}`. + +Experiment for each series can perform individual parameter sweep for :math:`x_A` and :math:`x_B`, +and experiment data yield outcomes :math:`y_A` and :math:`y_B`, which might be different size. +Data processing function may also compute :math:`\sigma_A` and :math:`\sigma_B` which are +the uncertainty of outcomes arising from the sampling error or measurement error. + +More specifically, the curve analysis defines following data model. + +- Model: Definition of a single curve that is a function of reserved parameter "x". + +- Group: List of models. Fit functions defined under the same group must share the + fit parameters. Fit functions in the group are simultaneously fit to + generate a single fit result. + +Once the group is assigned, a curve analysis instance internally builds +a proper optimization routine. +Finally, the analysis outputs a set of :class:`AnalysisResultData` entries +for important fit outcomes along with a single Matplotlib figure of the fit curves +with the measured data points. + +With this baseclass a developer can avoid writing boilerplate code in +various curve analyses subclass and one can quickly write up +the analysis code for a particular experiment. + + +.. _curve_analysis_define_group: + +Defining New Group +================== + +The fit model is defined by the `LMFIT`_ ``Model``. +If you are familiar with this package, you can skip this section. +The LMFIT package manages complicated fit function and offers several algorithms +to solve non-linear least-square problems. +Basically the Qiskit curve analysis delegates the core fitting functionality to this package. + +You can intuitively write the definition of model, as shown below: + +.. code-block:: python3 + + import lmfit + + models = [ + lmfit.models.ExpressionModel( + expr="amp * exp(-alpha * x) + base", + name="exp_decay", + ) + ] + +Note that ``x`` is the reserved name to represent a parameter +that is scanned during the experiment. In above example, the fit function +consists of three parameters (``amp``, ``alpha``, ``base``), and ``exp`` indicates +a universal function in Python's math module. +Alternatively, you can take a callable to define the model object. + +.. code-block:: python3 + + import lmfit + import numpy as np + + def exp_decay(x, amp, alpha, base): + return amp * np.exp(-alpha * x) + base + + models = [lmfit.Model(func=exp_decay)] + +See `LMFIT`_ documentation for detailed user guide. They also provide preset models. + +If the :class:`.CurveAnalysis` is instantiated with multiple models, +it internally builds a cost function to simultaneously minimize the residuals of +all fit functions. +The names of the parameters in the fit function are important since they are used +in the analysis result, and potentially in your experiment database as a fit result. + +Here is another example how to implement multi-objective optimization task: + +.. code-block:: python3 + + import lmfit + + models = [ + lmfit.models.ExpressionModel( + expr="amp * exp(-alpha1 * x) + base", + name="my_experiment1", + data_sort_key={"tag": 1}, + ), + lmfit.models.ExpressionModel( + expr="amp * exp(-alpha2 * x) + base", + name="my_experiment2", + data_sort_key={"tag": 2}, + ), + ] + +Note that now you need to provide ``data_sort_key`` which is unique argument to +Qiskit curve analysis. This specifies the metadata of your experiment circuit +that is tied to the fit model. If multiple models are provided without this option, +the curve fitter cannot prepare data to fit. +In this model, you have four parameters (``amp``, ``alpha1``, ``alpha2``, ``base``) +and the two curves share ``amp`` (``base``) for the amplitude (baseline) in +the exponential decay function. +Here one should expect the experiment data will have two classes of data with metadata +``"tag": 1`` and ``"tag": 2`` for ``my_experiment1`` and ``my_experiment2``, respectively. + +By using this model, one can flexibly set up your fit model. Here is another example: + +.. code-block:: python3 + + import lmfit + + models = [ + lmfit.models.ExpressionModel( + expr="amp * cos(2 * pi * freq * x + phi) + base", + name="my_experiment1", + data_sort_key={"tag": 1}, + ), + lmfit.models.ExpressionModel( + expr="amp * sin(2 * pi * freq * x + phi) + base", + name="my_experiment2", + data_sort_key={"tag": 2}, + ), + ] + +You have the same set of fit parameters in two models, but now you fit two datasets +with different trigonometric functions. + +.. _LMFIT: https://lmfit.github.io/lmfit-py/intro.html + +.. _curve_analysis_fixed_param: + +Fitting with Fixed Parameters +============================= + +You can also remain certain parameters unchanged during the fitting by specifying +the parameter names in the analysis option ``fixed_parameters``. +This feature is useful especially when you want to define a subclass of +a particular analysis class. + +.. code-block:: python3 + + class AnalysisA(CurveAnalysis): + + def __init__(self): + super().__init__( + models=[ + lmfit.models.ExpressionModel( + expr="amp * exp(-alpha * x) + base", name="my_model" + ) + ] + ) + + class AnalysisB(AnalysisA): + + @classmethod + def _default_options(cls) -> Options: + options = super()._default_options() + options.fixed_parameters = {"amp": 3.0} + + return options + +The parameter specified in ``fixed_parameters`` is excluded from the fitting. +This code will give you identical fit model to the one defined in the following class: + +.. code-block:: python3 + + class AnalysisB(CurveAnalysis): + + super().__init__( + models=[ + lmfit.models.ExpressionModel( + expr="3.0 * exp(-alpha * x) + base", name="my_model" + ) + ] + ) + +However, note that you can also inherit other features, e.g. the algorithm to +generate initial guesses for parameters, from the :class:`AnalysisA` in the first example. +On the other hand, in the latter case, you need to manually copy and paste +every logic defined in the :class:`AnalysisA`. + +.. _curve_analysis_workflow: + +Cureve Analysis Workflow +======================== + +Typically curve analysis performs fitting as follows. +This workflow is defined in the method :meth:`CurveAnalysis._run_analysis`. + +1. Initialization + +Curve analysis calls :meth:`_initialization` method where it initializes +some internal states and optionally populate analysis options +with the input experiment data. +In some case it may train the data processor with fresh outcomes, +or dynamically generate the fit models (``self._models``) with fresh analysis options. +A developer can override this method to perform initialization of analysis-specific variables. + +2. Data processing + +Curve analysis calls :meth:`_run_data_processing` method where +the data processor in the analysis option is internally called. +This consumes input experiment results and creates :class:`CurveData` dataclass. +Then :meth:`_format_data` method is called with the processed dataset to format it. +By default, the formatter takes average of the outcomes in the processed dataset +over the same x values, followed by the sorting in the ascending order of x values. +This allows the analysis to easily estimate the slope of the curves to +create algorithmic initial guess of fit parameters. +A developer can inject extra data processing, for example, filtering, smoothing, +or elimination of outliers for better fitting. + +3. Fitting + +Curve analysis calls :meth:`_run_curve_fit` method which is the core functionality of the fitting. +The another method :meth:`_generate_fit_guesses` is internally called to +prepare the initial guess and parameter boundary with respect to the formatted data. +A developer usually override this method to provide better initial guess +tailored to the defined fit model or type of the associated experiment. +See :ref:`curve_analysis_init_guess` for more details. +A developer can also override the entire :meth:`_run_curve_fit` method to apply +custom fitting algorithms. This method must return :class:`.CurveFitResult` dataclass. + +4. Post processing + +Curve analysis runs several postprocessing against to the fit outcome. +It calls :meth:`_create_analysis_results` to create :class:`AnalysisResultData` class +for the fitting parameters of interest. A developer can inject a custom code to +compute custom quantities based on the raw fit parameters. +See :ref:`curve_analysis_results` for details. +Afterwards, the analysis draws several curves in the Matplotlib figure. +Users can set a custom plotter in :class:`CurveAnalysis` classes, to customize +figures, by setting the :attr:`~CurveAnalysis.plotter` attribute. +Finally, it returns the list of created analysis results and Matplotlib figure. + + +.. _curve_analysis_init_guess: + +Providing Initial Guesses +========================= + +When fit is performed without any prior information of parameters, it usually +falls into unsatisfactory result. +User can provide initial guesses and boundaries for the fit parameters +through analysis options ``p0`` and ``bounds``. +These values are the dictionary keyed on the parameter name, +and one can get the list of parameters with the :attr:`CurveAnalysis.parameters`. +Each boundary value can be a tuple of float representing min and max value. + +Apart from user provided guesses, the analysis can systematically generate +those values with the method :meth:`_generate_fit_guesses` which is called with +:class:`CurveData` dataclass. If the analysis contains multiple models definitions, +we can get the subset of curve data with :meth:`CurveData.get_subset_of` with +the name of the series. +A developer can implement the algorithm to generate initial guesses and boundaries +by using this curve data object, which will be provided to the fitter. +Note that there are several common initial guess estimators available in +:mod:`qiskit_experiments.curve_analysis.guess`. + +The :meth:`_generate_fit_guesses` also receives :class:`FitOptions` instance ``user_opt``, +which contains user provided guesses and boundaries. +This is dictionary-like object consisting of sub-dictionaries for +initial guess ``.p0``, boundary ``.bounds``, and extra options for the fitter. +Note that :class:`CurveAnalysis` uses SciPy `curve_fit`_ as the least square solver. +See the API documentation for available options. + +The :class:`FitOptions` class implements convenient method :meth:`set_if_empty` to manage +conflict with user provided values, i.e. user provided values have higher priority, +thus systematically generated values cannot override user values. + +.. code-block:: python3 + + def _generate_fit_guesses(self, user_opt, curve_data): + + opt1 = user_opt.copy() + opt1.p0.set_if_empty(p1=3) + opt1.bounds = set_if_empty(p1=(0, 10)) + opt1.add_extra_options(method="lm") + + opt2 = user_opt.copy() + opt2.p0.set_if_empty(p1=4) + + return [opt1, opt2] + +Here you created two options with different ``p1`` values. +If multiple options are returned like this, the :meth:`_run_curve_fit` method +attempts to fit with all provided options and finds the best outcome with +the minimum reduced chi-square value. +When the fit model contains some parameter that cannot be easily estimated from the +curve data, you can create multiple options with varying the initial guess to +let the fitter find the most reasonable parameters to explain the model. +This allows you to avoid analysis failure with the poor initial guesses. + +.. _curve_fit: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + + +.. _curve_analysis_quality: + +Evaluate Fit Quality +==================== + +A subclass can override :meth:`_evaluate_quality` method to +provide an algorithm to evaluate quality of the fitting. +This method is called with the :class:`.CurveFitResult` object which contains +fit parameters and the reduced chi-squared value, +in addition to the several statistics on the fitting. +Qiskit Experiments often uses the empirical criterion chi-squared < 3 as a good fitting. + + +.. _curve_analysis_results: + +Curve Analysis Results +====================== + +Once the best fit parameters are found, the :meth:`_create_analysis_results` method is +called with the same :class:`.CurveFitResult` object. + +If you want to create an analysis result entry for the particular parameter, +you can override the analysis options ``result_parameters``. +By using :class:`ParameterRepr` representation, you can rename the parameter in the entry. + +.. code-block:: python3 + + from qiskit_experiments.curve_analysis import ParameterRepr + + def _default_options(cls) -> Options: + options = super()._default_options() + options.result_parameters = [ParameterRepr("p0", "amp", "Hz")] + + return options + +Here the first argument ``p0`` is the target parameter defined in the series definition, +``amp`` is the representation of ``p0`` in the result entry, +and ``Hz`` is the optional string for the unit of the value if available. + +Not only returning the fit parameters, you can also compute new quantities +by combining multiple fit parameters. +This can be done by overriding the :meth:`_create_analysis_results` method. + +.. code-block:: python3 + + from qiskit_experiments.framework import AnalysisResultData + + def _create_analysis_results(self, fit_data, quality, **metadata): + + outcomes = super()._create_analysis_results(fit_data, **metadata) + + p0 = fit_data.ufloat_params["p0"] + p1 = fit_data.ufloat_params["p1"] + + extra_entry = AnalysisResultData( + name="p01", + value=p0 * p1, + quality=quality, + extra=metadata, + ) + outcomes.append(extra_entry) + + return outcomes + +Note that both ``p0`` and ``p1`` are `UFloat`_ object consisting of +a nominal value and an error value which assumes the standard deviation. +Since this object natively supports error propagation, +you don't need to manually recompute the error of new value. + +.. _ufloat: https://pythonhosted.org/uncertainties/user_guide.html diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index b77da55e38..6967199bcf 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -22,22 +22,33 @@ Learning from the Ground Up learning Modules -=============== +======= .. toctree:: :maxdepth: 1 single_qubit_cals data_processor + curve_analysis + visualization -Writing a Custom Experiment -=========================== +Customizing Experiments +======================= .. toctree:: :maxdepth: 1 custom_experiment +.. toctree:: + :hidden: + + tutorials/index + howtos/index + guides/index + apidocs/index + release_notes + .. Hiding - Indices and tables :ref:`genindex` :ref:`modindex` diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst index 4508a414f4..7c27562482 100644 --- a/docs/tutorials/intro.rst +++ b/docs/tutorials/intro.rst @@ -2,13 +2,12 @@ What is Qiskit Experiments? =========================== Qiskit Experiments is a package for running device characterization and calibration -experiments on top of Qiskit Terra. +experiments on top of the core functionality of Qiskit Terra. -An **experiment** -is a series of circuits, executed on a device, and analysis of -of the subsequent data that's all built on top of the core functionality -of Qiskit Terra. Once an experiment is executed on a quantum backend through a series -of jobs, analysis is run automatically and results in the form of data and figures are generated. +An **experiment** is a series of circuits, executed on a device, and analysis of +of the subsequent data. Once an experiment is executed on a quantum backend, either +real or simulated, through a series of jobs, analysis is run automatically and results +in the form of data and figures are generated. In addition to the experiment framework itself, Qiskit Experiments also has a rich library of experiments for calibrating and characterizing qubits. @@ -23,14 +22,13 @@ What Qiskit Experiments can do * Transform the data through the data processor * Flexible visualization, storage, and retrieval of data -Qiskit Experiments is for -* Experimentalists who want to characterize and calibrate devices -* - A quick primer ============== -The Qiskit Experiments package consists of the experimental framework, +The Qiskit Experiments package consists of the experimental framework and the experiment +library. The framework itself consists of ``Experiment`` and ``Analysis`` classes, the +latter of which uses the Data Processor, Curve Analysis, and Visualization modules +to process the data, fit it to specified models, and plot the results, respectively. .. figure:: ./images/experimentarch.png :width: 400 diff --git a/docs/tutorials/learning.rst b/docs/tutorials/learning.rst index 79dd39137d..59c45cf7dc 100644 --- a/docs/tutorials/learning.rst +++ b/docs/tutorials/learning.rst @@ -77,19 +77,47 @@ Setting experiment options Often it's insufficient to run an experiment with only the default options. There are four types of options one can set for an experiment: -* Run options, for passing to the experiment's ``run()`` method -* Transpile options, for passing to the transpiler -* Experiment options, for the experiment class -* Analysis options, for the analysis class +* **Run options**, for passing to the experiment's ``run()`` method. Any run option that + Terra supports can be set and will be passed to the jobs at run time: -Setting up a calibrations instance -================================== +.. code-block:: + + exp.set_run_options(meas_level=MeasLevel.CLASSIFIED) + +* **Transpile options**, for passing to the Terra transpiler: + +.. code-block:: + + exp.set_transpile_options(scheduling_method='asap') + +* **Experiment options**, unique to the experiment class. Consult the API references + for the options for each experiment. + +.. code-block:: + + exp.set_experiment_options(delays=fields["delays_t1"]) + +* **Analysis options**, unique to the analysis class. Consult the API references for the + options for each experiment analysis. Unlike the other options, this one is + not set via the experiment object but via analysis instead: + +.. code-block:: + + exp.analysis.set_options(gate_error_ratio=None) -Parallel :math:`T_1` experiments on multiple qubits -============================================ -To measure :math:`T_1` of multiple qubits in the same experiment, we -create a parallel experiment: +Running experiments on multiple qubits +====================================== + +To run experiments across many qubits of the same device, we use **composite experiments**. +There are two core types of composite experiments: + +* **Parallel experiments** run across qubits simultaneously. The circuits cannot overlap in + qubits used. +* **Batch experiments** run consecutively in time. These circuits can overlap in qubits. + +Here's an example of measuring :math:`T_1` of multiple qubits in the same experiment, by +creating a parallel experiment: .. jupyter-execute:: @@ -104,11 +132,11 @@ create a parallel experiment: Viewing sub experiment data -====================== +=========================== The experiment data returned from a batched experiment also contains individual experiment data for each sub experiment which can be accessed -using ``child_data`` +using ``child_data``. .. jupyter-execute:: @@ -119,130 +147,3 @@ using ``child_data`` for result in sub_data.analysis_results(): print(result) -Writing a custom experiment -=========================== - -Qiskit-Experiments is designed to be easily customizable. To create an experiment subclass -based on either the :class:`.BaseExperiment` class or an existing experiment, you should: - -- Implement the abstract :meth:`.BaseExperiment.circuits` method. - This should return a list of ``QuantumCircuit`` objects defining - the experiment payload. - -- Call the :meth:`BaseExperiment.__init__` method during the subclass - constructor with a list of physical qubits. The length of this list must - be equal to the number of qubits in each circuit and is used to map these - circuits to this layout during execution. - Arguments in the constructor can be overridden so that a subclass can - be initialized with some experiment configuration. - -Optionally the following methods can also be overridden in the subclass to -allow configuring various experiment and execution options - -- :meth:`.BaseExperiment._default_experiment_options` - to set default values for configurable option parameters for the experiment. - -- :meth:`.BaseExperiment._default_transpile_options` - to set custom default values for the ``qiskit.transpile`` used to - transpile the generated circuits before execution. - -- :meth:`.BaseExperiment._default_run_options` - to set default backend options for running the transpiled circuits on a backend. - -- :meth:`.BaseExperiment._default_analysis_options` - to set default values for configurable options for the experiments analysis class. - Note that these should generally be set by overriding the :class:`.BaseAnalysis` - method :meth:`.BaseAnalysis._default_options` instead of this method except in the - case where the experiment requires different defaults to the used analysis class. - -- :meth:`.BaseExperiment._transpiled_circuits` - to override the default transpilation of circuits before execution. - -- :meth:`.BaseExperiment._metadata` - to add any experiment metadata to the result data. - -Furthermore, some characterization and calibration experiments can be run with restless -measurements, i.e. measurements where the qubits are not reset and circuits are executed -immediately after the previous measurement. Here, the :class:`.RestlessMixin` can help -to set the appropriate run options and data processing chain. - -Analysis Subclasses -~~~~~~~~~~~~~~~~~~~ - -To create an analysis subclass, one only needs to implement the abstract -:meth:`.BaseAnalysis._run_analysis` method. This method takes a -:class:`.ExperimentData` container and kwarg analysis options. If any -kwargs are used the :meth:`.BaseAnalysis._default_options` method should be -overriden to define default values for these options. - -The :meth:`.BaseAnalysis._run_analysis` method should return a pair -``(results, figures)`` where ``results`` is a list of -:class:`.AnalysisResultData` and ``figures`` is a list of -:class:`matplotlib.figure.Figure`. - -The :mod:`qiskit_experiments.data_processing` module contains classes for -building data processor workflows to help with advanced analysis of -experiment data. - -================================== -Subclassing an Existing Experiment -================================== - -This document will take you step-by-step through the process of subclassing an existing experiment in the Qiskit Experiment module. -The example in this guide focuses on adjusting the FineAmplitude experiment to calibrate on higher order transitions. -However, a similar process can be followed for other experiments. - -The FineAmplitude Experiment -============================ - -The ``FineAmplitude`` calibration experiment repeats N times per gate with a pulse to amplify the under-/over-rotations in the gate to determine the optimal amplitude. -This experiment can be performed for a variety of rotations and subclasses are provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as ``FineXAmplitude`` and ``FineSXAmplitude`` respectively. -These provided subclasses focus on the 0 <-> 1 transition, however this experiment can also be performed for higher order transitions. - -Subclassing the Experiment -========================== - -Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. -In order to do this, we need to create a subclass, shown below. - -.. code-block:: - - class HigherOrderFineXAmplitude(FineXAmplitude): - def _pre_circuit(self) -> QuantumCircuit: - """Return a preparation circuit. - - This method can be overridden by subclasses e.g. to calibrate schedules on - transitions other than the 0 <-> 1 transition. - """ - circuit = QuantumCircuit(1) - - circuit.x(0) - - if self.experiment_options.add_sx: - circuit.sx(0) - - if self.experiment_options.sx_schedule is not None: - sx_schedule = self.experiment_options.sx_schedule - circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) - circuit.barrier() - - return circuit - -In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate on higher energy transitions by using an initial X gate to populate the first excited state. - -Using the Subclass -================== - -Now, we can use our new subclass as we would the original parent class. -Pictured below are the results from following the Fine amplitude calibration tutorial for detecting an over-rotated pulse using our new ``HigherOrderFineXAmplitude`` class in place of the original ``FineXAmplitude`` class. -You can try this for yourself and verify that your results are similar. - -.. code-block:: - - DbAnalysisResultV1 - - name: d_theta - - value: -0.020710672666906425 ± 0.0012903658449026907 - - χ²: 0.7819653845899581 - - quality: good - - device_components: ['Q0'] - - verified: False \ No newline at end of file diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst new file mode 100644 index 0000000000..5d3e45c0d3 --- /dev/null +++ b/docs/tutorials/visualization.rst @@ -0,0 +1,82 @@ +========================================================= +Visualization +========================================================= + +The Visualization module provides plotting functionality for creating figures from experiment and analysis results. +This includes plotter and drawer classes to plot data in :py:class:`CurveAnalysis` and its subclasses. +Plotters inherit from :class:`BasePlotter` and define a type of figure that may be generated from +experiment or analysis data. For example, the results from :class:`CurveAnalysis` --- or any other +experiment where results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted +using the :class:`CurvePlotter` class, which plots X-Y-like values. + +These plotter classes act as a bridge (from the common bridge pattern in software development) between +analysis classes (or even users) and plotting backends such as Matplotlib. Drawers are the backends, with +a common interface defined in :class:`BaseDrawer`. Though Matplotlib is the only officially supported +plotting backend in Qiskit Experiments (i.e., through :class:`MplDrawer`), custom drawers can be +implemented by users to use alternative backends. As long as the backend is a subclass of +:class:`BaseDrawer`, and implements all the necessary functionality, all plotters should be able to +generate figures with the alternative backend. + +Data to feed into the module is split into `series` and `supplementary`: + +- **Series Data**: Values from experiment data or analysis instances, or values to be plotted as points, + lines, etc. A good rule-of-thumb: if it could have a legend entry, it's series data. +- **Supplementary Data**: Values unrelated to a series or curve, only related to the figure. Examples + include fit-reports, figure-wide text, or metadata. + +You can think of the structure of series and supplementary data as dictionaries with the data-keys and +series-names. This is an example for CurvePlotter, representing the dummy data we generated. + +.. code-block:: python + + series_data = { + "A": { # Series-name 'A' + "x": ..., # Data for data-key 'x' and series 'A' + "y": ..., + "x_interp": ..., + "y_interp": ..., + "y_interp_err": ..., + }, + "B": { # Series-name 'B' + "x": ..., # Data for data-key 'x' and series 'B' + "y": ..., + "x_interp": ..., + "y_interp": ..., + } + } + + +.. code-block:: python + + supplementary_data = { + "primary_results": ..., # Supplementary data only has a data-key, no series-name. + "fit_red_chi": ..., + } + + +Data consists of the ``{'y_interp_err', 'x', 'x_interp', 'y_interp', 'y'}`` keys. + + +Generating a figure using a plotter +=================================== + +.. code-block:: python + + # Create plotter and set options and style. + plotter = CurvePlotter(MplDrawer()) + plotter.set_options( + plot_sigma=[ + (1.0, 0.5) + ], # Controls confidence-intervals for `y_interp_err` data-keys. + ) + plotter.set_figure_options( + series_params={ + "A": {"symbol": "o", "color": "C0", "label": "Qubit 0"}, + "B": {"symbol": "X", "color": "C1", "label": "Qubit 1"}, + "C": {"symbol": "v", "color": "k", "label": "Ideal 0"}, + "D": {"symbol": "^", "color": "k", "label": "Ideal 1"}, + }, + xlabel="Parameter", + ylabel="${\\langle{}Z\\rangle{}}$", + figure_title="Expectation Values", + ) diff --git a/requirements-dev.txt b/requirements-dev.txt index 5c0e83e4aa..c69da150b8 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,7 +3,8 @@ stestr astroid==2.5 pylint==2.7.1 jinja2==3.0.3 -Sphinx>=1.8.3 +#Sphinx>=1.8.3 +sphinx==5.2.3 qiskit-sphinx-theme>=1.10 sphinx-autodoc-typehints sphinx-design From 795192fba62c5c7044cc06782234cedf0efaf73c Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 29 Nov 2022 06:41:05 -0500 Subject: [PATCH 12/64] minor changes --- docs/howtos/index.rst | 10 ++ docs/tutorials/custom_experiment.rst | 156 ++++++++++++++++++++++++--- docs/tutorials/single_qubit_cals.rst | 41 ++++--- 3 files changed, 176 insertions(+), 31 deletions(-) diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index d27a1a70e1..d4f3fe3105 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -13,6 +13,16 @@ How to... * +.. toctree:: + :includehidden: + + tutorials/index + howtos/index + guides/index + apidocs/index + release_notes + + diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 5b4b231041..b78d1a7353 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -1,22 +1,150 @@ Writing a custom experiment =========================== -In this tutorial, we'll use what we've learned so far to make a full experiment from -the :class:`.BaseExperiment` template. +Qiskit-Experiments is designed to be easily customizable. To create an experiment subclass +based on either the :class:`.BaseExperiment` class or an existing experiment, you should: -A randomized measurement experiment -=================================== +- Implement the abstract :meth:`.BaseExperiment.circuits` method. + This should return a list of ``QuantumCircuit`` objects defining + the experiment payload. +- Call the :meth:`BaseExperiment.__init__` method during the subclass + constructor with a list of physical qubits. The length of this list must + be equal to the number of qubits in each circuit and is used to map these + circuits to this layout during execution. + Arguments in the constructor can be overridden so that a subclass can + be initialized with some experiment configuration. -This experiment creates a list of copies of an input circuit -and randomly samples an N-qubit Paulis to apply to each one before -a final N-qubit Z-basis measurement to randomized the expected -ideal output bitstring in the measured. +Optionally the following methods can also be overridden in the subclass to +allow configuring various experiment and execution options -The analysis uses the applied Pauli frame of a randomized -measurement experiment to de-randomize the measured counts -and combine across samples to return a single counts dictionary -the original circuit. +- :meth:`.BaseExperiment._default_experiment_options` + to set default values for configurable option parameters for the experiment. -This has the effect of Pauli-twirling and symmetrizing the -measurement readout error. \ No newline at end of file +- :meth:`.BaseExperiment._default_transpile_options` + to set custom default values for the ``qiskit.transpile`` used to + transpile the generated circuits before execution. + +- :meth:`.BaseExperiment._default_run_options` + to set default backend options for running the transpiled circuits on a backend. + +- :meth:`.BaseExperiment._default_analysis_options` + to set default values for configurable options for the experiments analysis class. + Note that these should generally be set by overriding the :class:`.BaseAnalysis` + method :meth:`.BaseAnalysis._default_options` instead of this method except in the + case where the experiment requires different defaults to the used analysis class. + +- :meth:`.BaseExperiment._transpiled_circuits` + to override the default transpilation of circuits before execution. + +- :meth:`.BaseExperiment._metadata` + to add any experiment metadata to the result data. + +Furthermore, some characterization and calibration experiments can be run with restless +measurements, i.e. measurements where the qubits are not reset and circuits are executed +immediately after the previous measurement. Here, the :class:`.RestlessMixin` can help +to set the appropriate run options and data processing chain. + +Analysis Subclasses +~~~~~~~~~~~~~~~~~~~ + +To create an analysis subclass, one only needs to implement the abstract +:meth:`.BaseAnalysis._run_analysis` method. This method takes a +:class:`.ExperimentData` container and kwarg analysis options. If any +kwargs are used the :meth:`.BaseAnalysis._default_options` method should be +overriden to define default values for these options. + +The :meth:`.BaseAnalysis._run_analysis` method should return a pair +``(results, figures)`` where ``results`` is a list of +:class:`.AnalysisResultData` and ``figures`` is a list of +:class:`matplotlib.figure.Figure`. + +The :mod:`qiskit_experiments.data_processing` module contains classes for +building data processor workflows to help with advanced analysis of +experiment data. + +================================== +Subclassing an Existing Experiment +================================== + +This document will take you step-by-step through the process of subclassing an existing experiment in the Qiskit Experiment module. +The example in this guide focuses on adjusting the FineAmplitude experiment to calibrate on higher order transitions. +However, a similar process can be followed for other experiments. + +The FineAmplitude Experiment +============================ + +The ``FineAmplitude`` calibration experiment repeats N times per gate with a pulse to amplify the under-/over-rotations in the gate to determine the optimal amplitude. +This experiment can be performed for a variety of rotations and subclasses are provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as ``FineXAmplitude`` and ``FineSXAmplitude`` respectively. +These provided subclasses focus on the 0 <-> 1 transition, however this experiment can also be performed for higher order transitions. + +Subclassing the Experiment +========================== + +Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. +In order to do this, we need to create a subclass, shown below. + +.. code-block:: + + class HigherOrderFineXAmplitude(FineXAmplitude): + def _pre_circuit(self) -> QuantumCircuit: + """Return a preparation circuit. + + This method can be overridden by subclasses e.g. to calibrate schedules on + transitions other than the 0 <-> 1 transition. + """ + circuit = QuantumCircuit(1) + + circuit.x(0) + + if self.experiment_options.add_sx: + circuit.sx(0) + + if self.experiment_options.sx_schedule is not None: + sx_schedule = self.experiment_options.sx_schedule + circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) + circuit.barrier() + + return circuit + +In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate on higher energy transitions by using an initial X gate to populate the first excited state. + +Using the Subclass +================== + +Now, we can use our new subclass as we would the original parent class. +Pictured below are the results from following the Fine amplitude calibration tutorial for detecting an over-rotated pulse using our new ``HigherOrderFineXAmplitude`` class in place of the original ``FineXAmplitude`` class. +You can try this for yourself and verify that your results are similar. + +.. code-block:: + + DbAnalysisResultV1 + - name: d_theta + - value: -0.020710672666906425 ± 0.0012903658449026907 + - χ²: 0.7819653845899581 + - quality: good + - device_components: ['Q0'] + - verified: False + +.. Writing a custom experiment +.. =========================== + +.. In this tutorial, we'll use what we've learned so far to make a full experiment from +.. the :class:`.BaseExperiment` template. + +.. A randomized measurement experiment +.. =================================== + + +.. This experiment creates a list of copies of an input circuit +.. and randomly samples an N-qubit Paulis to apply to each one before +.. a final N-qubit Z-basis measurement to randomized the expected +.. ideal output bitstring in the measured. + +.. The analysis uses the applied Pauli frame of a randomized +.. measurement experiment to de-randomize the measured counts +.. and combine across samples to return a single counts dictionary +.. the original circuit. + +.. This has the effect of Pauli-twirling and symmetrizing the +.. measurement readout error. \ No newline at end of file diff --git a/docs/tutorials/single_qubit_cals.rst b/docs/tutorials/single_qubit_cals.rst index 9ba6aaf600..08d5330c90 100644 --- a/docs/tutorials/single_qubit_cals.rst +++ b/docs/tutorials/single_qubit_cals.rst @@ -1,6 +1,6 @@ -######################################### -Run a Single-Qubit Calibration Experiment -######################################### +########################## +The Calibrations Module +########################## To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). @@ -18,18 +18,25 @@ Each experiment usually provides additional information about the system used in On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. .. jupyter-execute:: + :hide-code: + :hide-output: - # from qiskit import IBMQ - # IBMQ.load_account() - # provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') - # backend = provider.get_backend('ibmq_armonk') + from qiskit.test.ibmq_mock import mock_get_backend + backend = mock_get_backend('FakeLima') + +.. jupyter-execute:: + + from qiskit import IBMQ + IBMQ.load_account() + provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') + backend = provider.get_backend('ibmq_lima') We can verify whether the backend supports Pulse features by checking the backend configuration. .. jupyter-execute:: - # backend_config = backend.configuration() - # assert backend_config.open_pulse, "Backend doesn't support Pulse" + backend_config = backend.configuration() + assert backend_config.open_pulse, "Backend doesn't support Pulse" On the other hand we can also use a mock backend in case no IBM Quantum Experience credentials found. For this tutorial, we will use mock backends prepared for each experiment. @@ -200,16 +207,16 @@ After saving the values of the parameters we may restart our kernel. If we do so A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage to a neighbouring transition. It is a standard pulse with an additional derivative component. It is designed to reduce the frequency spectrum of a normal pulse near -the $|1\rangle$ - $|2\rangle$ transition, reducing the chance of leakage -to the $|2\rangle$ state. The optimal value of the DRAG parameter is chosen to +the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage +to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to minimize both leakage and phase errors resulting from the AC Stark shift. -The pulse envelope is $f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)$. -Here, $\Omega_x$ is the envelop of the in-phase component of the pulse and +The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. +Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and $\beta$ is the strength of the quadrature which we refer to as the DRAG parameter and seek to calibrate in this experiment. The DRAG calibration will run -several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated -$N$ times. Here, Rp is a rotation with a positive angle and Rm is the same rotation +several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated +:math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation with a negative amplitude. We use a mock backend in case no IBM credentials found. @@ -247,7 +254,7 @@ If we do not set any experiment options using `set_experiment_options()` method, drag_data.figure(0) ================== -5. Miscalibrations +1. Miscalibrations ================== In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. @@ -285,7 +292,7 @@ Then we define a calibration for the `X` gate on qubit 0. For the `amp` paramete circ = transpile(circ, backend) circ.draw(idle_wires=False) -Execute our circuit +Execute our circuit: .. jupyter-execute:: From cb903c6e63ab7828680dcf28ef51281cf6797ee6 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 30 Nov 2022 09:51:41 -0500 Subject: [PATCH 13/64] added subtitles --- docs/index.rst | 19 +++++++++++++++++++ docs/tutorials/curve_analysis.rst | 2 +- docs/tutorials/data_processor.rst | 4 ++-- docs/tutorials/index.rst | 17 +++++++++-------- docs/tutorials/single_qubit_cals.rst | 6 +++--- docs/tutorials/visualization.rst | 2 +- 6 files changed, 35 insertions(+), 15 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 94cf2b3bcc..1504787566 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -28,6 +28,12 @@ We've divided up the documentation into four sections with different purposes: These step-by-step tutorials teach the fundamentals of the package and are suitable for getting started. + You'll find in these tutorials: + + * An overview of the package structure + * What constitutes an experiment + * How to write your own experiment + +++ .. button-ref:: tutorials/index @@ -46,6 +52,11 @@ We've divided up the documentation into four sections with different purposes: These individual recipes provide short and direct solutions to some commonly asked questions for Qiskit Experiments users. + You'll find in these recipes: + + * How to save and retrieve experiment data + * How to customize the appearance of your figures + +++ .. button-ref:: howtos/index @@ -64,6 +75,10 @@ We've divided up the documentation into four sections with different purposes: These are in-depth guides to key experiments in the package, describing their background, principle, and how to run them in Qiskit Experiments. + You'll find in these guides: + + * What is randomized benchmarking and when is it useful + +++ .. button-ref:: guides/index @@ -84,6 +99,10 @@ We've divided up the documentation into four sections with different purposes: Qiskit Experiments and how to use them, suitable for those working closely with specific parts of the package or writing your custom code. + You'll find in these references: + + * What are all possible input parameters to CurveAnalysis + +++ .. button-ref:: apidocs/index diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 9c97129b9a..94f877df6b 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -1,5 +1,5 @@ ========================================================= -Curve Analysis +Curve Analysis: Fitting your data ========================================================= .. currentmodule:: qiskit_experiments.curve_analysis diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 0941b9b2d2..4f430de95f 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -1,5 +1,5 @@ -The Data Processor -================== +Data Processor: Wrangling data +============================== Data processing is the act of taking the data returned by the backend and converting it into a format that can be analyzed. diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 6967199bcf..9a312922f8 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -2,10 +2,11 @@ Learning Tutorials ################## -These tutorials are learning-based documentation suitable for getting started. +These tutorials assume some familiarity with Qiskit, but no knowledge of Qiskit Experiments. +They're suitable for beginners who want to get started with the package. -Introduction -============ +Introduction and overview +========================= .. toctree:: :maxdepth: 2 @@ -13,15 +14,15 @@ Introduction intro -Learning from the Ground Up -=========================== +Getting started +=============== .. toctree:: :maxdepth: 1 learning -Modules +Exploring modules ======= .. toctree:: @@ -32,8 +33,8 @@ Modules curve_analysis visualization -Customizing Experiments -======================= +Write your own experiment +========================= .. toctree:: :maxdepth: 1 diff --git a/docs/tutorials/single_qubit_cals.rst b/docs/tutorials/single_qubit_cals.rst index 08d5330c90..075e892d2f 100644 --- a/docs/tutorials/single_qubit_cals.rst +++ b/docs/tutorials/single_qubit_cals.rst @@ -1,6 +1,6 @@ -########################## -The Calibrations Module -########################## +####################################################### +Calibrations: Schedules and gate parameters from experiments +####################################################### To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index 5d3e45c0d3..0fb8bc536e 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -1,5 +1,5 @@ ========================================================= -Visualization +Visualization: Making figures ========================================================= The Visualization module provides plotting functionality for creating figures from experiment and analysis results. From 15fb6cf7a278ab192da7a26260af58441a8a363d Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 30 Nov 2022 10:00:03 -0500 Subject: [PATCH 14/64] renamed file --- .github/workflows/neko.yml | 16 ++ docs/tutorials/calibrations.rst | 301 ++++++++++++++++++++++++++++++++ 2 files changed, 317 insertions(+) create mode 100644 .github/workflows/neko.yml create mode 100644 docs/tutorials/calibrations.rst diff --git a/.github/workflows/neko.yml b/.github/workflows/neko.yml new file mode 100644 index 0000000000..f8cfdca16e --- /dev/null +++ b/.github/workflows/neko.yml @@ -0,0 +1,16 @@ +name: Qiskit Neko Integration Tests +on: + push: + pull_request: + branches: ['main'] +concurrency: + group: ${{ github.repository }}-${{ github.ref }}-${{ github.head_ref }}-${{ github.workflow }} + cancel-in-progress: true +jobs: + neko: + name: Qiskit Neko Integration Tests + runs-on: ubuntu-latest + steps: + - uses: Qiskit/qiskit-neko@main + with: + test_selection: experiment diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst new file mode 100644 index 0000000000..075e892d2f --- /dev/null +++ b/docs/tutorials/calibrations.rst @@ -0,0 +1,301 @@ +####################################################### +Calibrations: Schedules and gate parameters from experiments +####################################################### + +To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). + +Each experiment usually provides additional information about the system used in subsequent experiments. + +.. jupyter-execute:: + + import numpy as np + + import qiskit.pulse as pulse + from qiskit.circuit import Parameter + + from qiskit_experiments.calibration_management import BackendCalibrations + +On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. + +.. jupyter-execute:: + :hide-code: + :hide-output: + + from qiskit.test.ibmq_mock import mock_get_backend + backend = mock_get_backend('FakeLima') + +.. jupyter-execute:: + + from qiskit import IBMQ + IBMQ.load_account() + provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') + backend = provider.get_backend('ibmq_lima') + +We can verify whether the backend supports Pulse features by checking the backend configuration. + +.. jupyter-execute:: + + backend_config = backend.configuration() + assert backend_config.open_pulse, "Backend doesn't support Pulse" + +On the other hand we can also use a mock backend in case no IBM Quantum Experience credentials found. For this tutorial, we will use mock backends prepared for each experiment. + +To use in the experiments we first need to define template schedule to calibrate for `x` pulse. + +.. jupyter-execute:: + + def setup_cals(backend) -> BackendCalibrations: + """A function to instantiate calibrations and add a couple of template schedules.""" + cals = BackendCalibrations(backend) + + dur = Parameter("dur") + amp = Parameter("amp") + sigma = Parameter("σ") + beta = Parameter("β") + drive = pulse.DriveChannel(Parameter("ch0")) + + # Define and add template schedules. + with pulse.build(name="x") as x: + pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) + + cals.add_schedule(x, num_qubits=1) + + return cals + + def add_parameter_guesses(cals: BackendCalibrations): + + """Add guesses for the parameter values to the calibrations.""" + for sched in ["x"]: + print(sched) + cals.add_parameter_value(80, "σ", schedule=sched) + cals.add_parameter_value(0.5, "β", schedule=sched) + cals.add_parameter_value(320, "dur", schedule=sched) + cals.add_parameter_value(0.5, "amp", schedule=sched) + +=================================== +1. Finding qubits with spectroscopy +=================================== +Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. + +We start with a mock backend. + +.. jupyter-execute:: + + from qiskit_experiments.test.test_qubit_spectroscopy import SpectroscopyBackend + spec_backend = SpectroscopyBackend() + +We then setup calibrations for the backend. + +.. jupyter-execute:: + + cals = setup_cals(spec_backend) # Block until our job and its post processing finish. + add_parameter_guesses(cals) + +We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal + + qubit = 0 + freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] + frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) + spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) + +.. jupyter-execute:: + + circuit = spec.circuits()[0] + circuit.draw() + +We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. + +.. jupyter-execute:: + + spec_data = spec.run().block_for_results() + spec_data.figure(0) + +We can see the analysis results + +.. jupyter-execute:: + + print(spec_data.analysis_results("f01")) + +======================================================== +2. Calibrating the pulse amplitudes with Rabi experiment +======================================================== +We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. + +First we define the mock backend. + +.. jupyter-execute:: + + from qiskit_experiments.test.mock_iq_backend import RabiBackend + rabi_backend = RabiBackend() + +We then setup calibrations for the backend. + +.. jupyter-execute:: + + cals = setup_cals(rabi_backend) + add_parameter_guesses(cals) + +We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. + +We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + +If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration import RoughAmplitudeCal + + qubit = 0 + + rabi = RoughAmplitudeCal(qubit, cals) + +The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. + +.. jupyter-execute:: + + rabi.circuits()[0].draw() + +After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. + +.. jupyter-execute:: + + rabi_data = rabi.run(rabi_backend) + rabi_data.block_for_results() # Block until our job and its post processing finish. + print(rabi_data) + +.. jupyter-execute:: + + rabi_data.figure(0) + +In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. + +.. jupyter-execute:: + + pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 + print(pi_pulse_amplitude) + +================================== +3. Saving and loading calibrations +================================== + +The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. + +.. code-block:: python + + cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") + +After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. + +.. code-block:: python + + from qiskit_experiments.test.mock_iq_backend import RabiBackend + rabi_backend = RabiBackend() + cals = BackendCalibrations(rabi_backend) + cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") + +======================================================= +4. Using the Calibrated Amplitude in Another Experiment +======================================================= +------------------------------------------------------ +4.1. Calibrating the value of the DRAG coefficient +------------------------------------------------------ + +A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage +to a neighbouring transition. It is a standard pulse with an additional derivative +component. It is designed to reduce the frequency spectrum of a normal pulse near +the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage +to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to +minimize both leakage and phase errors resulting from the AC Stark shift. +The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. +Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and +$\beta$ is the strength of the quadrature which we refer to as the DRAG +parameter and seek to calibrate in this experiment. +The DRAG calibration will run +several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated +:math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation +with a negative amplitude. + +We use a mock backend in case no IBM credentials found. + +.. jupyter-execute:: + + from qiskit_experiments.test.mock_iq_backend import DragBackend + drag_backend = DragBackend(gate_name="Drag(x)") + +We define the template schedule for `x` pulse using previous methods. + +Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. + +.. jupyter-execute:: + + cals = setup_cals(drag_backend) + add_parameter_guesses(cals) + +We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + +If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. + +.. jupyter-execute:: + + from qiskit_experiments.library import RoughDragCal + drag = RoughDragCal(qubit, cals) + +.. jupyter-execute:: + + drag_data = drag.run(drag_backend) + drag_data.block_for_results() + +.. jupyter-execute:: + + drag_data.figure(0) + +================== +1. Miscalibrations +================== + +In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. + +Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. + +We first define a simple circuit that contains an X gate and measurement. + +.. jupyter-execute:: + + from qiskit import QuantumCircuit + + circ = QuantumCircuit(1, 1) + circ.x(0) + circ.measure(0, 0) + circ.draw() + +Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. + +.. jupyter-execute:: + + from qiskit import pulse, transpile + from qiskit.test.mock import FakeArmonk + from qiskit.pulse.library import Constant + backend = FakeArmonk() + + # build a simple circuit that only contain one x gate and measurement + circ = QuantumCircuit(1, 1) + circ.x(0) + circ.measure(0, 0) + with pulse.build(backend) as my_schedule: + pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse + + circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule + circ = transpile(circ, backend) + circ.draw(idle_wires=False) + +Execute our circuit: + +.. jupyter-execute:: + + result = backend.run(transpile(circ, backend), shots=1000).result() + counts = result.get_counts(circ) + print(counts) \ No newline at end of file From 82a366bdfab5e51b31473ff4a86f19d561a6e3eb Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Fri, 2 Dec 2022 14:35:22 -0500 Subject: [PATCH 15/64] updated calibrations --- docs/tutorials/calibrations.rst | 8 +- docs/tutorials/index.rst | 2 +- docs/tutorials/single_qubit_cals.rst | 301 --------------------------- docs/tutorials/visualization.rst | 6 +- requirements-dev.txt | 1 - 5 files changed, 8 insertions(+), 310 deletions(-) delete mode 100644 docs/tutorials/single_qubit_cals.rst diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index 075e892d2f..ce51193b8d 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -13,7 +13,7 @@ Each experiment usually provides additional information about the system used in import qiskit.pulse as pulse from qiskit.circuit import Parameter - from qiskit_experiments.calibration_management import BackendCalibrations + from qiskit_experiments.calibration_management import Calibrations On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. @@ -44,9 +44,9 @@ To use in the experiments we first need to define template schedule to calibrate .. jupyter-execute:: - def setup_cals(backend) -> BackendCalibrations: + def setup_cals(backend) -> Calibrations: """A function to instantiate calibrations and add a couple of template schedules.""" - cals = BackendCalibrations(backend) + cals = Calibrations(backend) dur = Parameter("dur") amp = Parameter("amp") @@ -62,7 +62,7 @@ To use in the experiments we first need to define template schedule to calibrate return cals - def add_parameter_guesses(cals: BackendCalibrations): + def add_parameter_guesses(cals: Calibrations): """Add guesses for the parameter values to the calibrations.""" for sched in ["x"]: diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 9a312922f8..5377c70267 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -28,7 +28,7 @@ Exploring modules .. toctree:: :maxdepth: 1 - single_qubit_cals + calibrations data_processor curve_analysis visualization diff --git a/docs/tutorials/single_qubit_cals.rst b/docs/tutorials/single_qubit_cals.rst deleted file mode 100644 index 075e892d2f..0000000000 --- a/docs/tutorials/single_qubit_cals.rst +++ /dev/null @@ -1,301 +0,0 @@ -####################################################### -Calibrations: Schedules and gate parameters from experiments -####################################################### - -To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). - -Each experiment usually provides additional information about the system used in subsequent experiments. - -.. jupyter-execute:: - - import numpy as np - - import qiskit.pulse as pulse - from qiskit.circuit import Parameter - - from qiskit_experiments.calibration_management import BackendCalibrations - -On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. - -.. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - -.. jupyter-execute:: - - from qiskit import IBMQ - IBMQ.load_account() - provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') - backend = provider.get_backend('ibmq_lima') - -We can verify whether the backend supports Pulse features by checking the backend configuration. - -.. jupyter-execute:: - - backend_config = backend.configuration() - assert backend_config.open_pulse, "Backend doesn't support Pulse" - -On the other hand we can also use a mock backend in case no IBM Quantum Experience credentials found. For this tutorial, we will use mock backends prepared for each experiment. - -To use in the experiments we first need to define template schedule to calibrate for `x` pulse. - -.. jupyter-execute:: - - def setup_cals(backend) -> BackendCalibrations: - """A function to instantiate calibrations and add a couple of template schedules.""" - cals = BackendCalibrations(backend) - - dur = Parameter("dur") - amp = Parameter("amp") - sigma = Parameter("σ") - beta = Parameter("β") - drive = pulse.DriveChannel(Parameter("ch0")) - - # Define and add template schedules. - with pulse.build(name="x") as x: - pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) - - cals.add_schedule(x, num_qubits=1) - - return cals - - def add_parameter_guesses(cals: BackendCalibrations): - - """Add guesses for the parameter values to the calibrations.""" - for sched in ["x"]: - print(sched) - cals.add_parameter_value(80, "σ", schedule=sched) - cals.add_parameter_value(0.5, "β", schedule=sched) - cals.add_parameter_value(320, "dur", schedule=sched) - cals.add_parameter_value(0.5, "amp", schedule=sched) - -=================================== -1. Finding qubits with spectroscopy -=================================== -Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. - -We start with a mock backend. - -.. jupyter-execute:: - - from qiskit_experiments.test.test_qubit_spectroscopy import SpectroscopyBackend - spec_backend = SpectroscopyBackend() - -We then setup calibrations for the backend. - -.. jupyter-execute:: - - cals = setup_cals(spec_backend) # Block until our job and its post processing finish. - add_parameter_guesses(cals) - -We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal - - qubit = 0 - freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] - frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) - spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) - -.. jupyter-execute:: - - circuit = spec.circuits()[0] - circuit.draw() - -We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. - -.. jupyter-execute:: - - spec_data = spec.run().block_for_results() - spec_data.figure(0) - -We can see the analysis results - -.. jupyter-execute:: - - print(spec_data.analysis_results("f01")) - -======================================================== -2. Calibrating the pulse amplitudes with Rabi experiment -======================================================== -We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. - -First we define the mock backend. - -.. jupyter-execute:: - - from qiskit_experiments.test.mock_iq_backend import RabiBackend - rabi_backend = RabiBackend() - -We then setup calibrations for the backend. - -.. jupyter-execute:: - - cals = setup_cals(rabi_backend) - add_parameter_guesses(cals) - -We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. - -We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. - -If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration import RoughAmplitudeCal - - qubit = 0 - - rabi = RoughAmplitudeCal(qubit, cals) - -The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. - -.. jupyter-execute:: - - rabi.circuits()[0].draw() - -After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. - -.. jupyter-execute:: - - rabi_data = rabi.run(rabi_backend) - rabi_data.block_for_results() # Block until our job and its post processing finish. - print(rabi_data) - -.. jupyter-execute:: - - rabi_data.figure(0) - -In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. - -.. jupyter-execute:: - - pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 - print(pi_pulse_amplitude) - -================================== -3. Saving and loading calibrations -================================== - -The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. - -.. code-block:: python - - cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") - -After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. - -.. code-block:: python - - from qiskit_experiments.test.mock_iq_backend import RabiBackend - rabi_backend = RabiBackend() - cals = BackendCalibrations(rabi_backend) - cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") - -======================================================= -4. Using the Calibrated Amplitude in Another Experiment -======================================================= ------------------------------------------------------- -4.1. Calibrating the value of the DRAG coefficient ------------------------------------------------------- - -A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage -to a neighbouring transition. It is a standard pulse with an additional derivative -component. It is designed to reduce the frequency spectrum of a normal pulse near -the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage -to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to -minimize both leakage and phase errors resulting from the AC Stark shift. -The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. -Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and -$\beta$ is the strength of the quadrature which we refer to as the DRAG -parameter and seek to calibrate in this experiment. -The DRAG calibration will run -several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated -:math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation -with a negative amplitude. - -We use a mock backend in case no IBM credentials found. - -.. jupyter-execute:: - - from qiskit_experiments.test.mock_iq_backend import DragBackend - drag_backend = DragBackend(gate_name="Drag(x)") - -We define the template schedule for `x` pulse using previous methods. - -Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. - -.. jupyter-execute:: - - cals = setup_cals(drag_backend) - add_parameter_guesses(cals) - -We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. - -If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. - -.. jupyter-execute:: - - from qiskit_experiments.library import RoughDragCal - drag = RoughDragCal(qubit, cals) - -.. jupyter-execute:: - - drag_data = drag.run(drag_backend) - drag_data.block_for_results() - -.. jupyter-execute:: - - drag_data.figure(0) - -================== -1. Miscalibrations -================== - -In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. - -Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. - -We first define a simple circuit that contains an X gate and measurement. - -.. jupyter-execute:: - - from qiskit import QuantumCircuit - - circ = QuantumCircuit(1, 1) - circ.x(0) - circ.measure(0, 0) - circ.draw() - -Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. - -.. jupyter-execute:: - - from qiskit import pulse, transpile - from qiskit.test.mock import FakeArmonk - from qiskit.pulse.library import Constant - backend = FakeArmonk() - - # build a simple circuit that only contain one x gate and measurement - circ = QuantumCircuit(1, 1) - circ.x(0) - circ.measure(0, 0) - with pulse.build(backend) as my_schedule: - pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse - - circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule - circ = transpile(circ, backend) - circ.draw(idle_wires=False) - -Execute our circuit: - -.. jupyter-execute:: - - result = backend.run(transpile(circ, backend), shots=1000).result() - counts = result.get_counts(circ) - print(counts) \ No newline at end of file diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index 0fb8bc536e..1468280f2d 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -1,6 +1,6 @@ -========================================================= -Visualization: Making figures -========================================================= +=============================== +Visualization: Creating figures +=============================== The Visualization module provides plotting functionality for creating figures from experiment and analysis results. This includes plotter and drawer classes to plot data in :py:class:`CurveAnalysis` and its subclasses. diff --git a/requirements-dev.txt b/requirements-dev.txt index c69da150b8..d118184ff6 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,7 +3,6 @@ stestr astroid==2.5 pylint==2.7.1 jinja2==3.0.3 -#Sphinx>=1.8.3 sphinx==5.2.3 qiskit-sphinx-theme>=1.10 sphinx-autodoc-typehints From e36ed2e74f82e244c234af1e6bf39f5bdde27f0a Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 6 Dec 2022 00:27:29 -0500 Subject: [PATCH 16/64] upgraded qiskit-sphinx and updated landing page --- docs/_static/api.png | Bin 0 -> 131670 bytes docs/_static/api.svg | 1 - docs/_static/guides.png | Bin 0 -> 54681 bytes docs/_static/guides.svg | 1 - docs/_static/howtos.png | Bin 0 -> 137039 bytes docs/_static/howtos.svg | 1 - docs/_static/style.css | 4 +- docs/_static/tutorials.png | Bin 0 -> 89942 bytes docs/_static/tutorials.svg | 17 -- docs/apidocs/index.rst | 6 +- docs/howtos/index.rst | 6 +- docs/howtos/local_service.rst | 14 ++ docs/index.rst | 18 +- docs/tutorials/calibrations.rst | 289 ++++++++++++++++---------------- docs/tutorials/index.rst | 6 +- requirements-dev.txt | 2 +- 16 files changed, 177 insertions(+), 188 deletions(-) create mode 100644 docs/_static/api.png delete mode 100644 docs/_static/api.svg create mode 100644 docs/_static/guides.png delete mode 100644 docs/_static/guides.svg create mode 100644 docs/_static/howtos.png delete mode 100644 docs/_static/howtos.svg create mode 100644 docs/_static/tutorials.png delete mode 100644 docs/_static/tutorials.svg create mode 100644 docs/howtos/local_service.rst diff --git a/docs/_static/api.png b/docs/_static/api.png new file mode 100644 index 0000000000000000000000000000000000000000..cf18a0d8716d75f746acfecca36182c8d58216f7 GIT binary patch literal 131670 zcmeFY1yq$?w=hZx8&uec2uinru<34;?pB&jH*C5~P*Pf2x}>B#lt#L{ySup$zInd$ ze)pVv{_&4{|1qwP&3;(Vnsd!Hd(O4^C?_NO80`fb930$Zak1A5aB%R)_a6{4Fw#9b 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-================================ -Qiskit Experiments API Reference -================================ +============= +API Reference +============= Package Modules =============== diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index d4f3fe3105..df34856315 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -1,6 +1,6 @@ -############## -How-To Recipes -############## +####### +How-Tos +####### This section of the documentation provides concrete recipes for how to do specific useful actions in Qiskit Experiments. diff --git a/docs/howtos/local_service.rst b/docs/howtos/local_service.rst new file mode 100644 index 0000000000..2f71b7e2e4 --- /dev/null +++ b/docs/howtos/local_service.rst @@ -0,0 +1,14 @@ +How to save and load experiment data locally +============================================ + +Problem +------- + +You want to save and load experiment data to and from your computer. + +Solution +-------- + +Experiments can be saved locally as long as you have the ``qiskit-ibm-experiment`` +provider installed. + diff --git a/docs/index.rst b/docs/index.rst index 1504787566..bb72684978 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -20,10 +20,10 @@ We've divided up the documentation into four sections with different purposes: :gutter: 5 .. grid-item-card:: - :img-top: _static/tutorials.svg + :img-top: _static/tutorials.png - Learning Tutorials - ^^^^^^^^^^^^^^^^^^ + Tutorials + ^^^^^^^^^ These step-by-step tutorials teach the fundamentals of the package and are suitable for getting started. @@ -44,12 +44,12 @@ We've divided up the documentation into four sections with different purposes: To the learning tutorials .. grid-item-card:: - :img-top: _static/howtos.svg + :img-top: _static/howtos.png - How-To Recipes - ^^^^^^^^^^^^^^ + How-Tos + ^^^^^^^ - These individual recipes provide short and direct solutions to some commonly + These individual how-to recipes provide short and direct solutions to some commonly asked questions for Qiskit Experiments users. You'll find in these recipes: @@ -67,7 +67,7 @@ We've divided up the documentation into four sections with different purposes: To the how-to recipes .. grid-item-card:: - :img-top: _static/guides.svg + :img-top: _static/guides.png Experiment Guides ^^^^^^^^^^^^^^^^^ @@ -90,7 +90,7 @@ We've divided up the documentation into four sections with different purposes: .. grid-item-card:: - :img-top: _static/api.svg + :img-top: _static/api.png API Reference ^^^^^^^^^^^^^ diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index ce51193b8d..af8cd3752c 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -72,230 +72,225 @@ To use in the experiments we first need to define template schedule to calibrate cals.add_parameter_value(320, "dur", schedule=sched) cals.add_parameter_value(0.5, "amp", schedule=sched) -=================================== -1. Finding qubits with spectroscopy -=================================== -Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. +.. =================================== +.. 1. Finding qubits with spectroscopy +.. =================================== +.. Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. -We start with a mock backend. +.. We start with a mock backend. -.. jupyter-execute:: - - from qiskit_experiments.test.test_qubit_spectroscopy import SpectroscopyBackend - spec_backend = SpectroscopyBackend() - -We then setup calibrations for the backend. +.. We then setup calibrations for the backend. -.. jupyter-execute:: +.. .. jupyter-execute:: - cals = setup_cals(spec_backend) # Block until our job and its post processing finish. - add_parameter_guesses(cals) +.. cals = setup_cals(spec_backend) # Block until our job and its post processing finish. +.. add_parameter_guesses(cals) -We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. +.. We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal +.. from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal - qubit = 0 - freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] - frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) - spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) +.. qubit = 0 +.. freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] +.. frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) +.. spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) -.. jupyter-execute:: +.. .. jupyter-execute:: - circuit = spec.circuits()[0] - circuit.draw() +.. circuit = spec.circuits()[0] +.. circuit.draw() -We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. +.. We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. -.. jupyter-execute:: +.. .. jupyter-execute:: - spec_data = spec.run().block_for_results() - spec_data.figure(0) +.. spec_data = spec.run().block_for_results() +.. spec_data.figure(0) -We can see the analysis results +.. We can see the analysis results -.. jupyter-execute:: +.. .. jupyter-execute:: - print(spec_data.analysis_results("f01")) +.. print(spec_data.analysis_results("f01")) -======================================================== -2. Calibrating the pulse amplitudes with Rabi experiment -======================================================== -We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. +.. ======================================================== +.. 1. Calibrating the pulse amplitudes with Rabi experiment +.. ======================================================== +.. We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. -First we define the mock backend. +.. First we define the mock backend. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit_experiments.test.mock_iq_backend import RabiBackend - rabi_backend = RabiBackend() +.. from qiskit_experiments.test.mock_iq_backend import RabiBackend +.. rabi_backend = RabiBackend() -We then setup calibrations for the backend. +.. We then setup calibrations for the backend. -.. jupyter-execute:: +.. .. jupyter-execute:: - cals = setup_cals(rabi_backend) - add_parameter_guesses(cals) +.. cals = setup_cals(rabi_backend) +.. add_parameter_guesses(cals) -We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. +.. We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. -We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. +.. We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. -If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. +.. If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit_experiments.library.calibration import RoughAmplitudeCal +.. from qiskit_experiments.library.calibration import RoughAmplitudeCal - qubit = 0 +.. qubit = 0 - rabi = RoughAmplitudeCal(qubit, cals) +.. rabi = RoughAmplitudeCal(qubit, cals) -The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. +.. The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. -.. jupyter-execute:: +.. .. jupyter-execute:: - rabi.circuits()[0].draw() +.. rabi.circuits()[0].draw() -After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. +.. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. -.. jupyter-execute:: +.. .. jupyter-execute:: - rabi_data = rabi.run(rabi_backend) - rabi_data.block_for_results() # Block until our job and its post processing finish. - print(rabi_data) +.. rabi_data = rabi.run(rabi_backend) +.. rabi_data.block_for_results() # Block until our job and its post processing finish. +.. print(rabi_data) -.. jupyter-execute:: +.. .. jupyter-execute:: - rabi_data.figure(0) +.. rabi_data.figure(0) -In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. +.. In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. -.. jupyter-execute:: +.. .. jupyter-execute:: - pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 - print(pi_pulse_amplitude) +.. pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 +.. print(pi_pulse_amplitude) -================================== -3. Saving and loading calibrations -================================== +.. ================================== +.. 3. Saving and loading calibrations +.. ================================== -The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. +.. The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. -.. code-block:: python +.. .. code-block:: python - cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") +.. cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") -After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. +.. After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. -.. code-block:: python +.. .. code-block:: python - from qiskit_experiments.test.mock_iq_backend import RabiBackend - rabi_backend = RabiBackend() - cals = BackendCalibrations(rabi_backend) - cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") +.. from qiskit_experiments.test.mock_iq_backend import RabiBackend +.. rabi_backend = RabiBackend() +.. cals = BackendCalibrations(rabi_backend) +.. cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") -======================================================= -4. Using the Calibrated Amplitude in Another Experiment -======================================================= ------------------------------------------------------- -4.1. Calibrating the value of the DRAG coefficient ------------------------------------------------------- +.. ======================================================= +.. 4. Using the Calibrated Amplitude in Another Experiment +.. ======================================================= +.. ------------------------------------------------------ +.. 4.1. Calibrating the value of the DRAG coefficient +.. ------------------------------------------------------ -A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage -to a neighbouring transition. It is a standard pulse with an additional derivative -component. It is designed to reduce the frequency spectrum of a normal pulse near -the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage -to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to -minimize both leakage and phase errors resulting from the AC Stark shift. -The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. -Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and -$\beta$ is the strength of the quadrature which we refer to as the DRAG -parameter and seek to calibrate in this experiment. -The DRAG calibration will run -several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated -:math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation -with a negative amplitude. +.. A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage +.. to a neighbouring transition. It is a standard pulse with an additional derivative +.. component. It is designed to reduce the frequency spectrum of a normal pulse near +.. the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage +.. to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to +.. minimize both leakage and phase errors resulting from the AC Stark shift. +.. The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. +.. Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and +.. $\beta$ is the strength of the quadrature which we refer to as the DRAG +.. parameter and seek to calibrate in this experiment. +.. The DRAG calibration will run +.. several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated +.. :math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation +.. with a negative amplitude. -We use a mock backend in case no IBM credentials found. +.. We use a mock backend in case no IBM credentials found. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit_experiments.test.mock_iq_backend import DragBackend - drag_backend = DragBackend(gate_name="Drag(x)") +.. from qiskit_experiments.test.mock_iq_backend import DragBackend +.. drag_backend = DragBackend(gate_name="Drag(x)") -We define the template schedule for `x` pulse using previous methods. +.. We define the template schedule for `x` pulse using previous methods. -Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. +.. Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. -.. jupyter-execute:: +.. .. jupyter-execute:: - cals = setup_cals(drag_backend) - add_parameter_guesses(cals) +.. cals = setup_cals(drag_backend) +.. add_parameter_guesses(cals) -We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. +.. We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. -If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. +.. If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit_experiments.library import RoughDragCal - drag = RoughDragCal(qubit, cals) +.. from qiskit_experiments.library import RoughDragCal +.. drag = RoughDragCal(qubit, cals) -.. jupyter-execute:: +.. .. jupyter-execute:: - drag_data = drag.run(drag_backend) - drag_data.block_for_results() +.. drag_data = drag.run(drag_backend) +.. drag_data.block_for_results() -.. jupyter-execute:: +.. .. jupyter-execute:: - drag_data.figure(0) +.. drag_data.figure(0) -================== -1. Miscalibrations -================== +.. ================== +.. 1. Miscalibrations +.. ================== -In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. +.. In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. -Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. +.. Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. -We first define a simple circuit that contains an X gate and measurement. +.. We first define a simple circuit that contains an X gate and measurement. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit import QuantumCircuit +.. from qiskit import QuantumCircuit - circ = QuantumCircuit(1, 1) - circ.x(0) - circ.measure(0, 0) - circ.draw() +.. circ = QuantumCircuit(1, 1) +.. circ.x(0) +.. circ.measure(0, 0) +.. circ.draw() -Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. +.. Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. -.. jupyter-execute:: +.. .. jupyter-execute:: - from qiskit import pulse, transpile - from qiskit.test.mock import FakeArmonk - from qiskit.pulse.library import Constant - backend = FakeArmonk() +.. from qiskit import pulse, transpile +.. from qiskit.test.mock import FakeArmonk +.. from qiskit.pulse.library import Constant +.. backend = FakeArmonk() - # build a simple circuit that only contain one x gate and measurement - circ = QuantumCircuit(1, 1) - circ.x(0) - circ.measure(0, 0) - with pulse.build(backend) as my_schedule: - pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse +.. # build a simple circuit that only contain one x gate and measurement +.. circ = QuantumCircuit(1, 1) +.. circ.x(0) +.. circ.measure(0, 0) +.. with pulse.build(backend) as my_schedule: +.. pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse - circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule - circ = transpile(circ, backend) - circ.draw(idle_wires=False) +.. circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule +.. circ = transpile(circ, backend) +.. circ.draw(idle_wires=False) -Execute our circuit: +.. Execute our circuit: -.. jupyter-execute:: +.. .. jupyter-execute:: - result = backend.run(transpile(circ, backend), shots=1000).result() - counts = result.get_counts(circ) - print(counts) \ No newline at end of file +.. result = backend.run(transpile(circ, backend), shots=1000).result() +.. counts = result.get_counts(circ) +.. print(counts) \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 5377c70267..28a5037dae 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -1,6 +1,6 @@ -################## -Learning Tutorials -################## +######### +Tutorials +######### These tutorials assume some familiarity with Qiskit, but no knowledge of Qiskit Experiments. They're suitable for beginners who want to get started with the package. diff --git a/requirements-dev.txt b/requirements-dev.txt index d118184ff6..4b4c1ebf82 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -4,7 +4,7 @@ astroid==2.5 pylint==2.7.1 jinja2==3.0.3 sphinx==5.2.3 -qiskit-sphinx-theme>=1.10 +qiskit-sphinx-theme>=1.10.1 sphinx-autodoc-typehints sphinx-design pygments>=2.4 From b428d2974568a9bf8b75f2f2b48d09e3fe70d6b6 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 6 Dec 2022 00:47:07 -0500 Subject: [PATCH 17/64] fixed build error --- docs/tutorials/index.rst | 9 --------- docs/tutorials/learning.rst | 2 ++ 2 files changed, 2 insertions(+), 9 deletions(-) diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 28a5037dae..ba34dd6d36 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -41,15 +41,6 @@ Write your own experiment custom_experiment -.. toctree:: - :hidden: - - tutorials/index - howtos/index - guides/index - apidocs/index - release_notes - .. Hiding - Indices and tables :ref:`genindex` :ref:`modindex` diff --git a/docs/tutorials/learning.rst b/docs/tutorials/learning.rst index 59c45cf7dc..c86f77d9aa 100644 --- a/docs/tutorials/learning.rst +++ b/docs/tutorials/learning.rst @@ -121,6 +121,8 @@ creating a parallel experiment: .. jupyter-execute:: + from qiskit_experiments.framework import ParallelExperiment + # Create a parallel T1 experiment parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)]) parallel_exp.set_transpile_options(scheduling_method='asap') From 29e5a750733fda519b0c3b5bc0938ccc3f1b53f9 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 12 Jan 2023 13:30:50 -0500 Subject: [PATCH 18/64] Adding tutorials and merging --- docs/guides/index.rst | 3 + docs/guides/t1_experiment.rst | 4 +- docs/howtos/index.rst | 2 - docs/howtos/local_service.rst | 14 - docs/howtos/newexperimentdata.rst | 4 +- ...single-qubit_ gates_on_a_pulse_backend.rst | 388 ---- docs/tutorials/calibrating_real_device.ipynb | 1974 ----------------- docs/tutorials/calibrations.rst | 675 ++++-- docs/tutorials/curve_analysis.rst | 8 +- docs/tutorials/fine_amplitude_calibration.rst | 181 -- docs/tutorials/fine_calibrations.ipynb | 1109 --------- docs/tutorials/index.rst | 2 +- docs/tutorials/intro.rst | 1 + docs/tutorials/learning.rst | 73 +- 14 files changed, 559 insertions(+), 3879 deletions(-) delete mode 100644 docs/howtos/local_service.rst delete mode 100644 docs/tutorials/Calibrating_ single-qubit_ gates_on_a_pulse_backend.rst delete mode 100644 docs/tutorials/calibrating_real_device.ipynb delete mode 100644 docs/tutorials/fine_amplitude_calibration.rst delete mode 100644 docs/tutorials/fine_calibrations.ipynb diff --git a/docs/guides/index.rst b/docs/guides/index.rst index 3b4fb3aa6f..e7d6e4259b 100644 --- a/docs/guides/index.rst +++ b/docs/guides/index.rst @@ -2,6 +2,9 @@ Experiment Guides ##################### +These experiment guides are in-depth dives into individual experiments, their operational principles, +and how to run them in Qiskit Experiments. + .. nbgallery:: :glob: diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_experiment.rst index fed3538b63..9bbeccdb16 100644 --- a/docs/guides/t1_experiment.rst +++ b/docs/guides/t1_experiment.rst @@ -1,5 +1,5 @@ -The T1 Experiment -=========== +T1 Characterization +=================== Background =========== diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index df34856315..0c886fb659 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -5,8 +5,6 @@ How-Tos This section of the documentation provides concrete recipes for how to do specific useful actions in Qiskit Experiments. -How to... - .. toctree:: :maxdepth: 1 :glob: diff --git a/docs/howtos/local_service.rst b/docs/howtos/local_service.rst deleted file mode 100644 index 2f71b7e2e4..0000000000 --- a/docs/howtos/local_service.rst +++ /dev/null @@ -1,14 +0,0 @@ -How to save and load experiment data locally -============================================ - -Problem -------- - -You want to save and load experiment data to and from your computer. - -Solution --------- - -Experiments can be saved locally as long as you have the ``qiskit-ibm-experiment`` -provider installed. - diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst index f48469417f..6a1c282ab0 100644 --- a/docs/howtos/newexperimentdata.rst +++ b/docs/howtos/newexperimentdata.rst @@ -5,9 +5,9 @@ Problem ------- Sometimes due to events like a lost connection, the :class:`.ExperimentData` class that is returned -upon execution completion of an experiment may not contain correct results. There +upon completion of an experiment may not contain correct results. There are also times when you would like to rerun the analysis for existing experiments with -previously run jobs. +previously run jobs. Solution -------- diff --git a/docs/tutorials/Calibrating_ single-qubit_ gates_on_a_pulse_backend.rst b/docs/tutorials/Calibrating_ single-qubit_ gates_on_a_pulse_backend.rst deleted file mode 100644 index 393c6475b1..0000000000 --- a/docs/tutorials/Calibrating_ single-qubit_ gates_on_a_pulse_backend.rst +++ /dev/null @@ -1,388 +0,0 @@ -============================================================= -Calibrating single-qubit gates on a pulse backend -============================================================= -In this tutorial we demonstrate how to calibrate single-qubit gates -on ``SingleTransmonTestBackend`` using the calibration framework in qiskit-experiments. -We will run experiments to find the qubit frequency, calibrate the amplitude -of DRAG pulses and chose the value of the DRAG parameter that minimizes leakage. -The calibration framework requires the user to - -- setup an instance of Calibrations, - -- run calibration experiments which can be found in ``qiskit_experiments.library.calibration``. - -Note that the values of the parameters stored in the instance of the ``Calibrations`` class -will automatically be updated by the calibration experiments. -This automatic updating can also be disabled using the ``auto_update`` flag. - -.. jupyter-execute:: - - import pandas as pd - import numpy as np - import qiskit.pulse as pulse - from qiskit.circuit import Parameter - from qiskit_experiments.calibration_management.calibrations import Calibrations - from qiskit import schedule - from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend - -.. jupyter-execute:: - - backend = SingleTransmonTestBackend(5.2e9,-.25e9, 1e9, 0.8e9, noise=False) - qubit = 0 - cals=Calibrations.from_backend(backend) - print(cals.get_inst_map()) - -The two functions below show how to setup an instance of Calibrations. -To do this the user defines the template schedules to calibrate. -These template schedules are fully parameterized, even the channel indices -on which the pulses are played. Furthermore, the name of the parameter in the channel -index must follow the convention laid out in the documentation -of the calibration module. Note that the parameters in the channel indices -are automatically mapped to the channel index when get_schedule is called. - -.. jupyter-execute:: - - # A function to instantiate calibrations and add a couple of template schedules. - def setup_cals(backend) -> Calibrations: - - cals = Calibrations.from_backend(backend) - - dur = Parameter("dur") - amp = Parameter("amp") - sigma = Parameter("σ") - beta = Parameter("β") - drive = pulse.DriveChannel(Parameter("ch0")) - - # Define and add template schedules. - with pulse.build(name="xp") as xp: - pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) - - with pulse.build(name="xm") as xm: - pulse.play(pulse.Drag(dur, -amp, sigma, beta), drive) - - with pulse.build(name="x90p") as x90p: - pulse.play(pulse.Drag(dur, Parameter("amp"), sigma, Parameter("β")), drive) - - cals.add_schedule(xp, num_qubits=1) - cals.add_schedule(xm, num_qubits=1) - cals.add_schedule(x90p, num_qubits=1) - - return cals - - # Add guesses for the parameter values to the calibrations. - def add_parameter_guesses(cals: Calibrations): - - for sched in ["xp", "x90p"]: - cals.add_parameter_value(80, "σ", schedule=sched) - cals.add_parameter_value(0.5, "β", schedule=sched) - cals.add_parameter_value(320, "dur", schedule=sched) - cals.add_parameter_value(0.5, "amp", schedule=sched) - -When setting up the calibrations we add three pulses: a :math:`\pi`-rotation, -with a schedule named ``xp``, a schedule ``xm`` identical to ``xp`` -but with a nagative amplitude, and a :math:`\pi/2`-rotation, with a schedule -named ``x90p``. Here, we have linked the amplitude of the ``xp`` and ``xm`` pulses. -Therefore, calibrating the parameters of ``xp`` will also calibrate -the parameters of ``xm``. - -.. jupyter-execute:: - - cals = setup_cals(backend) - add_parameter_guesses(cals) - -A samilar setup is achieved by using a pre-built library of gates. -The library of gates provides a standard set of gates and some initial guesses -for the value of the parameters in the template schedules. -This is shown below using the ``FixedFrequencyTransmon`` library which provides the ``x``, -``y``, ``sx``, and ``sy`` pulses. Note that in the example below -we change the default value of the pulse duration to 320 samples - -.. jupyter-execute:: - - from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon - - library = FixedFrequencyTransmon(default_values={"duration": 320}) - cals = Calibrations.from_backend(backend, libraries=[library]) - print(library.default_values()) # check what parameter values this library has - print(cals.get_inst_map()) # check the new cals's InstructionScheduleMap made from the library - print(cals.get_schedule('x',(0,))) # check one of the schedules built from the new calibration - -We are going to run the spectroscopy, Rabi, DRAG, and fine-amplitude calibration experiments -one after another and update the parameters after every experiment. -We will keep track of the parameter values after every experiment. - -==================================== -1. Finding qubits with spectroscopy -==================================== -Here, we are using a backend for which we already know the qubit frequency. -We will therefore use the spectroscopy experiment to confirm that -there is a resonance at the qubit frequency reported by the backend. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal - - -We first show the contents of the calibrations for qubit 0. -Note that the guess values that we added before apply to all qubits on the chip. -We see this in the table below as an empty tuple ``()`` in the qubits column. -Observe that the parameter values of ``y`` do not appear in this table as they are given by the values of ``x``. - -.. jupyter-execute:: - - columns_to_show = ["parameter", "qubits", "schedule", "value", "date_time"] - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))[columns_to_show] - - -.. jupyter-execute:: - - freq01_estimate = backend.defaults().qubit_freq_est[qubit] - frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) - spec = RoughFrequencyCal(qubit, cals, frequencies, backend=backend) - spec.set_experiment_options(amp=0.005) - -.. jupyter-execute:: - - circuit = spec.circuits()[0] - circuit.draw(output="mpl") - -.. jupyter-execute:: - - next(iter(circuit.calibrations["Spec"].values())).draw() # let's check the schedule - - -.. jupyter-execute:: - - spec_data = spec.run().block_for_results() - spec_data.figure(0) - - -.. jupyter-execute:: - - print(spec_data.analysis_results("f01")) - - -The instance of ``calibrations`` has been automatically updated with the measured -frequency, as shown below. -In addition to the columns shown below, the calibrations also store the group to which a value belongs, -whether a values is valid or not and the experiment id that produce a value. - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit]))[columns_to_show] - - -================================================================= -2. Calibrating the pulse amplitudes with a Rabi experiment -================================================================= -In the Rabi experiment we apply a pulse at the frequency of the qubit -and scan its amplitude to find the amplitude that creates a rotation -of a desired angle. We do this with the calibration experiment ``RoughXSXAmplitudeCal``. -This is a specialization of the ``Rabi`` experiment that will update the calibrations -for both the ``X`` pulse and the ``SX`` pulse using a single experiment. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal - rabi = RoughXSXAmplitudeCal(qubit, cals, backend=backend, amplitudes=np.linspace(-0.1, 0.1, 51)) - -The rough amplitude calibration is therefore a Rabi experiment in which -each circuit contains a pulse with a gate. Different circuits correspond to pulses -with different amplitudes. - -.. jupyter-execute:: - - rabi.circuits()[0].draw("mpl") - -After the experiment completes the value of the amplitudes in the calibrations -will automatically be updated. This behaviour can be controlled using the ``auto_update`` -argument given to the calibration experiment at initialization. - -.. jupyter-execute:: - - rabi_data = rabi.run().block_for_results() - rabi_data.figure(0) - -.. jupyter-execute:: - - print(rabi_data.analysis_results("rabi_rate")) - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - -The table above shows that we have now updated the amplitude of our :math:`\pi` pulse -from 0.5 to the value obtained in the most recent Rabi experiment. -Importantly, since we linked the amplitudes of the ``x`` and ``y`` schedules -we will see that the amplitude of the ``y`` schedule has also been updated -as seen when requesting schedules form the ``Calibrations`` instance. -Furthermore, we used the result from the Rabi experiment to also update -the value of the ``sx`` pulse. - -.. jupyter-execute:: - - cals.get_schedule("sx", qubit) - -.. jupyter-execute:: - - cals.get_schedule("x", qubit) - -.. jupyter-execute:: - - cals.get_schedule("y", qubit) - - -===================================== -3. Saving and loading calibrations -===================================== -The values of the calibrated parameters can be saved to a .csv file -and reloaded at a later point in time. - -.. jupyter-execute:: - - cals.save(file_type="csv", overwrite=True, file_prefix="PulseBackend") - -After saving the values of the parameters you may restart your kernel. If you do so, -you will only need to run the following cell to recover the state of your calibrations. -Since the schedules are currently not stored we need to call our ``setup_cals`` function -or use a library to populate an instance of Calibrations with the template schedules. -By contrast, the value of the parameters will be recovered from the file. - -.. jupyter-execute:: - - cals = Calibrations.from_backend(backend, library) - cals.load_parameter_values(file_name="PulseBackendparameter_values.csv") - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - -=========================================================== - 4. Calibrating the value of the DRAG coefficient -=========================================================== - -A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage -and phase errors to a neighbouring transition. It is a standard pulse with an additional -derivative component. It is designed to reduce the frequency spectrum of a -normal pulse near the :math:`|1> - |2>` transition, -reducing the chance of leakage to the :math:`|2>` state. -The optimal value of the DRAG parameter is chosen to minimize both -leakage and phase errors resulting from the AC Stark shift. -The pulse envelope is :math:`f(t)=\Omega_x(t)+j\beta\frac{\rm d}{{\rm d}t}\Omega_x(t)`. -Here, :math:`\Omega_x(t)` is the envelop of the in-phase component -of the pulse and :math:`\beta` is the strength of the quadrature -which we refer to as the DRAG parameter and seek to calibrate -in this experiment. The DRAG calibration will run several -series of circuits. In a given circuit a Rp(β) - Rm(β) block -is repeated :math:`N` times. Here, Rp is a rotation -with a positive angle and Rm is the same rotation with a -negative amplitude. - -.. jupyter-execute:: - - from qiskit_experiments.library import RoughDragCal - cal_drag = RoughDragCal(qubit, cals, backend=backend, betas=np.linspace(-20, 20, 25)) - cal_drag.set_experiment_options(reps=[3, 5, 7]) - cal_drag.circuits()[5].draw(output='mpl') - -.. jupyter-execute:: - - drag_data = cal_drag.run().block_for_results() - drag_data.figure(0) - -.. jupyter-execute:: - - print(drag_data.analysis_results("beta")) - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="β"))[columns_to_show] - -========================================================== -5. Fine amplitude calibration -========================================================== -The ``FineAmplitude`` calibration experiment repeats :math:`N` times -a gate with a pulse to amplify the under or over-rotations -in the gate to determine the optimal amplitude. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitudeCal - amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name="x") - amp_x_cal.circuits()[5].draw(output="mpl") - -.. jupyter-execute:: - - data_fine = amp_x_cal.run().block_for_results() - data_fine.figure(0) - -.. jupyter-execute:: - - print(data_fine.analysis_results("d_theta")) - -The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the FineXAmplitude experiment. Note that this calculation is automatically done by the Amplitude.update function. - -.. jupyter-execute:: - - dtheta = data_fine.analysis_results("d_theta").value.nominal_value - target_angle = np.pi - scale = target_angle / (target_angle + dtheta) - pulse_amp = cals.get_parameter_value("amp", qubit, "x") - print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") - print(f"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.") - -Observe, once again, that the calibrations have automatically been updated. - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - -To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again. - -.. jupyter-execute:: - - data_fine2 = amp_x_cal.run().block_for_results() - data_fine2.figure(0) - -.. jupyter-execute:: - - print(data_fine2.analysis_results("d_theta")) - -As can be seen from the data above and the analysis result below -we have managed to reduce the error in the rotation angle dtheta. - -==================================================================== -Fine amplitude clibration of the :math:`\pi`/2 rotation -==================================================================== - -We now wish to calibrate the amplitude of the :math:`\pi/2` rotation. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.fine_amplitude import FineSXAmplitudeCal - - amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name="sx") - amp_sx_cal.circuits()[5].draw(output="mpl") - -.. jupyter-execute:: - - data_fine_sx = amp_sx_cal.run().block_for_results() - data_fine_sx.figure(0) - -.. jupyter-execute:: - - print(data_fine_sx.analysis_results(0)) - -.. jupyter-execute:: - - print(data_fine_sx.analysis_results("d_theta")) - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - - - - - - diff --git a/docs/tutorials/calibrating_real_device.ipynb b/docs/tutorials/calibrating_real_device.ipynb deleted file mode 100644 index 6de10a5aa5..0000000000 --- a/docs/tutorials/calibrating_real_device.ipynb +++ /dev/null @@ -1,1974 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b11c2f66", - "metadata": {}, - "source": [ - "# Calibrating single-qubit gates on a real device\n", - "\n", - "In this tutorial we demonstrate how to calibrate single-qubit gates on `ibmq_lima` using the calibration framework in qiskit-experiments. We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses and chose the value of the DRAG parameter that minimizes leakage. The calibration framework requires the user to\n", - "\n", - "* setup an instance of `Calibrations`,\n", - "* run calibration experiments which can be found in `qiskit_experiments.library.calibration`. \n", - "\n", - "Note that the values of the parameters stored in the instance of the `Calibrations` class will automatically be updated by the calibration experiments. This automatic updating can also be disabled using the `auto_update` flag." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e3836dba", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import qiskit.pulse as pulse\n", - "from qiskit.circuit import Parameter\n", - "\n", - "from qiskit_experiments.calibration_management.calibrations import Calibrations\n", - "\n", - "from qiskit import IBMQ, schedule" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "87e1101f", - "metadata": {}, - "outputs": [], - "source": [ - "IBMQ.load_account()\n", - "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n", - "backend = provider.get_backend('ibmq_lima')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e3f6f6c1", - "metadata": {}, - "outputs": [], - "source": [ - "qubit = 0 # The qubit we will work with" - ] - }, - { - "cell_type": "markdown", - "id": "5325fe3d", - "metadata": {}, - "source": [ - "The two functions below show how to setup an instance of `Calibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ca0c1462", - "metadata": {}, - "outputs": [], - "source": [ - "def setup_cals(backend) -> Calibrations:\n", - " \"\"\"A function to instantiate calibrations and add a couple of template schedules.\"\"\"\n", - " cals = Calibrations.from_backend(backend)\n", - "\n", - " dur = Parameter(\"dur\")\n", - " amp = Parameter(\"amp\")\n", - " sigma = Parameter(\"σ\")\n", - " beta = Parameter(\"β\")\n", - " drive = pulse.DriveChannel(Parameter(\"ch0\"))\n", - "\n", - " # Define and add template schedules.\n", - " with pulse.build(name=\"xp\") as xp:\n", - " pulse.play(pulse.Drag(dur, amp, sigma, beta), drive)\n", - "\n", - " with pulse.build(name=\"xm\") as xm:\n", - " pulse.play(pulse.Drag(dur, -amp, sigma, beta), drive)\n", - " \n", - " with pulse.build(name=\"x90p\") as x90p:\n", - " pulse.play(pulse.Drag(dur, Parameter(\"amp\"), sigma, Parameter(\"β\")), drive)\n", - "\n", - " cals.add_schedule(xp, num_qubits=1)\n", - " cals.add_schedule(xm, num_qubits=1)\n", - " cals.add_schedule(x90p, num_qubits=1)\n", - " \n", - " return cals\n", - "\n", - "def add_parameter_guesses(cals: Calibrations):\n", - " \"\"\"Add guesses for the parameter values to the calibrations.\"\"\"\n", - " for sched in [\"xp\", \"x90p\"]:\n", - " cals.add_parameter_value(80, \"σ\", schedule=sched)\n", - " cals.add_parameter_value(0.5, \"β\", schedule=sched)\n", - " cals.add_parameter_value(320, \"dur\", schedule=sched)\n", - " cals.add_parameter_value(0.5, \"amp\", schedule=sched)" - ] - }, - { - "cell_type": "markdown", - "id": "31ec74cd", - "metadata": {}, - "source": [ - "When setting up the calibrations we add three pulses: a $\\pi$-rotation, with a schedule named `xp`, a schedule `xm` identical to `xp` but with a nagative amplitude, and a $\\pi/2$-rotation, with a schedule named `x90p`. Here, we have linked the amplitude of the `xp` and `xm` pulses. Therefore, calibrating the parameters of `xp` will also calibrate the parameters of `xm`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a2f9d7e5", - "metadata": {}, - "outputs": [], - "source": [ - "cals = setup_cals(backend)\n", - "add_parameter_guesses(cals)" - ] - }, - { - "cell_type": "markdown", - "id": "af0cd5a0", - "metadata": {}, - "source": [ - "A samilar setup is achieved by using a pre-built library of gates. The library of gates provides a standard set of gates and some initial guesses for the value of the parameters in the template schedules. This is shown below using the `FixedFrequencyTransmon` which provides the `x`, `y`, `sx`, and `sy` pulses. Note that in the example below we change the default value of the pulse duration to 320 samples." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "48895a9f", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bd83e088", - "metadata": {}, - "outputs": [], - "source": [ - "library = FixedFrequencyTransmon(default_values={\"duration\": 320})\n", - "cals = Calibrations.from_backend(backend, libraries=[library])" - ] - }, - { - "cell_type": "markdown", - "id": "d145b612", - "metadata": {}, - "source": [ - "## 1. Finding qubits with spectroscopy\n", - "\n", - "Here, we are using a backend for which we already know the qubit frequency. We will therefore use the spectroscopy experiment to confirm that there is a resonance at the qubit frequency reported by the backend." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "908ff764", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal" - ] - }, - { - "cell_type": "markdown", - "id": "4c2699ac", - "metadata": {}, - "source": [ - "We first show the contents of the calibrations for qubit 0. Note that the guess values that we added before apply to all qubits on the chip. We see this in the table below as an empty tuple `()` in the qubits column. Observe that the parameter values of `xm` do not appear in this table as they are given by the values of `xp`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fa22b8a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0σ()sx8.000000e+01defaultTrue2022-07-14 14:54:15.214182-0400None
1β()x0.000000e+00defaultTrue2022-07-14 14:54:15.214160-0400None
2β()sx0.000000e+00defaultTrue2022-07-14 14:54:15.214297-0400None
3drive_freq(0,)None5.029745e+09defaultTrue2022-07-14 14:54:15.475717-0400None
4duration()x3.200000e+02defaultTrue2022-07-14 14:54:15.214171-0400None
5amp()x5.000000e-01defaultTrue2022-07-14 14:54:15.214147-0400None
6σ()x8.000000e+01defaultTrue2022-07-14 14:54:15.214114-0400None
7duration()sx3.200000e+02defaultTrue2022-07-14 14:54:15.214323-0400None
8amp()sx2.500000e-01defaultTrue2022-07-14 14:54:15.214312-0400None
9meas_freq(0,)None7.425143e+09defaultTrue2022-07-14 14:54:15.475778-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 σ () sx 8.000000e+01 default True \n", - "1 β () x 0.000000e+00 default True \n", - "2 β () sx 0.000000e+00 default True \n", - "3 drive_freq (0,) None 5.029745e+09 default True \n", - "4 duration () x 3.200000e+02 default True \n", - "5 amp () x 5.000000e-01 default True \n", - "6 σ () x 8.000000e+01 default True \n", - "7 duration () sx 3.200000e+02 default True \n", - "8 amp () sx 2.500000e-01 default True \n", - "9 meas_freq (0,) None 7.425143e+09 default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 14:54:15.214182-0400 None \n", - "1 2022-07-14 14:54:15.214160-0400 None \n", - "2 2022-07-14 14:54:15.214297-0400 None \n", - "3 2022-07-14 14:54:15.475717-0400 None \n", - "4 2022-07-14 14:54:15.214171-0400 None \n", - "5 2022-07-14 14:54:15.214147-0400 None \n", - "6 2022-07-14 14:54:15.214114-0400 None \n", - "7 2022-07-14 14:54:15.214323-0400 None \n", - "8 2022-07-14 14:54:15.214312-0400 None \n", - "9 2022-07-14 14:54:15.475778-0400 None " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "502aef29", - "metadata": {}, - "outputs": [], - "source": [ - "freq01_estimate = backend.defaults().qubit_freq_est[qubit]\n", - "frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51)\n", - "spec = RoughFrequencyCal(qubit, cals, frequencies, backend=backend)\n", - "spec.set_experiment_options(amp=0.005)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "91184061", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "circuit = spec.circuits()[0]\n", - "circuit.draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "32a49399", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "schedule(circuit, backend).draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1e24ce2a", - "metadata": {}, - "outputs": [], - "source": [ - "spec_data = spec.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e880af97", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spec_data.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6e8e067c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: f01\n", - "- value: (5.02937+/-0.00008)e+09\n", - "- χ²: 0.004001950803601646\n", - "- quality: good\n", - "- extra: <1 items>\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(spec_data.analysis_results(\"f01\"))" - ] - }, - { - "cell_type": "markdown", - "id": "125628a5", - "metadata": {}, - "source": [ - "We now update the instance of `Calibrations` with the value of the frequency that we measured using the `Frequency.update` function. Note that for the remainder of this notebook we use the value of the qubit frequency in the backend as it is not yet possible to updated qubit frequencies with the circuit path." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6937956d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0drive_freq(0,)None5.029375e+09defaultTrue2022-07-14 15:04:35.248000-0400c9a39474-dd2e-4b43-b8ee-afc2d7a50906
1meas_freq(0,)None7.425143e+09defaultTrue2022-07-14 14:54:15.475778-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 drive_freq (0,) None 5.029375e+09 default True \n", - "1 meas_freq (0,) None 7.425143e+09 default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:04:35.248000-0400 c9a39474-dd2e-4b43-b8ee-afc2d7a50906 \n", - "1 2022-07-14 14:54:15.475778-0400 None " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit]))" - ] - }, - { - "cell_type": "markdown", - "id": "adc314d6", - "metadata": {}, - "source": [ - "As seen from the table above the measured frequency has been added to the calibrations." - ] - }, - { - "cell_type": "markdown", - "id": "351b4f8a", - "metadata": {}, - "source": [ - "## 2. Calibrating the pulse amplitudes with a Rabi experiment\n", - "\n", - "In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. We do this with the calibration experiment `RoughXSXAmplitudeCal`. This is a specialization of the `Rabi` experiment that will update the calibrations for both the `X` pulse and the `SX` pulse using a single experiment." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ed4a5f77", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "8227b8ba", - "metadata": {}, - "outputs": [], - "source": [ - "rabi = RoughXSXAmplitudeCal(qubit, cals, backend=backend, amplitudes=np.linspace(-0.1, 0.1, 51))" - ] - }, - { - "cell_type": "markdown", - "id": "1b425031", - "metadata": {}, - "source": [ - "The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "b82cf6dc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rabi.circuits()[0].draw(\"mpl\")" - ] - }, - { - "cell_type": "markdown", - "id": "f8ecc750", - "metadata": {}, - "source": [ - "After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "68d32b29", - "metadata": {}, - "outputs": [], - "source": [ - "rabi_data = rabi.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ffb1e7b8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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L7UMNnQJ+D1aLgc/nx2qzssvljljzMm0G1V43/mBWRNGDqeyr3V52L3iZP1w0lSuvnhh5FlSX7+R3P7mcpZ/Mp1uvHoiAkv2fTYbNypBuWWypqKPGG6Csso77/vYGB13/LO+WZeDbvg6bxWDg1U/zm7+9zciRI+nVqxcvvfQSS5YsYciQIW3+rNKdlPPS13QuYYVsmvFrI8q+INNGrzwnFsPA5hfyM+1NKvTWmDikCMMQ/AHTfC8IRVkODIRtlaZT0K5qL0cOyO/0vsWK0aNHf2/bhx9+yD333MOaNWuoqqoiEAjg9XopKyujd+/egGkFiD52wIAB9O7dm9WrVx+Qwu/bt+/3FMGGDRv43e9+x6effsqePXsIBoMEg0G2bNnS7Hn27NnD1q1bufrqq7nmmmsi21vzC+iIz0Fbqa+v58orr2TcuHH84x//IBAI8OCDD3LmmWeyfPlysrKyYnbtYDCIx+PhhRdeYNiwYYA5SDvooIP47LPPGDt2bJPH/e1vf6O+fn9akSlTpnDfffc1276rWLR+LwqJzMDrjQz8zkz2uEzrT688J1aBXZ99QbB4Gi63b39InSEMZwcrvp7HJfP+HnkWeDwejjhiOg/e+8fIctFwyy6WWXMiv3kAi8WgT76TTXtr8WIh56iz2LVrO9lOG4W5uQAM6NuHzYaV4WdcTf3X73LYYYfxox/9qFkrhub7aIWvaZGJQ4pAYFN5DR6/OdPPz7TROz8DQ0xFbRjCpKHdWjlT0+Q4bVwxoWR/dq7QA6Agy05AKXZWufEGglw56zOeu/IoPiutTPj83I2VzObNmzn11FP5yU9+wp133klRURGff/45P/rRj77n1NeZaUebUnannXYaffv25a9//St9+vTBarUyYsSIFp0Lw+v0f/nLXxqYuFtj8eLF37MoNOa2227jtttua/M5G/Piiy+yYcMGFi1aFJkhv/jiixQUFPCf//yHiy+++HvHhC0Xu3bton///pHtu3btiuzr2bMne/bsaTCgUUqxe/fuSJtevXphtVojyh5g6NChWCwWtmzZ0qwC79OnT4P3VquVPn36HPBMtWfPnixatKjBtvLycgKBQAMrTeNjAoEA5eXlEatOZZ2XgFLYbKY1TRkWVEhFFOc4KMyyU1HjxrvzO+6YdDZGr+ENrHmTxo7h3HPPbWC12blzJ2vWrOGKK67giiuuAMzvU9bhJ/O1+wx69+2L3ZERGTTceMIw/vzBetz+ILaCXuzYtZvckMLPynAyrKQf/fv8imf+81eKC/O46qqrGDRo0AF9bumIVviaFsm0W8mwWagPxdpmOyz0iVL2NZ4AMyYP7FBa3IvHDQBg1uJS6n2ByKwhw27h3CP78NoX21m5o5pjH5hPUbYDf6P83H0T3Hdz+fLleL1eHnnkkYhSeuutt77XLhgMsmLFishsfsuWLezYsYPhw4d3ihx79+7l22+/5amnnmLq1KkAfP755/j9+yMg7HbzYR8IBCLbiouL6d27Nxs2bODSSy9t8/W6wqRfV1eHiDQwnxuGgYg061A4cOBAevbsyfvvv89RRx0FmFnbFi5cyAMPPACYSVtqampYsmQJhxxyCGCuldfW1kYGPRMnTsTv97NhwwYGDzbdjjZu3EggEGDAgAEd6ld7GD9+PH/84x/Ztm0bffv2BeD999/H4XA0aW0C0wpls9l4//33ufDCCwEI1LvwetxkFRdSVe/Dj/ldLcywUJTtwBDB467H8NczaeyYiCIG0/fjq6++4tFHH21wnT59+vDNN9802PbUU0/x/vvv849rxrMjmIs7aEQGDZ+sK6d7joPyGi/1PvBlFLGlopb+hebg1WoxUD5hvcuKXfbx7rvvcv/993fK55gOaIWvaZG73lrNmp1mqJLdIhRk2qms80VG5DMmD4wo7ANFRLhkfAlnHdGnSR+AWo+feat2UeMJkJsRpCjLAezPzz2lZ4CpHe5p7Bg6dCjBYJBHH32Uc845h6VLl37vwQjmTO83v/kNTzzxBBkZGfziF79g5MiRB2TOb4qCggK6devGM888Q79+/di+fTs333wzVuv+x0CPHj3IyMjg3XffpaSkBKfTSV5eHnfccQfXX389+fn5nHLKKfh8Pj7//HO2b9/Orbfe2uT1usKkP23aNG6++WauvfZabrjhBoLBIPfeey8Wi4XjjjsOgO3bt3P88cdzzz33cPbZZyMi3Hjjjdx9990cfPDBDBs2jD/+8Y9kZ2dHlN/w4cOZPn06V199NY888ghZWVlcffXVnHbaaRx00EEAnHDCCRx55JFceeWVkft54403MnbsWMaMGdOszBUVFQ0sKkuXLgVosN7e3My8KU488URGjhzJpZdeykMPPcTevXu5+eab+clPfhJRysuWLePSSy/l6aefZurUqeTl5fHjH/+YX//61/To0YOioiJm3nULjoN/hMWeyeaKOkCgvoqamno8zn54vD7K9+zmwuOObKDsAWbOnMnQoUO/l0DGZrMxatSoBtt69OiBw+Hg6CMPjWz705/+hHXsWNZ5C/B4fWQGa6j1GRg2B7WeAEoFqXa5QIEHK4uXf8mvH/41Bx98cMRyoGkdrfA1zTJvZRmzF5cC0DvPyawrjmJTeV2zTnkdpSkfAJfbx3e7a8hxWnG5/eyodJNps5Bht0bi9N/f4uY3Hn/CFt859NBDeeyxx7jvvvv47W9/y4QJE3jwwQc5//zzG7RzOBzcfPPNXHrppWzZsoVx48bx2muvdZqZ3zAMXn75ZW644QZGjRrFkCFDeOihhxqY3a1WK3/+85+58847ueOOOzjmmGOYP38+V111FVlZWTzwwAPceuutZGRkMHLkyGYd9rqKgw8+mDfffJM77riD8ePHIyIcfvjhzJ07NzLb9fl8rF27lqqqqshxv/71r6mvr+dnP/sZ+/btY+zYsbz33nsNMre9+OKLXH/99ZxzzjkAnHHGGTzxxBOR/YZh8NZbb3HDDTcwefJkMjIymDZtGg8//HCzDnsA55xzDh9//HGL/WpPZIbFYuHtt9/m2muvZeLEiWRkZHDRRRdFrBVgWkLWrl3bwHfg0UcfxWq1cv7551NfX8/xxx/PddMP5eklZQQVZDusFBd2Z/v2bXy79jssziwOc+7j0Xvva3B9l8vFSy+9xO9///s2y9yYmpoarrnmGvY4epNzzKU4DUXPbt3Z7QF/ULGnxos1EGD7tu34xcazb77GqePG8cADD2CzJd6yXqIiqRzKPmbMGNXZGcPSJbVuWZWbEx/9mOp6P/kZNv75k7EM753XdQKGmLeyjHvnriHLYWHdrlq8gSACDO+VgyX0UN1VUc2d5x7Z5cV3xowZ0+kZ6XQa18QjVfoBrffld//9hheWbsFiCMU5DkTo0vK2LrePM59chMNiYLUYVNZ52brPHKQM6paFw2rgCQR547pJKG99i32Jxe8zFsRCp4jICqXU98xMiTkl0sSVYFDxi5e/pLrej90i3HnmyLgoe9gfFmiIjYHdMlm7qwYFlO6tY1B3MzQooEiqOH2NJhGZ+81OXli6Bash3HfuIWTYLeaAPwbWvOZo7MSbn2mn1hOgos7Lloo6inMc/HTKYLIdVlz6J99utMLXfI9nFm5kyca9GAInjijm5EN6xU2W6Dh9u9XCgEJzfbHOG2BvjZdu2Q4sQlLF6XcGI0eOZPPmpmsM/PWvf+Wiiy7qYok0yUTjQlglRZn8+tWvAbjqmIGcckgvMuzxUQ+NnXidNsFmEXwBRYbdwo+O7tfKGTTNoRW+pgErt1fxwLtm6tDuOQ5uP2NUROHGg8Zx+rkZNgoybeyr81FW5SbLbmAYHHBYYLLyzjvv4PP5mtzXOPZeowkTLoQ1a3EpwaDCFwhiNYSyag8ef5BxgwqZccwgnLb4lQNuyonX6w9y37xvWb+nln8t386FY/u3fiLN99AKXwOYI/75a/dwx5ur8AcVmTaDR88/nG45jrjK1VScfu98Jy63H39QsXWfmx8OtSasw16s6MqwL03q8PKKHTy/bIf5W7KbA/mKWi8efxBD4Ij++eRn2WO6Tt9WGjvx2izCrf9ZyX3zvuXEET2I75MpOUmGanmaGBJd+vZ3r6+kvMZcGLNbDdbtqkmICmsXjxvAjMn7i+9U1vkpyjI9cz3+IJnW+Muo0SQ6LrePf3y2IzJw3r6vjq0VtZRVmU5xRdkO3l25izpvoJUzxYcLju7P6P75VNX7uOOt1QnxbEo2tMJPc8Klbw2gKpQv22YIPXKdPLMwMWrRh0180cV37jjzEKaPNE3Xs1f7qa7XHjwaTUssWr+XoNpfCAugzhsgoMy6GN2y7ARV4lamFBHuPfdQrIbw5lc7WVpa1fpBmgZohZ/GRJe+3e3yRPLk9yvMxGG1JFwt+rCJ74Kj+zN9VE8eOO8wsh1WPAG46ZWv4i2eRpPQmBEv+2fFvkAQb0AhQI8cB5aQY1wiR7wMLc5hxmQzle4f562nPkGtEYmKVvhpTLj0bb0vQLU7VO7WEDIdpsNOoteiz8mwce85ZtrT91fv5rNNe+MskSaZ2bp1K1OmTGHEiBEceuihvPLKK/EWqVMxI17MtfmgUnhDyr8gy06Ww4pgesMnesTLDccNoV9hBlsq6nj8w3XatN8OtMJPY0zv1wA7Ks01PAEyQz/8MIk+4j/tsN4MKzDre/3qla8IBvWPX3NgWK1WHn30UVavXs17773HjTfeSG1tbbzF6jQmDimK1MAorzG98m0WoXu2HUPocCGsrsJpt/Kns8x0vX9dsJHHP1jHvJVluNxNR61o9qMVfhqTn2mnzheMjPSLcx30L8xs0CYZRvxXjrQjAlsq6vnj26t5adkW/QDoIk477TQuv/zyeIvRKXL06tWLww8/HDBz2Xfr1o2KioqOC5cg5DhtXHRUb6rqfeyu9gCmKd9mNfAHFDWeAFdMKEn4iBelFJv31pFptxAIKp76eAP3zF3DmU8u4oUlpXrG3wJa4acxQ3tkNXDUK8puGOiSLCP+4iyDw/uamQBnLy7lsf99x736AaDpACtWrCAQCNCvX9cmeVmwYAFnnHEGffr0QUSYPXt2u46/5557EJEmaxw89dRT/OmK6Wxa9TkKsBmgFNS4/XgCQQ5SW7n/6jPIzc0lNzeX8ePH8/bbb3dOxzqRsKNxcY4dAdy+IBZDcFgMZi5IDEfjREUr/DRm1uLSiKNer3wnRlTsbbj0bTKM+D/c4mdXtQcBggoCypzN6AdA24iu3KYxq9ldeumlzJw5s8uvXVNTw6hRo3jsscfIyMho17FLly5l5syZHHrood/b9/LLL/Pzn/+cK3/+GzL6jQClqPjo71x0eCG3nDycN66bxOnD87j/vvv4/PPPWb58OccddxxnnXUWX3/9dWd1r8NEOxpn2CzYraYKK6tyY7FIwjkaJxpa4acpe+qC/HPZVgBKijKxGgYut4+KWi8utw9PINgppW9jjcvt4/0tPvIybJEkQbtdHgLBYKSann4ANGTKlClcc8013HTTTXTv3p2JEyeilOL+++9n8ODBZGRkcMghhzBnzpwGx9XV1XH55ZeTnZ1NcXExd999d5Pnbjy7vPzyyznttNMi75VSPPTQQwwdOhSHw0Hfvn0jJXY7S46mOP/88ykqKmpQmnjNmjVkZmby0ksvAeDxeDjrrLO45ZZbInXvu5JTTjmFu+++m/POO6/FinuNqaqq4qKLLuLvf/87BQUF39v/8MMPc/nll7Oj8EiCCk4c2ZOCPV+zbcG/mD6qJ9kOK2eeeSYnn3wyQ4YMYdiwYfzpT38iJyeHJUuWdGYXO0TY0TgcWui0mf/XeQNU1/sT3tE43miFn6b8d72XQFCRYTN48qIjef26/THu4RH/JeNLEiLjVkuYDwAzoqBHjiPibrij0g0kfqRBvJgzZw5KKRYuXMjzzz/Pb3/7W5599lmefPJJVq9eza233srVV1/dwKR700038f777/Pqq6/ywQcf8MUXX7BgwYJ2X/u2227jrrvu4tZbb2XVqlW88sorEdN5LOV49NFHufDCC7njjjsAU7n/6Ec/4rzzzuOCCy5AKcXll1/OcccdxyWXXNLq+e6++26ys7NbfC1cuLDdn8+BMGPGDM477zymTp36vX1er5cVK1YwYuJJzFtTjs0izJg8iBNPnMbixYubPF8gEOCll16ipqYmLgOf5ggX0wpjEYko/V3VboIqmPCOxvEksW21mpiwfreLxTvM+NUJgwsZ3jMXw5AuLy/bGVTWeQmHFhsiZNgt1HkDVNb76OkPYLNa9AOgCQYOHMhDDz0EQG1tLQ8//DDvvfcexxxzTGT/smXLePLJJzn11FOpqanh2Wef5e9//zsnnXQSALNmzYrUnG8rNTU1PPLIIzz66KNceeWVAAwZMoTx48fHXI5evXrxq1/9iieeeILNmzfz6KOPUl1dzZNPPgnAokWLePnllzn00EP573//C8ALL7xASUlJk+f76U9/yg9/+MMWr9mnT5+2fCwd4plnnmH9+vXfs4SEKS8vJxAI8EF5NhDgjMN6c0jfPHr27MkHH3zQoO0333zD+PHjcbvdZGdn85///IdDDjkk5n1oK9HFtAD6FGQSDCrW7nLh8QeprPNhsxgJ72gcL7TCTxOiq2O9/NlWFGZ2rVtPGYFhJPYsviXyM+1YosQf1C2Ltbtc+AKK7ZVuSrplJUWkQVczevToyN+rV6/G7XYzffr0BhYdn88XUXYbNmzA6/Uyfvz4yP7s7Ox2K4PVq1fj8Xg4/vjjm9zXGXL84x//4Oqrr468nzt3bmQAUVJSQn5+Pvfffz8zZ85kwYIFkZrqkyZNIhjcP3sM43K5muxLYWEhhYWF7eh957N27Vpuu+02PvnkE2w2W7PtnP0PZWV5gEy7hauOGYS9mYJYBx10EF9++SVVVVX8+9//5rLLLmP+/PmMGjUqVl1oF9HFtMLfEMMQeuQ42FHlZle1h34FGQnvaBwvtMJPcZRSzFm6OVIdq9bjZ08oX/6gblkMLMqKs4Qdw3wAEKmmJyL0ynOypaIel8dPvdeXFJEGXU1W1v77HlZyb775Jv37N6xC1pISaQrDML4XFdFcVb/GdJYcZ5xxBmPHjo28bzzLPuyww3jqqae4/fbbGwwc2svdd9/dqv9A9GAjFixZsoTy8nJGjhwZ2RYIBFiwYAF/+ctfqK2tpaioiIIplwPwg9F9GNI9GxFh165d9OzZ0Kpnt9sZMmQIYA4KP/vsMx555BGeffbZmPWhPUQX08qwGVhCRf0Ks+zsqfHgCyiG9cxJeEfjeKE/lRQnHMISro4VLo4DsLfWy4vLtnDJ+JL4CdhBcpw2pvW3Mb/MHykKkuu0YbO4I7P8204Zrh8ALTBixAgcDgebN2/muOOOa7LN4MGDsdlsLF26lEGDzNSmtbW1rFy5ksGDB0fade/enZ07dzY49quvvorM0IcPH47D4eCDDz5g6NChMZEjJycnMmtvCqUUI0eO5Le//W0Ln0rrJIJJ/6yzzmLMmDENtl1xxRUMHDacEy66jte+2sWOynrsvYZhC7i56Kg+2EKe7e+//z7nnntui+cPBoN4PJ6YyX8ghB2Jn124EW/Ahy+gsFmEgkw7u10evt5WhcennXSbQj8FU5joEBarxWBzeQ01HnPtPssG+RmmB/vZR/ZNaoV4XH8rw4YNZNbiUup9AXwBRV6GjfIaL25/kJNH6frwLZGTk8NNN93ETTfdhFKKyZMnU1NTw9KlSzEMgxkzZpCdnc2Pf/xjfvOb39C9e3d69+7NnXfeSSDQMJf5cccdx4033sgbb7xB3759mTNnDlu3bo0o/JycHH7+859z66234nA4mDx5Mnv37mXFihWRyIHOkKM5nnzySRYsWMBBBx2ExdKxmu+xMOnX1NSwfv16wFS2W7Zs4csvv6SwsJD+/fvzxBNP8MQTT/Dtt98CkJ+fT35+fuR4pRSBgRNZ1Xs8u1fV4gusiwzyqzd+ydx/7yAw7QT+8pe/sGPHDn76059Gjr3llls49dRT6devHy6XixdffJH58+cnXCx+uJjWCUPz+KrMQ2Wdl/xMO+MHF3Ha4wvZWlHPv1dsj7eYCUnyPuU1rRIJYQnVva73myZTQ6DAIVgtFup9Pj5ZV56UDnthwg+As47oE/FTyM2wceebqymrdvPHt9fw6AVHxlvMhOauu+6iuLiYBx98kGuuuYbc3FwOP/xwfv3rX0faPPjgg9TW1nL22WeTmZnJ9ddf/73Us1deeSVff/01V155JUoprrvuOs4++2zKy/dHSdxzzz0UFBRw1113sW3bNoqLi7n00ks7VY6mWL16NTfffDM/+9nPePrpp6mrqyMzM7PV47qS5cuXN/C0/8Mf/sAf/vAHLrvsMmbPnk15eTlr165t9vg5SzdT1fsoMgmS47RR6/FHat13H3YkD7/+Kr+++SZGjRrFO++8w4AB+8Nuy8rKuPjiiykrKyMvL49DDz2UuXPnRpwjE41sh5XpoxqGIF47ZTC3vraSZxZuJKiS1zcpVkgqZyEbM2aMWr58eaeec/78+UyZMqVTzxkrXlq2hcc/XEdhlgO3L8C63TUAFGTayLMGyMnJoaLWy/XHDeGCo/u3crbEpbl78u/lW7np319jNYRl/3c8hVmO7x/cAcaMGUNnf79cLleL5uhkIpH64vF4GDt2LCNGjOBvf/sbOTk5LFq0iHHjxrV6bCL1oyVcbh9nPrkIh8WIxKmXltfi8vgpzLTRI8d8Drz188lJbdEL09R98fqDHHPfh+xyeXAsn8Pa//0zTtK1nVjoFBFZoZQa03i7jsNPYaJDWPbU7F+H65GzX/Glsgf72Uf2pXuOA39Qcd+8b+MtjiaO3HLLLVRVVfH000+TmZnJ0KFDeeyxx9iyZUu8Res0Giel2bzXVPYCFGbbsVktCV3vvjOwWw1+Eiqf6x48hde/2KZra0ShFX4KEw5hqff6qQzlzM92WCPpKJMlV/6BYjGEX5xgOoa9tmI71fX6B5+OvPfeezzxxBPMmTOHvDyz5sL//d//8eGHH3LZZZfFWbrOo3FSmnqf6deQl2HDaTX9FfzB1M9JceHR/cmwWZCCvvz+9dU8/uE6XVsjhFb4KUw4hKUsVBlLBHrmOQEhEEye6lgd4Qdj+lGYacMXVNz/7rfMW1mmR/xpxoknnojP52PixImRbZdccgm7du3io48+iqNknUu0Rc/jN51XwZzdh9MaWI3UteiFefXzbdhDyTlcHh/5mXZdWyOEVvgpzmmH9sLjN0f6Nos523e5ffgUSZErv6PYLAZXH2ua+P6xdAv3zF2jR/yalCQ6Kc0elznIz3WaRWbA3G5I6lr0YH9kUnGuE1SQoIKNe0yHTl1bQ3vppzxzlm6JxKnecNxQumU7yM+0o8rWcHISx9+3B4shCKAAnz8YKQPsDwSZuWAjQFLnItBoYL9F7+n5G9gXWsIryrJjyP7ql5ce3TulLXphPwaH04p4alHOHNy+AEopRASrxaDeF0j6yKQDRc/wU5h6b4C/L9oEQL+CTH4yeRAXHN2f6aN6kmFNj5AVl9vHi8u2kp9pZmrbWeVm2746QI/4NanHxeMGMKzY9Fx3Wg08/iCuUL37GZMHcv7o3nGWMLZE+zEYPjMqSWGa9sOkc22N1B3qafjPF9vZV+fDagi3TD8Ih7VjiUaSkfCIv0eOk311PhTgD+w34af7iF+TWnj8Qb7eXgXAhWP7M7Q4h4JMO5OGdiPbYW22LkCqEO3HICpIttNKjdtPWZWHXKcNkJSOTGoNrfBTFKUUs0Kz+4JMG5OGdY+zRPEhPOLPcdrIcVhxefzUeRvO5tN5xK9JLd76eicVtV4Gdc/ixhOGkpuRXoot2o8BoEeOnRq3mXzI4wtiMSSlI5NaQ5v0U5RPN1WwbncNhsB1xw0m056eY7voEX/3XHPtPqDA69ufijWdR/ya1CF6kH/24X3IdrSv8FEqEPZjqPH4UWKQZbdFwpB3VtWnRWRSS2iFn6KEf/jZDiunHxr7mtyJSvSIP8tuxRH68e+odgOpn4tAkz4s37yPVTuqycuwce7oPkld9rojXDxuADMmDwLDisvtI9NmLmW6PAEuHtcv5SOTWiI9hzkpzo7Ket5fvQuAk0YUU5jduSllk4nocprZDis9chxs3VdvOjL5/NT7FDMmDzygEX+vXr2+V6mso7jdbpxOZ6eeM16kSl+SpR/Vh/wAikex74t5nHb8rRjyfYWfLH1pC6315aA+/bnh6qfYW+Phj2+vod4XoKrejzTxuaQLcVH4InItcDPQC1gF3KiUWthC+wuBXwPDgGrgf8BNSqmyLhA36ZizdDNBBRk2gxlTBrd+QIoTHtHPWlyKIWbxoKCC3S4PvzrxoAMe8b/55pudKSaQXLUaWiNV+pIM/dhRWc+k+z4EBRdcdCn3/+DhJtslQ1/aSnv6sqOynifnb+CFJZu54fihOG3p58AMcTDpi8j5wGPA3cARwGJgrog0Wb1FRCYCLwDPASOBs4ARwD+6Qt5kw+0L8OKnZn7wQd2zGFiUFWeJ4k+4mt7rP5vIraeM4PiDewBmmtELxw5I6xG/JjV4ITTId9oMfjplSLzFSTh+fMwgLIbg8vh5d1X6zhPjsYb/S2C2UuoZpdQapdT1wE7gmmbajwe2KaUeUUptUkotBR4HxnaRvEmBy+1j3soyfvfflVTWm6F4vzxhWKSQhsY0708f1ZP7zjsMiwg1ngBLNqZuIRFNehA9yB9WnEOJHuR/j8IsOyeOKAbgkfe/IxhMz+yaXaoNRMQOjAbea7TrPWBCM4ctAnqJyOli0g24AHgndpImD0opXlhSyplPLuKed1bz3y+3R7aXVtTptLFNUJhl5/jh5iz/vrm6ip4muXnzqx1U1fuwGcJvTjoobZ31WuOa0PLm5oo6NpfXxFma+CBdqRBEpDewHThWKbUgavvvgYuUUgc1c9w5wGwgA9Pv4H3gTKVUfRNtZwAzAIqLi0e/9NJLndqHmpoasrOzO/WcHeGDzT7mlfpwWsAXhLI6837m2sBhFaaX2Dh+wPfDcxKtHx3hQPqytsLPPcs8CPDYFCe5zsRY00v3+5KIJHo/7lpSz4aqILl2ePDYzEjhmKZI9L60hwPpyy0L6iirU4ztaeGawxPDeTEW92Tq1KkrlFLf8yhOeC99ERmBacK/C3gX09HvAeCvwKWN2yulZgIzAcaMGaM620ElkZxeXG4fd65YRLc8B1aLwdqyasB0SutdlIMAi8qD/OaCSd/zQk+kfnSUA+nLsUrxwrqP2LavnhWeYu6YPio2wrWTdL8viUgi9+Pbsmo2zFuIADecMJwTQ7XgmyOR+9JeDqQv1zpK+f3rq/iyXHH0hEkJkZ+kK+9JVy/wlgMBoLjR9mKgOU+KW4FlSqkHlFJfK6XeBa4FLhGRvrETNfEJp421WgwCQYU3lDI2y27FZjELRQSDik/W6XXqxogIl08oAeBfy7c1qCOu0SQ6YZ+dO95YDUCG3cIZh6d2nvzO4Nwj+2K3mDUG3gwtf6YTXarwlVJeYAUwrdGuaZje+k2RiTlIiCb8Pq090qILRUSnhu2R6wBMs55OG9s8Pzyqn1ky2BfgozW74y2ORtMq0T47d7+zmqUb9wIQDCreXVWmfXZaIcth5ZRDzZoZT360Ie2c99qsMEXELiLjROQcEblIRE4SkZIDuObDwOUicpWIDBeRx4DewF9C13leRJ6Pav8mcKaIXCMig0Jhen8GPldKbTmA66cM0WljK0JKXaBBjKlOG9s8uSGvfYAH3lsbZ2k0mtaZs3QzMxdsxGExCAbNSnAC9MxzMnPBRuYs3RxvEROen0wylz227qtn4570ct5rUeGLiEVEzhOReUAVpsf8vzHj4ucCG0Rki4jcJyJtCv5USr0M3Aj8FvgSmAScopQKf1P7h17h9rMxQ/muA1aGrv8dcGbbupi6hNPGutw+3D5zpt8z1xHJsKXTxrbOVZMGArBhTw07K+viLI1G0zwut49Zi0vJdlixWgx2htJDG4aQ5bDqUs9tZGSfPAZ1z0IBD7//XbzF6VKaVfgich7wLTAH8GAq6GnAYZgZ78YBF2Iq4LOBNSLyjIg0Xp//Hkqpp5RSJUoph1JqdLTHvlJqilJqSqP2jyulRiqlMpVSvZRSFymltrW7tylGOG3sHpcHMJ318kKzeX8gmPaFItrCYf0KGNQti6BKvx+/JrmI9tlx+wIEQubobtl2DNE+O+3hipD/zgff7qY2jQZILc3w/ww8CfRUSp2plHpIKfWhUuobpdR6pdQypdTLSqlfKqWGYc7UiwiFxGm6hrOP6IMv5Kxntxq43D5cbh+eQJAZkwemdaGItnLFxBIA3vx6Z6SspkaTaET77FTU7vfLKYhastM+O23j3NH7nff+7z/fMG9lGS63L95ixZyWpn6DlFLutp5IKfUpcI6IJEZwY5rwzjdleANBbIZw7ZTB9Mhxkp9pZ9LQbnpm30bOOqIPd7y5GrcvyGP/+44+BZnkZ9qZOKSIHGf6lRjVJCZhn51gULEvpNSthumnE0b77LSOUopXV2zDbhW8ATNx0RdbKrFYhCsmlHDxuNRNt92sRmiPsu+M4zQHxj8+NV0f8jJt/OSYwWTYEyOBTDKR7bAyrGc2q3e4+OuCjXTPcWCzGBhG6j8ANMlD2GdnX52XoDKd9foXZRGOyNE+O20j7PjYLdtOjaeegAKHzcAiwswFGwG4ZHxJfIWMEW3y0heRYSJydNT7DBG5R0TeFJHrYieepiW+Lavmq21VCHDZ+AFa2R8gc5ZuZk+16QfhDShynTZynDYcFkN7PmsShrDPzt6QOd8whIxQRI722Wkb0Y6POU474SzEe1werBYj5R0f2xqW9wRwXtT7PwG/wgyne0REftbZgmla51+fbQXMpBtnHZHWOYgOmPADoCjLjjX069+0txYgLR4AmuRiykE98PjNdfwsh4XKOu2z0x6iHR8BHFbz/6p6H0GlUt7xsa1DwcMwHfgQEQMzpe1vlFKPiMgfMB31noyNiJqm8AWC/OcLM1PU8F459MnPiLNEyUn4AWCzWynIsrPH5cHjCxKOcLZaDOp9AT5ZVx6J2ddo4sV/Q795p83g1unDEUH77LSDaMdHMBV+vS9IUEGtx0eO057Sjo9t/YbkAXtDfx8BFGCG4wHMB27qXLE0rTF/7R721ZllcK89drCukHWARD8ACjNNha8Aty8YSWCUyg8ATfKglOJfK0yrXr+CDM4/qp/+3beT6GRlAP0Ks/CV11DrCbC72kuO057Sjo9tNenvAsKJdU4ENiiltobeZwPa3tnFvBL64Wc7LBw1qCjO0iQv0Q8Au9XAEnqA7qre73uayg8ATfKwfPM+tlbUYwj8atowrewPgLDjY3T4bVGW+duu8wVwe30p7fjYVoX/BnCPiDyIuXb/StS+Q4CNnS2Ypnn21Xr5MJT7/aSRxeTq0LEDpvEDoFeuGVXqcvtRSmnPZ03C8Mpyc5Cf5bAyYYj+Ph4IYcfHGo8/8pvPddoiznu7a7wp7fjYVoV/C/AWcBKm8v9T1L4zMOvTa7qIN7/egT+osFsNLh5XEm9xkprGD4C8TBuCuYJfUevVns+ahKDO6+ftr3cCMHFwEbkZ2uJ0oFw8bgAzJg/CEwjicvvYV+eLlMlVCn50dP9WzpC8tOkpppSqBX7SzL4JnSqRpklcbh+L1u+lss7L3xZuAiA/w8rQ4pw4S5b8hD2bZy0uJehTZNgt1HkD7Knx8PvTRmjPZ03ceXdVGbXeADaLcO2UNpUt0TSDiHDJ+BLOOqJP5Jla4/bzx3fW4HL72bS3hqE9cuMtZkxok8IXkY3A2Uqpr5rYNwp4Qyk1qLOF05iOOnOWbjaVUVBR5/Wz22U6kB3WNz8SVqI5cBo/AL7eVslT8zcQCCpOP6y3Trqj6XKiB/j5mXb+ucw05+c6bRzcKzWVUVeTE1UtE2DOp5sp3VvHEx9s4LEfHRFHyWJHW+2UJYCjmX1OQE+BYkQ4K1S2w4rVbkQKPQiwckc1c5ZuTtmsUF1N+AFw0shiXv18G7uqPTy3uJSfnzAs3qJp0oTGA3xfIIgA2ypNJ9JLxvXDrgf5MeHCsf25+51veW91GW5foEGZ8VShPd8c1cz2MUBlx0XRNKZxOcxtFbWU15ize4fNIC9DJ4WJBSLCD0b3A+CFpZtRqrmvvkbTuUTXu89x2ijMcuALVcUTwGqknhJKFH4wuh+GQL0vyLLSva0fkIS0VB73F6Fa91swlf2b4fdRrz2YCXfmdZXA6UTjrFD+oIqMurpn27FZLCmdFSqenDfazFy4t8bLlr11cZZGkw40HuBv31fHtopa9tWZVdxsFuG1L7brAX6MKMiyc3RJIQBPfbQhztLEhpZm+BuBD0IvAZZHvQ+/XgV+QTMOfZqO0TgrVDilpiGQHQrF00lhYkNJtywO7pmDAp76eH28xdGkAY0H+AABBd7Q774410FQ6QF+LDn/aNOy9/nmypQsl9uswldKva6UukIpdQXwHHB9+H3U66dKqT8rpfQUKAZEJ4UJBhXeUN37bIc1kvddJ4WJHRccZf743/xyJ4GgNutrYkvjAT7sV/aGmD4meoAfW04e1Qu71cAbCDJ35c54i9PptGkNP6TcN8VaGE1DopPCVEeNNrtlOwDRSWFizBmH98EQMwPXV1v3xVscTYrTOO1r7/wM/KGBptNmwWIYeoAfY5w2Cycc3AOAvy3clHL+O8166YvI74G/KaV2hP5uCaWUuqtzRdOEk8LMXLCRilBJTIsIGXZLpBzmjMkDdVKYGFGYZWfC4G58sr6cR/+3jud/PDbeImlSmOgBvtViUOsJRBR+jxyHHuB3ERcc3Y93VpaxYU8tr3y2hSDmIGvikCJykjyraUua4nZMZ7wdob9bQgFa4ceAi8cNoNbj5955awHTO7/W48cwRJfD7AIuOKofn6wvZ+nGCrz+oA6J0sSM6AF+tsNKZX2o7r2YVd30AL9rmDC4G06bgdsX5E9zvyXbYcVmMTAM4YoJJVw8bkDS5uZo9pujlDKa+lvTtYgIWaFRpdUQrppUwoje+bocZhdxwohinFYDtz/I/1bv4pRDe8VbJE0KEx7AP7toE5Uh73yn1cAXVHqA30X8c9mWSBGtOk+AfoWZSGgJdeYCs2xMsuY+0Yo8CXjt820AFGTauHbqUKaP6qmVfRfhtFk4KZSN6/GP1sVZGk2qE876+LMpQ1CAReAX04bxxnWTuGR8SdLOLJOFcGhk92zTT8IXVJGwXKvFINuR3LlP2q3wRaSHiPRv/IqFcBrYWlHHF1sqEczRfypmf0p0wt763+2qoS5Jf+ia5OLdVWUA5GXYuWyCNuN3FeHQyGyHDZvFHFyFw6HBVPrJnPukrbn0c4HHgPNpPsWu1kQx4I2vdgDgtBmcfljvOEuTnhw9sIj8TBuVdT7ueGsVR/QrSBknHk3isa/Wy/y1ewA458g+2m+kCwmHRooIBZk2dru8eP1BlFIR60oyh0a2ddj4JHAu8CzwDeCJmUSaCEqpiDm/OMdBv8LMOEuUnhgCw3pks6x0H/9esY1P1pWnjBOPJvF4+5ud+IMKh9XgonHaeNqVRIdGFmTa2e3yogC3L0BGqIRuModGtlXhTwduVko9GUthNA35tszFhj21iMCVkwY1iNHVdB1zlppVtAACQbNimdVipIQTjybxCFv18jKs9C/MirM06UV0aKTdasFhNfD4g+yp8dC/0Jr0oZHt0SBrYyaFpkn++8V2ADJtFo4f3iPO0qQnYSeewkxbJLvhpr21QGo48WgSi7IqN59tqgDgxxMHRrzFNV1DODSyxuPHHwhSkGXO5F1uP15/gBpPgCsmlCStT0VbFf5LwOmxFETTEKUUr39pjvQHdcuiZ15GnCVKT8JOPDarhfxMc73e40sdJx5NYvH2NztRmD47ZxzeJ97ipCUXjxvAjMmD8ASC2EIDrqACV70/6UMj2zpMeQ94VERygHeAisYNlFIfdqZg6c7nWyopq3ZjCFx1jB7px4vo/OYFmXbKa8w1PX8ggNVi+qkmsxOPJrF4/UvTqleUZac41xlnadKTcGjkWUf0YdH6vfzx7dVs21fP0OLspF+6a6vCfz30/0Dg8qjtCrOSnkJ76Xcqb4Zm95l2KxOGJOd6USoQ7cTjtFkwxBzt73F56ZVvWl2S2YlHkzhs2VvH19uqEOCnxw7G0IP8uJLjtDF9VE/2uNz87vVVfFpaQZ3XT6Y9Oc350HaFPzWmUmgaEAgq3vrGVPgjeuVQlNVcJKQm1jTOb94t28Ful4d99T565TvxB1RSO/FoEoc3vw6H4FqYHkr2pIk/px/Wmz+8sQq3L8iyjXuZcnBxvEU6YNqk8JVSH8daEM1+lm2qoLzGi0XgiokleqQfRxrnNzdDdTwEgoo6rx+vH53fXNMphL3ze+c7QxUxNYlAfqadMQMKWFa6j78u2Jj6Cl8Te1xuH4vW76WyzstbX5t1mDMdVo4eWBRnyTRhJ51Zi0sJBhVWQ/AHFburPfx6+sFJ7cSjSQzW7XKxtsyFANdNGazzOiQYPxjTj2Wl+/h8SyUuty9pE261NdNeaw55Sil1fCfIk3YopZizdHNEmXj9AcqqzbxGPbLtFGQm5xcrlWjsxPPmV9t5+5syggouGquT7mg6zpuhQX6G3ZLUM8hU5ZRDenHra9/g8Qf5+Ls9nHZocmY9bWtYnoHpnBf96gZMBIaF3msOgDlLNzNzwUYcFoMcpw271UKoBDaV9T7+8emW+AqoiRB24vnD6SMBqPUG2FReE2epNMmOUoo3Qt75JUWZkdhvTeKQ5bAyYYhpbf3bwk1xlubAaZPCV0pNUUpNbfQ6FBgB7APujqmUKUo4qUu2w4rVYrB9Xx07KusBM51rYZZdJ3VJQHrkOjmsbx4AT83fEGdpNMnOqh3VlO6twxC44fih8RZH0ww/HGMW0Vq1o4qqUOniZKNDuVqVUhuAe4EHOkec9CKc1MUaCvtSSuENmNP7LLsVp82ik7okKGcfYSZFmbeyjGDYJKPRHABh7/xMu4UJg7XPTqJywvBi7FYDX0Dx3uqyeItzQHRGcvY9mGZ9TTuJTuoCZgKXMEXZdkB0UpcE5dRDeyOYZv11u6vjLY4mSVFK8dZX5vr9sOJscjO0OT9RcdosHDusOwDPLS5FqeQb6HdI4YtIEfBLoF12TRG5VkQ2iYhbRFaIyDGttLeLyJ2hYzwiskVEbuiI7IlAdFIXAE9I+VuESHIHndQlMeme4+CI/vkAPD0/edf0NPHl621VbK+sxxC47jhtzk90zj3StOyt3eWiqj75zPpt9dLfhJlNLxo7EHYnPbetFxSR84HHgGuBT0L/zxWREUqp5jzUXgL6AjOAdaHrJn1y+eikLkYo1AtMBxFL1Had1CUxOfuIvny+pZL3VptmfZ0vQdNe3v4mFIJrt3B0SWGcpdG0xtSDe0Qq6L27cifnH51cIbltneF/3MTrTeB3wMFKqTfacc1fArOVUs8opdYopa4HdgLXNNVYRE4EjgdOUUq9r5QqVUp9qpSa345rJiTRlZkq67yELURF2Xb8gWDSV2ZKdU45pCciUOcN8G2ZNutr2o7L7WPuNzt5+bOtAAzrkUN2ksZ2pxMOq4UpIbP+80s2J51Zv62Z9i7vjIuJiB0YDTzYaNd7wIRmDjsL+Az4pYhcCtQDc4HblFJJHxMVTtpy79xvATO+0R9QeCSY9JWZUp2ibAej+xewfPM+nvxoA09edGS8RdIkONF5N+o9/ohZeEdVPS8sKeXicTqvQ6Jzzui+vLt6F9/trqGyzpdUYZTSlSMUEekNbAeOVUotiNr+e+AipdRBTRwzD5gCfADcCeQDjwNfK6XOa6L9DEzTP8XFxaNfeumlTu1DTU0N2dnZnXpOb0Dxsw/q8AVhYC6cNtjBiCILGdbY/fBj0Y94Ec++zN/qY/YqLw4LPH1CJkYHH9b6viQendmPDzb7mFfqw2mBKq+i2msO8ntngTcoTC+xcfyA2M30U+WeQPz64g8qrvmf+bye0tfCwDwL2XY54Gd2LPoxderUFUqpMY23J4Ot2MD0H7hQKVUFICLXAe+KSLFSald0Y6XUTGAmwJgxY9SUKVM6VZj58+fT2eect3InvuDnWA3h7vOPZvzg2K/Zx6If8SKefTms1svzq9/HE4DuQ4/gkL75HTqfvi+JR2f1w+X2ceeKRXTLc2AxhG07zWWgDLuFwvxs/IEgi8qD/OaCSTFbxkuVewLx64tSiqHfLGT1TheLdgb5rsaG3WLw9jbhigkl7bbSdGU/OiMsrz2UAwH2O/uFKQaaC2zcCWwPK/sQa0L/9+9c8eJDuGhGtsPCyD55cZZG0x4KsuwcFXK20kl4NC0RnXej3heI+Oz0yDFNwlaLofNuJAFzlm5mt8tMf+4LKPIybOQ4bTgsBjMXbGTO0s1xlrB5ulThK6W8wApgWqNd04DFzRy2COgtItE2j3Dcf+J+sm2k3hvgw293AzB5WHdyteNO0nFOKFRn/trdBHQSHk0zROfdqIzK1Jbl2P+b13k3EptwdtRuWXbCk/jN5XWAOWDLdlgTOjtqV8/wAR4GLheRq0RkuIg8BvQG/gIgIs+LyPNR7V8E9gKzRGSkiEzEDOv7t1Jqd1cL39nMX7sbty+IzRB+dHRKGCzSjukje2EI1PuCrNpe1foBmrQknHdDKRVx1rMYNPD70Hk3EpuwlcZmtZAbWnbx+AOoUNR6oltpulzhK6VeBm4Efgt8CUzCDLkLz9b7E2WqD3ninwDkYXrr/wszLPDKLhM6hoTTamY5LIzorc35yUhepo2jB2qzvqZlwnk3XG5/JOdGv4L96UR03o3EJ9pKEx6YBRQEorKkJrKVpsMKX0T6iUi7pqZKqaeUUiVKKYdSanS0x36oUM+URu3XKqVOVEplKqX6KKV+ppRydVT2eFPvDfDhmrA5vwd5Gdqcn6yEc+t//J0262uaJpx3Y2+tuf5ryH5zvs67kRxEZ0fNce6/T5VRWfcS2UrTGTP8jaGXpp3MX7sbt1+b81OBk0b2jJj1V+/QZn1N01x4dP+IB7fNYlBZ58Pl9uEJ6LwbyUB0dlQRIS+k9CtqvYBKeCtNZwwl78IMJdW0k7B3vmnOz42zNJqOkJ9pZ0xJIcs2VfD0/A08dfHoeIukSUC+3FaJy+3HELj22IEU52WSn2ln0tBuemafBIStNDMXbCTbYSU/006V24/HH8TtC+D2KWZMHpiw97LDUiml7uwMQdKNem+Aj9aa5vxjD9Lm/FTg7CP6sGxTBR+t3aNz62ua5J1vzOjjLLuFqyYPbuChr0kOwlaYWYtLAYVgJorZV+vl5ycMS2grTTy89DWYa71h7/wfHtU33uJoOoHpI83c+vW+AI+8/x3zVpbhcidfRS1NbAgGVaRYzsg+eVrZJykiwiXjS3j9ZxO59ZQRjAxZZwuy7AmfGrnNM3wRyQd+AYwH+mCmyF0MPKqUqoyFcKnMm1Hm/FG98+MrjKbDKKV46+sdOCwGbn+Qvy3cSHGeE8M4sOxbmtTjy22VlFW5sQj8bOrgeIuj6SA5ThvTR5m+OzNeWEHp3joqar0UZTviLVqztGmGLyKHYZalvRVwAqtD/98GfCcih8RMwhTE7Qvw4bd7AJg0tLs256cAc5ZuZuaCjeRnmvfSHQiS7bQmRfYtTdcwN6oU7pH9C+IsjaazmHKQWTLXF1DMXbkz3uK0SFtN+n/GTH4zVCk1WSn1A6XUZMyMdxWYxWw0beTj7/ZQ7wtgNYQfHd0v3uJoOkg4+1a2w0pBKBxHKXD7gkmRfUsTe5Tab84f3itXm/NTCLvV4NhQydwXl25J6JK5bVX4RwG/i0qOA4BSqhT4A3B0J8uV0rz19f7c+dqcn/xE50i3WgysIWe9PS43kPjZtzSx5+ttVeyodGMIXHfckHiLo+lkzj3S9MNat6eGfXWJ67fTVoW/F/A0s88d2q9pA25fgA9CyXbGD+5GbkZihm9o2k509i0wR/wALrc/knIzkbNvaWLPO9qcn9JMObh7xKz/7qrENeu3VeE/DdwsIs7ojSKSAdwEPNnZgqUaLrePeSvLuPudNdR5w+b8/tqRKwWIzr4FYLeY9zQYMutDYmff0sQW06HTVAIH9cwhWxfISjkcVgvHhJLtzFmyOWHN+s1OL0UkOr5egAHAFhF5B9iFWdL2FKAeyIylkMmMUoo5Szcza3EpwaBiV7Vp5g0qxZqd1RwztJtW+klOdPYtq8WgX2EW3j011HkD7HF56B3y1k/U7Fua2LJyezXbK+tNc/5Ubc5PVc45si//W7Ob73bXUFnnoyAr8Qb4LdmTf9vM9kub2PZ/wO87Lk7qEfbeznZYMWyCx2/O+DLsFuYs3Uym3cIl40viK6SmQzTOvmW1GORn2qjzBqiu95HrtDJj8qCEzb6liS1vR5nzx5QUxlkaTaw47uAe2K0GXn+Q91bt5PyjEy8BT7MmfaWU0Y6XpSuFThaivbetFoPNe2sJ11Xpnu3Q3tspxMXjBjBj8iA8gSAut49gaElfAWcd3juhs29pYodSKrJ+P6RHNjnanJ+yOG0WJg0pAmBOgnrrt7qGLyJ2Efm5iIzqCoFSiWjvbQBvaHZvEXOGr723U4fo7Fu3nDycG08YSkmRudK1s8qtl23SlFU7qtlSUafN+WnC2UeY3vprd7moqk88b/1WFb5SygvcC2hbVDuJ9t4OKoU3VDM5026NhG5p7+3UIpx964Kj+3PZhBIA5q4sS8jRvib2hBOxZNgtjB1UFGdpNLFm2ohibBbBG1B8sHpXvMX5Hm310l8DDIqlIKlItPd2bZTZvjDbTrjAoPbeTl1OOaQXALXeAJvKa+MsjaarifbOH9w9S5vz0wCnzcLEwaZz7vNLE89bv60K//fA73QK3fYR7b0dNu8IpvMOkPC1kzUdozjXySF9zMIaMxdsiLM0mq5m7S4Xm/ea5vyfaXN+2nDWEX0AWLOzOuHM+m1V+L8BsoEvRGS9iCwUkQVRr49jKGPSEvbedrl9kRuf5TDN+f5AkBpPgCsmlGjv7RTmzMPNH//bX2uzfroRLoWbYbMwXpvz04YTRxZjNUyz/sff7Y63OA1oq8IPYBbMWQhsBfyhbeFXsPlD05uLxw3g+OHFEe98u1Vwuf14AkFmTB6ovbdTnFMPNc36NR4/Wyvq4iyNpit5O5RCu6Qok9wMvWyXLmTarYwbZLq8Pb84scz6bZpaKqWmxFiOlEVEcIdj720G1x47mL6FWUwa2k3P7NOAXnkZjOiVy+qd1cxcsJE/nq1XxVIdl9vHK8u3sWFPLQJcOVG7P6UbZx/Rl0/W72Xljmqq6/3kZSaG/0ZbZ/iaA8QfCPLuKtO0N2ZAAVdOGsT0UT21sk8jzjy8NwBvfLUjoUb7ms5FKcULS0o588lF/PmDdZHtj3+0jheWlOp7n0acNKonFsNMtLZwfeKY9dul8EWkQESOFpHJjV+xEjDZWVZaQWWdD4shXHBUPwxDx2OnG2GzvsvtZ/s+bdZPVcJZNR0Wg3pfADALKWXYLMxcsJE5Sze3cgZNqpDtsDK6fz4AD777HfNWluFyx9+Br03TzFDRnL8DPyQcT/Z9dLa9Jnj76/1pNY/WjjtpSd+CTA4qzmHtLhdPzt/AscN6UFnnJT/TzsQhRTpcKwWIzqoZCKpICu0eOQ6sFiOSVfPsI/tq616KE66fEg7FLd1bx5/eXo3NanDFhBIuHjcgbom42vrN+x0wBbgMeAH4GWZZ3MuBXsDPYyBb0hMIKuauNM35o3rlUJTliLNEmnhx5uG9uf/dtbz82VYWb9iLPxDEZjEwDIn7Q0DTcSJZNe0GFXXuyPYcp/mItYZm/Z+sK2f6qJ7xElPTBYQtPQWZdvbUmEnVRMBhMZi5YCNA3OqntNWkfy5wJ/BS6P2nSqlZSqljga+A6bEQLtlZXlpBRa0Xi8CFYwdoc34a449kXASnzaAwy0GO0xZ5CGhzb3ITnVWzqs403RoCFmP/I1Zn1Ux9oi09DpslklF1b423gaUnXvVT2qrw+wOrlFIBwAdkRe37O3B+ZwuWCoSrZGXYrTqtZhrjcvv471c7sFvNn1tpeV1kLT8RHgKajhPOqunxByJROU5bw8erzqqZ+jSunxL+zdd5AwRC2+NZP6WtCn8vZuIdMOPwD4va1w3I6EyhUoFgcH+VrOE9c+iWrc356Ur4IVCQYa7Ve/1Bov214/0Q0HSccFbNfbX7TbgDivbPi3RWzfQg2tIDYLeYM3zF/vTq8bT0tFXhLwWOCP39KnCXiNwqIjcDDwCfxEK4ZObzLfsor/FiCFw0tr8256cx4YdAOBZXYQ4Io9Hm3uQmnFWzMpRR02YxIuZcnVUzfYiunwJgiES+B+U1HiC+lp62fvvuwzTrA/wRGIK5pm/BHAxc0/miJTdvhbJsZdqtjB+szfnpTPgh4LBacFgNPP5gxOwbJvIQ0FF7ScvkYd3xhSpi5jgsVNT6sFkEwxCdVTNNiK6fYrUY9CnIxFnrYUelmzpvAI8vEFdLT1sz7S0Hlof+dgHniogDcCilqmMoX1ISDCre/tr0zh9WnE23HGecJdLEk+iHQF6Gjd0uD/6oGX60uXe5tuonLftz5xvcesoI/IEg+Zl2nVUzjQhbemYu2Ei2w4rVYpCfYWdHpRsFVNR6+fkJQ+P2fTjgqyqlPICnE2VJGb7Yuo89NR4MgYvH9seizflpTeOHwG6Xh0BQ4QsEEIQaT4AZkwdqpZDkhH12inOdnHtkHx1mmaaELTmzFpdS7wvgCyjsVgOvP0huhjWulp5mnzAico5S6rX2nExEegEDlFJLOyxZEvPmV+FkO1YmDtFOOpqGDwGrIfiDih2VbrrlOLS5NwXYWlHHN9urEOCaYwdrZZ/GiAiXjC/hrCP6sGj9XirrvHy5rZKXlm1lR5Ubl8dPbpySbbXktPe4iHwpIj8VkcKWTiIix4jITGA9cGinSphkRHvnD+2hzfkak/BD4PWfTYwkXlEK3rhuEpeML9EKIskJ/+adNgsnjCiOszSaRCDHaWP6qJ5ccHR/bpl+MIaA2xdk+aaKuMnUksIfCryG6Zy3S0S+FpEXRORhEblHRP4iIu+JSAUwP9R+mlJqZuzFTly+2FrJbpdpzr9wbD9tztc0IMdp48YThgFQ4/Xj9unY+1QgnEK7R66dwiwda69pSH6mnUP75gMwe1Fp3ORoVuErpeqUUncCfYGLMZ32RgNXAr8ATsf00n8MGKmUmqqUWhx7kROb/d75Fo4Z2j3O0mgSkSE9shlQlIlSMOuT0niLo+kg2/bV8bU252ta4axQ1cxlmyviVkin1Th8pZRXKfWyUupKpdQIpVS+UsqplOqjlDpeKXWHUurbrhA20Yk25w/pnk13bc7XNMNZh/cB4OXlW+MsiaajzA155zttFk4Yrs35mqY54/A+CKZZf8XmfXGRoV3lcTUt8+W2SnZVh8z547R3vqZ5Tj/MHO3vrfVSXuNupbUmkXnrG9Oq1y3bTpHOqKlphsIsO4f0zQPg759siosMWuF3Ei63jyc/XA+YWbaO6FsQZ4k0icyQHtmUaLN+0rO9sp6vtprm/J9qc76mFc4+wrTsLSutoDoOZv24KHwRuVZENomIW0RWiMgxbTxukoj4RWRlrGVsK0opXlhSyhlPfML87/ZEts2Ys4IXlpSilGrlDJp0JWLW/0yb9ZOVd77e751/0khtzte0zFnRZv3SrvfW73KFLyLnYzr63Y2Zn38xMFdE+rdyXAHwPPBBzIVsB+HaxyoIgVD2tO45DpxWXfZU0zKnh5x49tZ52ePSZv1k5M2vtTlf03YKsuwc1i9+Zv14zPB/CcxWSj2jlFqjlLoe2Enr+fifBZ4DlsRawLYSXfvYFVXaNMdp02VPNa0yuHs2A7uZZv14relpDpytFXV8vc005187RZvzNW3j7CP6AvBZ6T7+88U2Pt7qY97Ksi7x3O9ShS8idszQvvca7XoPmNDCcdcCxZiFexKGcNlTiyFUhapkWYRIdSRd9lTTGmGz/r+Wb4uzJJr28nZUsp1pOtmOpo2ccVgv06zvD3LP29/yxgYf985dw5lPLor5MrB05RqziPQGtgPHKqUWRG3/PXCRUuqgJo45BPgfME4ptUlEbgfOU0qNauYaM4AZAMXFxaNfeumlTu1DTU0N2dnZAHy81ccbG3zYDCirMz9HuwG9s/ePo1xexRmDbRzbLz6pFJsjuh/JTjL3paw2yC0L6xHg4SlObP76pO1LY5L5vkTTuB/1fsXqvQFeXONlr1vR3Qn3H5uZFDP8VLknkLx9+WCzj3+u9eIPgtMC3TMUFsNCIKhwB2B6iY3jB3RMX0ydOnWFUmpM4+0t5dIPYpbubgtKKdXplT9CFfleBm5SSrXJ5hnK9DcTYMyYMWrKlCmdKtP8+fMJn9O9soyPytbgcvsBs5Z5hsNGTk7m/gPcPsYeMZwpoXSqiUJ0P5KdZO/LzDXz2Vhey1rVm3HZu5K6L9Ek+30JE+6HUoo5Szcza0UpHp+w1x16PFrtbHOa9RASXemnyj2B5OyLy+3jzhWLKMoSdrk8uANQ4REG9cgBzMqZi8qD/OaCSTEpptXSGe+k7Qq/rZQDAUzzfDTFQFkT7XsBw4FZIjIrtM0ARET8wClKqcbLA13GxCFFiEBlyJyfabPQryAjsj+67KlG0xxnHt6HR/73Hf9avo1xxySWJUizn7CDbrbDSm3IL0cE8jNtzFywEYBLxpfEUUJNohNeBi7KdrDLZRabDQT377daDOp9AT5ZVx6pudGZNKvwlVK3d/bFlFJeEVkBTANeido1DXi1iUO2A4c02nZtqP3ZQGlny9gecpw2jjuoB8+GciMXZNkiI3x/IKjLnmraxBmH9+aR/33Hvlov+9w6NUYiEu2ga7UYlIce1jaLgdNmxWoEmbW4lLOP7Kt/75pmqazz4gsEsRhCpt1CnTeAN9iwjS+gqKzzxuT68Xi6PAxcLiJXichwEXkM6A38BUBEnheR5wGUUj6l1MroF7Ab8ITe18RB/ga4/ebdEkAQKmq9uNw+PIGgLnuqaRMDu2UxuHsWCnh3U3xybGtaJjwzs1oMPP4AgZDts0e2WShHO+hq2kJ+ph2bxVS7+RmmNS+o9od0A9gsQn5mbAowtXkoGvKwPxk4CGicJF4ppe5qy3mUUi+LSBHwW0yT/UpM03w4YL3FePxEIhBUzA156o7un8+Vxwyiut5HfqadSUO76ZG+ps2cfWQfHnz3Oz7ZEUAplfBrwelGeGYGRCJyAHIz9i/BxHJmpkkNJg4pwjAEfyBIfqadHVVm/o06r58cpy3my8Bt0kgh7/pPgBLMdf3w0yh6jb9NCh9AKfUU8FQz+6a0cuztwO1tvVYs+XTTXirqfFgELho/gFMO6RVvkTRJypmHmQq/xgcvLt2MxWKQn2ln4pAicpx6XT/eRM/MqupMhW8IWIz9RtJYzsw0qUGO08YVE0oiviBhs/6+Oh8ZNkvMl4HbetYHgD3AZGALMDb0/krgfODEmEiX4LzxZagUrsPKxMHaMU9z4PQtyKA418Guag/3zFtLXoYVm8XAMIQrJpQkhQd4KhOemdV6fJFlvD75+w2d2kFX01bCy7yzFpeSYTPMdXx/oEuWgduq8I8BbgJ2hN4HlVKlwO9FxAL8GTiz88VLXPyBIHNXmoEFo/rk6rSamg4xZ+lmPD5TkdR5/QzslgmYpj/tAR5/wjOzB95dC5je+WHLi3bQ1bQHEeGS8SWcdUQfFq3fy5wPv+TYI4byo6MHxPz701anvSJgh1IqCNQC0aXgPgSmdLJcCc+SjXupqvdhMYQfHaVL4WoOnLAHeLeQA1hQwea9dQA6RXMCcdHY/hGzvlWEqnq/dtDVHDA5ThvTR/Xk3GF2fjC6X5cMFtt6hW1A2Fa1AdOE/7/Q+6OBtKv88Z8vtgOQZbcwblBRnKXRJDNhD/Aspw27Ad4guH2ByP5Yx+Zq2sbqnS721noxBK6cVMLAbtnaQVfTYQqcRpf5frT1W/oRcCzwX+CvwJMicjjgA04KbUtpXG4fi9bv5dOtPlxf7WBeyJx/ZP98umlzvqYDRHuAZ9uFCrfCG1AoFBLyj9Ue4PHnza9CPjt2C9cdN1Q7U2qSjrYq/N8ChQBKqadFxIrprJcJ3I+ZlS8liaTTXFxKMKhw1fp4a8sq6rwBDIHzj+qHoc35mg4Q7QGeZYVwlWy3L0iGzQJoD/B4Ewwq3ggp/IN75mhlr0lK2qTwlVLlmGlxw+8fBx6PlVCJRHQ6TavdAJ+bilBqJAWUhtZaNZoDJTo218zAZXru7nG56V+YpT3AE4DPt+xjZ5Ubi8DPTxgab3E0mgNC5/FsgcbpNLfvq2NPXTBULAey7RZeWbFNO1NpOkTYA7zG4ycQVBRkmrPHarcfXyBAjSfAFRNK9DpxHImY8x1WRvcvjLM0Gs2B0Z5Me8cCP8LMhNdUpr3jO1OwRCCSTtO+f1zkV/szD3XLdkTSaWpnKk1HCHt4P/m/1dhC8fZKgcsd4KfHDtIe4HEkEFS8+fX+jJqZeuClSVLammnvauBpzOXF7wBP4yadLFdCEO1MFSaUcwNDhAy7hap6v3am0nSYcGxut9pNSM/hPPTeWtbtrmFI9ywdfx9n1lQEqaj1YjGE66YOibc4Gs0B09ah6q+AF4ErlVJpo92inakAgkpFimbkZlixGKKdqTSdSoZVmDKqJ15/gBte+pKlmyrwB4JYLXr1LV58unP/Et6ovvnxFUaj6QBtfYr0AWalk7KHhs5UQMSUJ0BBlh1/QGlnKk1MmDaiJ3argccfZMF3ugJbvPD4AyzfZSr8Y4Z2xxmKmtBokpG2KvwVwKBYCpKIRDtT+QNBKiNFMwSriHam0sSMDLuFacN7APD4h+viLE368vHaPdT7wWYI10wdHG9xNJoO0VaFfwNwo4hMjqUwicjF4wYwY/Igar1+6rxm9rMsu4FfKZ1OUxNTfniUWSn6m+1V1Ht1JEg8CGfUzHZaGVacE2dpNJqO0dap6ZtALvCRiNQB+xrtV0qplNR8YWeqqnofD773HRaBnx03lIvGxb7QgSa9mTi4iBynFZfbz+tf7uCCo/vHW6S0oqrexwdrdgNw5mG9G/jzaDTJSFs11geY0Whpy8mH9GLpxr2s3rqXS8YPINOulb0mtlgtBmcc1pt/fLqFvy7YoBV+FxFOo/3+6jK8gSBWgcsmlMRbLI2mw7Q1097lMZYj4RncPZt7zjmUV99fpJW9psv4wZh+/OPTLWzeW0d1vZfcDB0REisap9HeUWXWBAsoWLCunJJuWYikZASyJk3QNqp20CPXwUFFWtlruo7D+ubRM9dJUMHsxaXxFielCafRdlgMHFYL3lDSjVw7/G3hRuYs3RxnCTWajtHWxDuXtrA7CFQBXyiltnWKVAmKw2ohw6pH+JquQ0Q4d3QfnvxoAy8s2cwNxw+Lt0gpSeM02ut3uwAQgRy7kO2wMmtxKWcf2Vf77miSlrZ+c2ezfw0/WuNFbwuKyMvAFekWr6/RxJLzRvfjyY82UF7jZUdlPb3zM+ItUsoRnUZbKYUnNLu3GQZWw/SnqPcFdBptTVLTVoU/EfgHprf+v4FdQDHwQ+A04FpgJGaZ3M3AbZ0uqUaTpgzslsWw4hy+2+Xisf99x9SDi6ms85KfaWfikCJdqrUTiE6j7fYHCYamMsW5DgiYmcR9AaXTaGuSmrYq/JuAl5RS0Yr8O2ChiLiAGUqps0UkD7gIrfA1mk7lh2P68Me3v+Vfy7dF0u3aLAaGIVwxoYSLxw3QDmUdIDqNdrRSz3Faqas1Fb5Oo61JdtrqtHciZmheU3wIhCvlLcBMw6vRaDoRf6iIgwKsAoVZDnKcNhwWg5kLtENZRwmn0fb5A5GMmpl2CxbDfET6A0GdRluT9LRV4XuA0c3sGw2Eh8QGUNtRoTQazX5cbh//WrGNLLuZx31zRT3b99UB5tpy2KGsxqOz8R0o4TTae2u9+EP2/OIcB2CWx9VptDWpQFsV/ivAHSLyKxEZICIZof9vAm4HXg61OxxY2/liajTpS9ihrCjbNCf7g4qg2p8Hy2oxCAYVn6zTRXY6wsXjBtAr5BApgCcQxOX24VPoNNqalKCtw9VfAjnA/aFXNC9ils8FWAks6RzRNBoN7Hcoy8+0Y0g9QQXeQMPEl9qhrOO4PH7WlpnheOMGFXLm4X3Iz7SjytZw8viS+Aqn0XQCbc20Vw9cLCJ3AmOBXsBOYJlSam1Uu7djIqVGk8aEHcoMEfIzbFTU+XD7Ag3aaIeyjvPWVzvx+IPYrQZ3njmKoaFiOfPLv42zZBpN59CuBSml1HeY3vkajaaLCDuU+QNBCrMcVNT5CCrwBwJYLRbtUNZJ/Gv5VgDyM6wM6p4dZ2k0ms6n2TV8EekvIraov1t8dZ3IGk16EXYoq/H4sRpgt5jhd7tdXvyBoHYo6wTW73bx5dZKBPjZ1CFYDB3iqEk9WnpCbALGA8uAUlqvlmfpJJk0Gk0jwg5jsxaXkmW34q33UVHrJS/Dph3KOoFXVphZwTPsFk4/TEcWa1KTlhT+lcCGqL/TujyuRhNPRIRLxpdw1hF9mPtNGb959WsUcM85oxg7SJvyO4I/EOS1z7cDcHDPHAqztC+EJjVpVuErpZ6L+nt2l0ij0WhaJMdp44dH9eO/X25n8Ya9PDV/g1b4HWThunL2uDxYDOHXJ+niRJrU5YDK44pInoiMEZG+nS2QRqNpnYvGmm4zizfsxe3TCXc6QthZL8dp5fD+hXGWRqOJHS057Z0kIvc2sf02YDfwKbBZRF4UEe0tpNF0IdNG9CTHYcUXUBGFpWk/+2q9/G/NLgDOOKw3Tpt2RdKkLi3N8H8KNLBvicg04I/At8CNwF+B84Gfx0g+jUbTBHarwQ/GmAa2pz7agFLaxeZA+O+X2/EFFA6rwY8nDYy3OBpNTGlJ4R8BNE6kcwXgBk5SSj2ulLoWU+lfGCP5NBpNM1wayv62q9rDhj018RUmCVFK8Y9PtwBQlG2nX0FmnCXSaGJLSwq/B/u99MNMAz5RSpVFbXubRpYAjUYTe0q6ZXF4v3wU8MC7uoRFe1m+eR/rd9dgCNx84jAMHXuvSXFaUvguICv8RkSGAkXA0kbtqtEx+BpNXLhiYgkAH327B0+jdLualgmXFM52WDlhRM84S6PRxJ6WFP63wJlR78/EjMV/r1G7gcCu9lxURK4VkU0i4haRFSJyTAttzxGR90Rkj4i4RORTETmjPdfTaFKV6aN6kmW34A0E+e8X2+MtTlLgcvv494ptvPX1TgCOHdqdHKctzlJpNLGnJYX/CHCViPxbRJ4E7gC+ARY1ancK8FVbLygi5wOPAXdj+gksBua2kJ73WOBD4NRQ+3eA/7Q0SNBo0gWH1cK5o03nvcc+XBdnaRIbpRQvLCnlzCcX8ae3VxMI1b3/clslLywp1Y6PmpSnWYWvlPovpif+UcClmKb8H6ioX4WI9AROwFTCbeWXwGyl1DNKqTVKqesxK+9d04wcP1dK3auUWqaUWq+UugNYAZzVjmtqNCnLZRNKANhZ6WZzuXbea445Szczc8FG7IZQ6zGXP2wWIcthYeaCjRETv0aTqrSYeEcp9Wel1AClVI5S6nil1LpG+8uUUt2UUjPbcjERsQOj+f6ywHvAhHbInQPsa0d7jSZlGdw9m0P65JrOe+/pYpZN4XL7mLW4lGyHFU9A4Q0EAeiZ68RmsZDtsDJrcSk1Hp3ESJO6SFeasUSkN7AdOFYptSBq+++Bi5RSB7XhHD8D7gVGKaW+NyQXkRnADIDi4uLRL730UmeJD0BNTQ3Z2clfOjNV+gG6LwCLtvt45hsvVoGnTsjAbjmgJJqdSiLdlxW7/Lz8rZdMm7C7LkhdSK/3zxEMMb3z63yK8w+2M7q4YR6xROpHR9F9STxi0Y+pU6euUEqNabw9qTLkici5wAPA+U0pe4CQtWEmwJgxY9SUKVM6VYb58+fT2eeMB6nSD9B9ARjvD/DP7/5HjcfPdmcJV0wc1PnCtZNEui9ly7Zg27SODKeNumoXYJYZzsvNjbTx1XrpM3AIU45u6E6USP3oKLoviUdX9qOrpwHlQAAobrS9GCj7fvP9iMh5wAvApUqpN2MjnkaTnDisFi48uh8AT3y4nmBQO6BFk59px2YxqKjzRrZl2BvOd2wWIT9TV8rTpC5dqvCVUl5Mh7tpjXZNw/TWbxIR+SGmsr9cKfXv2Emo0SQvV04ahAjsrfVx77w1zFtZhsvti7dYCcHEIUXmZ1NjKny7xaBfYUZkvz8QxDCESUN15UFN6hIPk/7DwAsisgwzxO+nQG/gLwAi8jyAUurS0PsLMJX9TcCCUGQAgFcpVdHFsms0CYlSivdXl5Fhs1DnDTBrUSnvrdqFYQhXTCjh4nEDEEnfTHI5ThtHlRSyqXwbAL3yHAjm5+EPBKnxBJgxeSDZjqRa5dRo2kWXf7uVUi+LSBHwW6AXsBI4JWpNvnE8/k8x5Xw09ArzMTAllrJqNMlCOOSse7adzRX1+AIKp81AEGYu2AjAJaHc++nKd7vMtXtDzAxiFbVebBbBMIQZkwdy8bgB8RVQo4kxcRnOKqWeAp5qZt+Ult5rNJqGRIecWQzBkHqCCsqq3PQrzIqEnJ19ZN+0ncF+vmUfX26tQgR+ecIwhhTnUFnnJT/TzqSh3dL2c9GkF/pbrtEkOYvW7yUYVFjtpkuOM2TWr6r30ycYxGoxqPcF+GRdOdNHpWfO+FmfbAIg227l4nEDyM/Sznma9CP+wboajaZDVNZ58YUSyQA4LObatAIq6kynPV9AURnloZ5O7Kyq551vzLz500f11Mpek7Zoha/RJDnhkLMwIoLDar7fXe1GodI65Oz5JZsJKMiwWbhu6pB4i6PRxA2t8DWaJGfikCIMQ/CHZvl9CjIZ2M2sbB1QpgUgXUPO6r0B/hHKkT+sOIv+RZlxlkijiR9a4Ws0SU6O08YVE0qo8fgjSt9mMchzmi46O6s8XD6hJC0d0177YhvVbj82i/CH00emdWiiRpN+TwCNJgUJh5TNWlxKvS+AL6DIclqpcvsJBBWjeue2cobUweX2sWj9XvbWeHjkf2YxoYJMO4f0zY+vYBpNnNEKX6NJAUSES8aXcNYRfVi0fm8k5Oy5JZtYsqGC372+irdvOCbeYsYUpRRzlm5m1uJSgkFFVb2PfSGnxWOHdcNq6Nm9Jr3RJn2NJoXIcdqYPqonFxzdn+mjevKbkw4GYPWOataHEs+kKuHkQw6LQbbDSp3XrHlvEViysULXu9ekPVrhazQpzOH9Czisbx4K+P3rq+ItTsyITj5ktRhUu/14/KY/Q3Gekxxd716j0Qpfo0l1fnXiMACWbtpLWXV9nKWJDZHkQxYDpRTb99UB5uw+P8OO1WIQDCo+WVceZ0k1mvih1/A1mhTnmKHdGdQ9i417avntf1Zy3uh+kTX+iUOKyHHa4i1ih4lOPlTj8RMIVQfukevAElq7T+fkQxoNaIWv0aQ8IsIvjh/K9S99yf/W7ObbsmqUMkP3UqWaXjj5kFKK3dUeAATTOz9MOicf0mhAm/Q1mrSgos4bmelW1/spzHKQ47ThsBjMXLAx6R3awsmHqut91PlMZ73uOQ4shvmI0/XuNRqt8DWalMfl9vHcks30zHUAUO32s2VvLQDWkEd7sju05ThtXD5+AGUuc3ZvESjKNmfz4Xr3V6Rp8iGNJoxW+BpNihN2aCvItEdy7NeGQtaAlHFo61OQiTfkmZ/lsFJd78fl9uEJBHW9e40GvYav0aQ8YYc2EaF3npNNe+vwBxX1Pj8ZNvMRkOwObf5AkLvf+RaAvAwrvz11BIGg0vXuNZoo9K9Ao0lxoqvpZTttZNgs1PsCbNtXz9AeOUDyO7S9+vk2NuypwRD4+XFD+MGYfvEWSaNJOLRJX6NJcRpX0+ud5wTA7QtS6/UlvUNbvTfAg++ZOfOLsuz84Kj+cZZIo0lMtMLXaFKcxtX0Mh1Wsh0WALZW1FPj8Se1Q9vfF21ij8uD1RD+cMaIlMgroNHEguT8hWs0mnbRuJpept1CjcesqjduUGFSObSFq+FVhkINn/xoPQB98p2cMLxnnKXTaBIXrfA1mjSgqWp6//liO59uqmDuyjLuOGMUmQk+w29cDc8XCFLt9lPnDWAx4N5zDsFps8RbTI0mYdEmfY0mjYiupvfMpaMjM/0730r8wjrR1fBynDYybFZcbjN3gMUQ1u6qibOEGk1ioxW+RpOm5GbY+d2pwwH41/JtrEvg8rmNq+EppdhWaRbIMQT65Gfy/NLNSZ08SKOJNVrhazRpzAVH9+egnjkEFVz7j88JBlW8RWqS6Gp4ABv21OD2mVEHhVl2shyWlEgepNHEEq3wNZo0RkR49PzDEWDd7hpeWFrKvJVlvLRsC/NWluFy++ItItCwGp7XH6Q+pOythtAjx4EgSZ88SKOJNYntpaPRaGLO8F65XHBUP/752VZuf2M1fQucBBOsml50NbztIVO+CPQryIgUyEn25EEaTazRM3yNRsPgHtmIgAIqan0UZNkTqppeOHlQRa2XGo9ZByDXaSUrFFmQ7MmDNJquQCt8jSbNcbl9vLhsC31DGfhqvQHKXaZpPFGq6eU4bZxzRB/Kqt0AWETolZeBiOhqeBpNG9EKX6NJc8IOcflZDgoyzSx1ZdVu3D5TwSdCNT1fIMj/1uwi7FOYn2mG5OlqeBpN29HDYY0mzYl2iOuT76Sq3kdQwabyOoYVZ2MxjLg7xN3zzhq+3FqFIXDiiGJOO6w3NW6/roan0bQD/SvRaNKc6Gp6IgZDemSzblcN/qBie6Wb/oUZXeoQF506Nz/TTp3Xz98XlQLQryCTP541im45zi6RRaNJJbTC12jSnOhqelaLgcNqoXe+k+2Vbqrqfex2GWQ5rDF3iGsqdS7Ajkpz3T4vw8rjFx6hlb1Gc4DoNXyNJs1pXE0PoDDLQa7TnA/sqvZw1ICCmJvNG6fOzcuwU17jJZwK6LiDenBIn7yYyqDRpDJa4Ws0Gi4eN4AZkwfhCQRxuX1U1HrJz7BhD5n6//35dhav3xOz6zdOnRsIKjaW1+DxmwOQ/AwrX26rotYbiJkMGk2qo036Go2myWp6+Zl2DuuXxw//soSt++q5YvZy/nvtBIb37vxZdiR1rt1U9mt3uQiEXPKz7BZ652dQ5w3wybpypo/SJXA1mgNBK3yNRhMhXE0vmteuncD0Rxeyt9bLD/66hF+eMJRMh438TDsThxSR47R1+LrhSIFAULGpvDai7DPtFvoVZoYiBfw6da5G0wG0wtdoNC3SPcfJv68Zz/RHF1LjCXDX29+Sn2kjL8PWaal38zPtGCKU7q2l3mea7TNsBv0LMyMRBDp1rkbTMbTC12g0rfLJunLyM23scXkIKthX58MiQrccOzMXbATgkvElwP6wuk+3+nCvLPueFaBx2N3EIUUYotheWR9JrJNhMxhQlBVR9jp1rkbTceKi8EXkWuBmoBewCrhRKbWwhfbHAg8DI4EdwP1Kqb90hawaTboTdqgrzLRTkGmjtLwOtz9Iea2Xel+A3vlOZi0u5awj+vDfL7ZHwupctT4+KlsTsQJcNLY///h0S4OwO6shVNb7qarfX5Wv8cw+nDp3xuSBOsGORtMBuvzXIyLnA48B1wKfhP6fKyIjlFJbmmg/EHgH+DtwMTAJeEpE9iilXu06yTWa9CTaoQ4gw24hqBTegKLWG2D97lqyHRZ+//o3LC+tND3t7Qb43OQ4bfgDQWYu2Minm/by1dYqsh1WLDah2u2jrNoT8cR3WIWjBhSwdZ8bj99U8jaLYBiiU+dqNJ1APIbLvwRmK6WeCb2/XkSmA9cAtzbR/qfADqXU9aH3a0RkLHAToBW+RhNjolPvAgiQ5bBSaDXYVe1BAS5PgP98sZP8DJuppKOW860WgwybhXdX7aJ/YSZ7a71U1Hrxh+33gNWA/Aw7j/7oSBxWo4HJX6fO1Wg6hy79FYmIHRgNPNho13vAhGYOGx/aH827wGUiYlNK+Zo4RqPRdBLRqXcB+hRkRv7OcVrZ5fJQXW8W2qms91EZMs8LkOWpRaHw+IL4g4oNe2obnNsQs8xt9xwn3kCQ5aX7mD6qpw6902higCilWm/VWRcT6Q1sB45VSi2I2v574CKl1EFNHPMdMEcpdWfUtsnAx0BvpdTORu1nADMAiouLR7/00kud2oeamhqys7M79ZzxIFX6Abovsaber7hjST02AYvxfU98f1Cxz63wBsEfhLY+UawC3TIFu2FaBFxexRmDbRzbr+Nhfp1JIt6TA0X3JfGIRT+mTp26Qik1pvH2lLOTKaVmAjMBxowZo6ZMmdKp558/fz6dfc54kCr9AN2XrqA8q5SZCzaSEcqEFybsUHfG8CIWb9iLw2pQ7w9Q5w1QW+8liKAwZ/vegCLbbiEnw0aO04rdYjQM5XP7GHvEcKYk2Ow+Ue/JgaD7knh0ZT+6WuGXAwGguNH2YqCsmWPKmmnvD51Po9HEmLDD3KzFpdT7AvgCqoFD3ZmH9+aspxZjMYT8DDv5GYoq8ZOZlYUCfP4AW/bV0zvficP2/ceODrvTaGJPlyp8pZRXRFYA04BXonZNo3kHvCXA2Y22TQOW6/V7jaZraC71brRD3RUTSpi5YGMkH74hYLMY+ANBfAGYPrKYr7ZWYTGCTVoJdNidRhNb4vHrehh4QUSWAYswvfB7A38BEJHnAZRSl4ba/wW4TkQeBf4KTAQuB37UpVJrNJomU++GaWwFcHkVuH0RK0B0HH5TVgIddqfRxJYuV/hKqZdFpAj4LWbinZXAKUqpzaEm/Ru13yQipwCPYIbu7QBu0DH4Gk1i0dgK8OkX3zD2iOENrACtWQk0Gk3siMuvTCn1FPBUM/umNLHtY+DIGIul0Wg6gbAVwFn+bZMOeC1ZCTQaTewwWm+i0Wg0Go0m2dEKX6PRaDSaNEArfI1Go9Fo0gCt8DUajUajSQO0wtdoNBqNJg3QCl+j0Wg0mjRAK3yNRqPRaNKALq2W19WIyB5gc6sN20c3UiOHf6r0A3RfEpVU6Uuq9AN0XxKRWPRjgFKqe+ONKa3wY4GILG+q7GCykSr9AN2XRCVV+pIq/QDdl0SkK/uhTfoajUaj0aQBWuFrNBqNRpMGaIXffmbGW4BOIlX6AboviUqq9CVV+gG6L4lIl/VDr+FrNBqNRpMG6Bm+RqPRaDRpgFb4Go1Go9GkAWmr8EXEISKPi0i5iNSKyBsi0reVYyaH2m0XESUilzfRRkTkdhHZISL1IjJfREY2alMgIi+ISFXo9YKI5HdlX0LHXSsim0TELSIrROSYqH0loT429bo5qt38Jva/lEh9aaucSXJfCkPn/Db0/doqIk+LSFGjc5Q20d972yF7i59nE+2PDbVzi8hGEflpe895oJ9XV/ZDRG4Vkc9EpFpE9ojImyIyqlGb2U189ks70o8Y9eX2JuQsa9RGpJXnWYL0panvuxKRt9vT31j2Q0R6iciLod9uQERmN9PuXBFZLSKe0P9nN9p/4PdEKZWWL+BpYAcwDTgSmA98CVhaOOYU4G7gPKAOuLyJNr8BXMC5wCjgX6Hr5ES1mQusAsaHXquAN7u4L+cDPuAnwHDgcaAG6B/abwF6NnpdAwSBgVHnmQ/8vVG7vETqS1vlTJL7Mgp4DTgDGAIcG5LzvUbnKQXuaNTf7DbK3ern2aj9QKA21G546DgfcG4771G7P6849ONd4IrQfTgE+A9QBhRGtZkNvN/osy88kD7EuC+3A982krN7o/O0+jxLkL50b9SPIzCfVZe1p78x7kcJ8GfgcmAxMLuJNuMBP/B/oXP+X+j92M64Jwf8BUzmF5AHeIGLorb1C31BTmrjOWpopPABAXYC/xe1LSN0c64OvR8OKGBiVJtJoW0HdVVfgE+BZxptWwfc08Ix7/N9xTIfeCKe96UtfWlNziS/L6eEzpsbta0UuOkA70O7ZADuA9Y12vY3YElbz9kZv8mu6EcTx2QDAeD0qG2zgbcOROYuvie3AytbuGarz7NE6UsTx/wfUAlktLW/se5Ho3Zv0bTCfxl4v9G2/wH/7Ix7kq4m/dGADXgvvEEptRVYA0zowHkHYo4ao89bDyyIOu94zMHC4qjjFmGOYA/k2u3ui4jYQ8e912jXey0cMwg4nqZDSC4ImWFXiciDIpLT7l6YxLovLcmZlPclRC7gwbQ6RXOTiOwVkS9F5P9C52+RA5RhfBPt3wXGiIitjefs1N9kLPrRzDE5mEuj+xptnyQiu0XkOxF5RkR6tF36hsS4L4NCpuFNIvJS6Hcepi3Ps3bRFfdFRAT4MTAnJG80LfW3zXTgt9oazfU1fM4O3ZN0Vfg9MUfljfMX7wrt68h5w+dp7rw9gT0qNDQDCP29+wCvfSB96YZpsm9JzsZcBewBXm+0/UXgImAqcBemmenVtgjeBLHsS2tyJuV9EdPH4C7MmYY/atefgR9h9vcJ4BfAU22Q+0C+Gz2baW8Nna8t5+zs32Qs+tEUj2EuOyyJ2jYPuBRzgPwr4GjgQxFxtFH2xsSqL59impenY5qlewKLZb8/SFueZ+2lK+7LNEzF+Eyj7a31tz0cSD/aQnN9jf6d0EqbZrF2QLCEQ0T+iGnKaYmpXSFLR0mkvoiIFXPd8jmllC96n1Iqesb/jYhsBD4VkSOVUp+Hjo97X9oiZ1tIhL5EyZINvAlsB34dvU8p9XDU269FpBp4WUR+o5Ta2xXypToi8jDmss8kpVQgvF0pFe0M+o2IrMAs4nUqpv9FQqCUmhv9XkzHwo3AZcDDTR6UHPwE+Ewp9VX0xhTub5tJKYUPPArMaaXNFmAc5uisG+asNUwxsLAD1w97fBaHrhN93rKoNt1FRMKzyZAJqkdUG4htX8oxZ1PFjbZHyxnN6Zijx7+1Ig/A8tC5hwJhRfooidOX5uRMqvsSUvbvhN6eppRytyLTp6H/hwAtKfwD+TzLmmnvD51P2nDOMjr3NxmLfkQQkUeAC4CpSqmNLQmilNohItswv2sHQkz7EiVnjYisipKzLc+z9hLr+9IDOBP4WWuCNNHf9nCgz53WaK6v0b+T8LZ235OUMukrpcqVUt+28qoDVmB6V04LHytm+M9wGq7htpdNmB969HmdwDFR512C6egzPuq48UBW9LVj2RellDd03LRGu6Y1c8xPgI+VUt+1+gmYnssWTMeSROxLc3ImzX0J+R7MC8l/ilKqpoV+hjk89P/Olhod4Oe5pJn2y5VSvjaes1N/k7HoR5Rcj2EulxynlPq2NVlEpBvQh1Y+++aIZV8ayekEDo6Ssy3Ps3bRBX25HNOf5Z+tydJEf9tMB547rdFcX8Pn7Ng9ac2rL1VfmCFA24ATMEM4PqJRCBBmCMd1Ue+zMR+ch2M6SP0+9Hd0aNFvgCrgHMyQiZdoOizvG/aHf31Dx8O/2tuX8zG9oq/CfKg+hum0NqDRuftjjmQvauK6g0OfwRjMkJNTMJ2sPufAQ6k6vS9tlTMZ7gumk9gSzFC8oTQMMbKH2ozHXLM/HHMt84eYZv/X2yh3azI8Dzwf1T4cNvVoqP1VoeMbh+W1+H1ry+fVzs8/Fv14EqgGjmv02WdHPSMeDN2DEmBK6H5to+OhbJ3dlwcxwzoHAmMxPcerG92TVp9nidCXUDsBvqOR53x7+hvLfoS2HR56LQDeCP09Imr/BEzLxS2Yg5FbMQfCjcPyDuieHNANS4UX4MCMm9yLqbzfBPo1aqOA26PeTwlta/ya3ehLdzvmqNENfAyManTeAkyzcHXoNQfI78q+hLZdixm+5cEcrU5u4tx3ABWAs4l9/UL92xs6x/rQl/6AY45j0Ze2ypkM96WF76ACpoTaHAksxQxLqsccVNwOZLZD9pZkmA/Mb9T+WMwBlAdzFvLT9pyzrZ/XAdyDTu1HC5/97aH9GZhe1bsxlcFmzDC9DvUjRn0JKwov5oDwVaKUT6hNq8+zROhLqM3U0L04uplrttrfLuhHU9+d0kZtzsP8zXoxJybndNY90cVzNBqNRqNJA1JqDV+j0Wg0Gk3TaIWv0Wg0Gk0aoBW+RqPRaDRpgFb4Go1Go9GkAVrhazQajUaTBmiFr9FoNBpNGqAVvkaT5ISqsalQutd4yTBbREqj3peEZLo8atvlInJlDK59eehaJZ19bo0mldAKX6NJYkQkAzOTHsCFoUJHicBOzGxzb0dtuxzodIWv0Wjahlb4Gk1ycxaQi1lIpwdm6c+4o5TyKKWWKqX2tN5ao9F0BVrhazTJzWXAPszZc33ofQQRuT1k7j5YRN4VkVoR2SIiV4T2XyIi34pIjYh8JCKDGx1fKiJzROQnIrJeRNwi8rmItFgCuLFJX0TmY6ZHnRjarkLbIjI2cY4GywShbYNE5G0RqRORPaFCNk3WmReRGSLyVUjmchF5VkQKW5Jbo0llEsX8p9Fo2omI9MYsNPOMUmqPiPwXOEdECpRS+xo1fwV4BrOAyLXA30VkKGZu/lsAG2Z9gRcxC4tEMwUYDfwfZs7w3wBzReQwpdTaNop7LWZtAgtwdWhbdRuPBUBE7MD7mPnqf4aZr/5qzCIijdveC/wK+DNwM2a1uj8Co0RkgoqqX6/RpAta4Ws0ycvFmAr0+dD75zDLtp4P/KVR2weUUs8DiMhy4HRMZTlQKVUd2t4LeExEBiilNkcd2wMYr5TaGmr3AWZRmN8Cl7RFUKXUahGpBqxKqaXt7qnJZcCgkCxLQ7KEKxxGCDnv3QzcoZS6M2r7d8AnmH3/7wHKoNEkLdqkr9EkL5cB65RSS0Lv/4dZDeyyJtrODf8Rmv3vBpaGlX2IcG33fo2OXRpW9qHjXZjOeOM7Jn67GQ9sjR4wKKWCwL8atZuG+Wz7h4hYwy/gU8AFTO4qgTWaREIrfI0mCRGRMcAI4DURyReRfCAHeA0YJyLDGh3S2MTvbWYbgLPR9l1NiLAL00zelfRqQZZoeoT+X49ZSzz6lQMUxUpAjSaR0SZ9jSY5Cc/ifxN6NeZSTJN7Z1DczLbtnXR+N5hr9Eopb9T2xop5JzCyGVmi2Rv6/0S+P6iJ3q/RpBVa4Ws0SUbIee1HmCbqW5po8ghwiYj8rpMuOU5E+kWt4ecAp9Iwxr4teDBn2I0J+wuMAj4PXSMfmIBpgg+zBLhCRMZFreEb7M9DEOZ9IAj0V0q9304ZNZqURSt8jSb5OBVz9vsrpdT8xjtF5K/A05je9Z3BLuA9Ebmd/V76WcBd7TzPauBaETkf2AC4Ql7+c4Eq4BkR+QNmmN2vgZpGxz+HOcB5TURuw/RD+ClmHoIISqkNInIf8ISIHAR8jGlF6Ie5vv83pdRH7ZRdo0l69Bq+RpN8XIY5832lmf3/pImY/A7wMfAQcDfwMuYa/8lKqe/aeZ77gA+AvwGfAX8FUEpVAqdhzsr/BdwDPA40UMohc/804EvgKcwBwCbMcDsatb0NmIHpoPcv4HXMgco+YF075dZoUgJR6nv5LjQajQYwE+8AnyilLo63LBqNpmPoGb5Go9FoNGmAVvgajUaj0aQB2qSv0Wg0Gk0aoGf4Go1Go9GkAVrhazQajUaTBmiFr9FoNBpNGqAVvkaj0Wg0aYBW+BqNRqPRpAFa4Ws0Go1Gkwb8P+JKB33mEIENAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rabi_data.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "444d829c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: rabi_rate\n", - "- value: 8.006+/-0.007\n", - "- χ²: 1.4033703465519922\n", - "- quality: good\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(rabi_data.analysis_results(\"rabi_rate\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7fa0e4b4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0amp(0,)x0.062450+0.000000jdefaultTrue2022-07-14 15:14:35.606000-0400273991e1-0985-43d8-9ad2-d9d4738bc1fb
1amp(0,)sx0.031225+0.000000jdefaultTrue2022-07-14 15:14:35.606000-0400273991e1-0985-43d8-9ad2-d9d4738bc1fb
2amp()sx0.250000+0.000000jdefaultTrue2022-07-14 14:54:15.214312-0400None
3amp()x0.500000+0.000000jdefaultTrue2022-07-14 14:54:15.214147-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 amp (0,) x 0.062450+0.000000j default True \n", - "1 amp (0,) sx 0.031225+0.000000j default True \n", - "2 amp () sx 0.250000+0.000000j default True \n", - "3 amp () x 0.500000+0.000000j default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:14:35.606000-0400 273991e1-0985-43d8-9ad2-d9d4738bc1fb \n", - "1 2022-07-14 15:14:35.606000-0400 273991e1-0985-43d8-9ad2-d9d4738bc1fb \n", - "2 2022-07-14 14:54:15.214312-0400 None \n", - "3 2022-07-14 14:54:15.214147-0400 None " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))" - ] - }, - { - "cell_type": "markdown", - "id": "c90b2be5", - "metadata": {}, - "source": [ - "The table above shows that we have now updated the amplitude of our $\\pi$-pulse from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the `x` and `y` schedules we will see that the amplitude of the `y` schedule has also been updated as seen when requesting schedules form the `Calibrations` instance. Furthermore, we used the result from the `Rabi` experiment to also update the value of the `sx` pulse. This was achieved by specifying `(np.pi/2, \"amp\", \"sx\")` when calling `update`." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "bd9ff343", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.03122496+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"sx\", qubit)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "95d75c23", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.06244991+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"x\", qubit)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "62b1318f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.06244991j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"y\", qubit)" - ] - }, - { - "cell_type": "markdown", - "id": "addeda59", - "metadata": {}, - "source": [ - "## 3. Saving and loading calibrations\n", - "\n", - "The values of the calibrated parameters can be saved to a `.csv` file and reloaded at a later point in time. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "317994db", - "metadata": {}, - "outputs": [], - "source": [ - "cals.save(file_type=\"csv\", overwrite=True, file_prefix=\"Lima\")" - ] - }, - { - "cell_type": "markdown", - "id": "b384d6d0", - "metadata": {}, - "source": [ - "After saving the values of the parameters you may restart your kernel. If you do so, you will only need to run the following cell to recover the state of your calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "24256b82", - "metadata": {}, - "outputs": [], - "source": [ - "cals = Calibrations.from_backend(backend, library)\n", - "cals.load_parameter_values(file_name=\"Limaparameter_values.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "80ca665c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0amp(0,)sx0.031225+0.000000jdefaultTrue2022-07-14 15:14:35.606000-0400273991e1-0985-43d8-9ad2-d9d4738bc1fb
1amp(0,)x0.062450+0.000000jdefaultTrue2022-07-14 15:14:35.606000-0400273991e1-0985-43d8-9ad2-d9d4738bc1fb
2amp()sx0.250000+0.000000jdefaultTrue2022-07-14 15:14:51.580671-0400None
3amp()x0.500000+0.000000jdefaultTrue2022-07-14 15:14:51.580614-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 amp (0,) sx 0.031225+0.000000j default True \n", - "1 amp (0,) x 0.062450+0.000000j default True \n", - "2 amp () sx 0.250000+0.000000j default True \n", - "3 amp () x 0.500000+0.000000j default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:14:35.606000-0400 273991e1-0985-43d8-9ad2-d9d4738bc1fb \n", - "1 2022-07-14 15:14:35.606000-0400 273991e1-0985-43d8-9ad2-d9d4738bc1fb \n", - "2 2022-07-14 15:14:51.580671-0400 None \n", - "3 2022-07-14 15:14:51.580614-0400 None " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))" - ] - }, - { - "cell_type": "markdown", - "id": "7d1f127f", - "metadata": {}, - "source": [ - "## 4. Calibrating the value of the DRAG coefficient\n", - "\n", - "A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage\n", - "to a neighbouring transition. It is a standard pulse with an additional derivative\n", - "component. It is designed to reduce the frequency spectrum of a normal pulse near\n", - "the $|1\\rangle$ - $|2\\rangle$ transition, reducing the chance of leakage\n", - "to the $|2\\rangle$ state. The optimal value of the DRAG parameter is chosen to\n", - "minimize both leakage and phase errors resulting from the AC Stark shift.\n", - "The pulse envelope is $f(t) = \\Omega_x(t) + j \\beta \\frac{\\rm d}{{\\rm d }t} \\Omega_x(t)$.\n", - "Here, $\\Omega_x$ is the envelop of the in-phase component of the pulse and\n", - "$\\beta$ is the strength of the quadrature which we refer to as the DRAG\n", - "parameter and seek to calibrate in this experiment. \n", - "The DRAG calibration will run\n", - "several series of circuits. In a given circuit a Rp(β) - Rm(β) block is repeated\n", - "$N$ times. Here, Rp is a rotation with a positive angle and Rm is the same rotation\n", - "with a negative amplitude." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "74edd0ee", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library import RoughDragCal" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "fac11c93", - "metadata": {}, - "outputs": [], - "source": [ - "cal_drag = RoughDragCal(qubit, cals, backend=backend, betas=np.linspace(-20, 20, 25))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3a337cf4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cal_drag.set_experiment_options(reps=[3, 5, 7])\n", - "\n", - "cal_drag.circuits()[5].draw(output='mpl')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c3958dff", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "drag_data = cal_drag.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3a6430f4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drag_data.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "dc39db70", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: beta\n", - "- value: 0.909+/-0.007\n", - "- χ²: 3.4576970514589824\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(drag_data.analysis_results(\"beta\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f02bd7a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0β()sx0.000000defaultTrue2022-07-14 15:14:51.580660-0400None
1β()x0.000000defaultTrue2022-07-14 15:14:51.580628-0400None
2β(0,)x0.908745defaultTrue2022-07-14 15:25:35.434000-0400d809cd2d-9487-449f-b0cd-21a4a048cb1c
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 β () sx 0.000000 default True \n", - "1 β () x 0.000000 default True \n", - "2 β (0,) x 0.908745 default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:14:51.580660-0400 None \n", - "1 2022-07-14 15:14:51.580628-0400 None \n", - "2 2022-07-14 15:25:35.434000-0400 d809cd2d-9487-449f-b0cd-21a4a048cb1c " - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters=\"β\"))" - ] - }, - { - "cell_type": "markdown", - "id": "00ae061c", - "metadata": {}, - "source": [ - "## 5. Fine amplitude calibration\n", - "\n", - "The `FineAmplitude` calibration experiment repeats $N$ times a gate with a pulse\n", - "to amplify the under or over-rotations in the gate to determine the optimal amplitude.\n", - "The circuits that are run have a custom gate with the pulse schedule attached to it\n", - "through the calibrations." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "20ab91f2", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitudeCal" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "bfb3124b", - "metadata": {}, - "outputs": [], - "source": [ - "amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "c6127e65", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "amp_x_cal.circuits()[5].draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "24067164", - "metadata": {}, - "outputs": [], - "source": [ - "data_fine = amp_x_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "076bed0c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_fine.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "cf2cc09a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.0212+/-0.0005\n", - "- χ²: 9.42123821511291\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_fine.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "markdown", - "id": "367c2e1f", - "metadata": {}, - "source": [ - "The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the `FineXAmplitude` experiment. Note that this calculation is automatically done by the `Amplitude.update` function." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "81adf659", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of 0.021 rad.\n", - "Thus, scale the 0.0620+0.0000j pulse amplitude by 0.993 to obtain 0.06162+0.00000j.\n" - ] - } - ], - "source": [ - "dtheta = data_fine.analysis_results(\"d_theta\").value.nominal_value\n", - "target_angle = np.pi\n", - "scale = target_angle / (target_angle + dtheta)\n", - "pulse_amp = cals.get_parameter_value(\"amp\", qubit, \"x\")\n", - "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n", - "print(f\"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "97c69c65", - "metadata": {}, - "source": [ - "Observe, once again, that the calibrations have automatically been updated." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "81e7f3de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0amp(0,)sx0.031225+0.000000jdefaultTrue2022-07-14 15:14:35.606000-0400273991e1-0985-43d8-9ad2-d9d4738bc1fb
1amp(0,)x0.062032+0.000000jdefaultTrue2022-07-14 15:33:45.470000-0400f175c8ae-2db8-4f4a-9856-8b0da99ba563
2amp()sx0.250000+0.000000jdefaultTrue2022-07-14 15:14:51.580671-0400None
3amp()x0.500000+0.000000jdefaultTrue2022-07-14 15:14:51.580614-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 amp (0,) sx 0.031225+0.000000j default True \n", - "1 amp (0,) x 0.062032+0.000000j default True \n", - "2 amp () sx 0.250000+0.000000j default True \n", - "3 amp () x 0.500000+0.000000j default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:14:35.606000-0400 273991e1-0985-43d8-9ad2-d9d4738bc1fb \n", - "1 2022-07-14 15:33:45.470000-0400 f175c8ae-2db8-4f4a-9856-8b0da99ba563 \n", - "2 2022-07-14 15:14:51.580671-0400 None \n", - "3 2022-07-14 15:14:51.580614-0400 None " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))" - ] - }, - { - "cell_type": "markdown", - "id": "114a92a9", - "metadata": {}, - "source": [ - "To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "84146c1a", - "metadata": {}, - "outputs": [], - "source": [ - "data_fine2 = amp_x_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "5218f8e0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_fine2.figure(0)" - ] - }, - { - "cell_type": "markdown", - "id": "2fbc466a", - "metadata": {}, - "source": [ - "As can be seen from the data above and the analysis result below we have managed to reduce the error in the rotation angle ${\\rm d}\\theta$." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "25ddccd3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.0059+/-0.0005\n", - "- χ²: 14.235087428889651\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_fine2.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "markdown", - "id": "a4935730", - "metadata": {}, - "source": [ - "### Fine amplitude calibration of the $\\pi/2$ rotation\n", - "\n", - "We now wish to calibrate the amplitude of the $\\pi/2$ rotation." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "50423105", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library.calibration.fine_amplitude import FineSXAmplitudeCal" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "a075eacb", - "metadata": {}, - "outputs": [], - "source": [ - "amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"sx\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "3d38a13f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "amp_sx_cal.circuits()[5].draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "6c00c3f5", - "metadata": {}, - "outputs": [], - "source": [ - "data_fine_sx = amp_sx_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "0117cb2d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_fine_sx.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "3d6416eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: @Parameters_FineAmplitudeAnalysis\n", - "- value: CurveFitResult:\n", - " - fitting method: least_squares\n", - " - number of sub-models: 2\n", - " * F_spam cal.(x) = amp / 2 * (2 * x - 1) + base\n", - " * F_fine amp.(x) = amp / 2 * cos((d_theta + angle_per_gate) * x - phase_offset)...\n", - " - success: True\n", - " - number of function evals: 4\n", - " - degree of freedom: 11\n", - " - chi-square: 151.8690392576554\n", - " - reduced chi-square: 13.806276296150491\n", - " - Akaike info crit.: 39.37545730432373\n", - " - Bayesian info crit.: 41.292629293169504\n", - " - init params:\n", - " * amp = 0.9575106223444139\n", - " * base = 0.48712821794551364\n", - " * d_theta = 0.01609675454624555\n", - " * angle_per_gate = 1.5707963267948966\n", - " * phase_offset = 3.141592653589793\n", - " - fit params:\n", - " * amp = 0.9577757484044117 ± 0.0030085604459862336\n", - " * base = 0.48725465793313366 ± 0.0013044949734525495\n", - " * d_theta = 0.017888140290864118 ± 0.0003383997071097267\n", - " * angle_per_gate = 1.5707963267948966 ± 0.0\n", - " * phase_offset = 3.141592653589793 ± 0.0\n", - " - correlations:\n", - " * (amp, d_theta) = -0.22302123064374882\n", - " * (base, d_theta) = -0.18796538515909572\n", - " * (amp, base) = 0.5175856247793784\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_fine_sx.analysis_results(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "5d5a1131", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.01789+/-0.00034\n", - "- χ²: 13.806276296150491\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_fine_sx.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "markdown", - "id": "72571816", - "metadata": {}, - "source": [ - "The parameter value is reflected in the calibrations." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "ae984c47", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterqubitsschedulevaluegroupvaliddate_timeexp_id
0amp(0,)sx0.030873+0.000000jdefaultTrue2022-07-14 15:48:35.561000-04008975dc2f-4c33-4320-9cad-55078d42a509
1amp(0,)x0.061915+0.000000jdefaultTrue2022-07-14 15:42:25.570000-04003473321c-f37a-4933-9c99-821cff0c2f5f
2amp()sx0.250000+0.000000jdefaultTrue2022-07-14 15:14:51.580671-0400None
3amp()x0.500000+0.000000jdefaultTrue2022-07-14 15:14:51.580614-0400None
\n", - "
" - ], - "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 amp (0,) sx 0.030873+0.000000j default True \n", - "1 amp (0,) x 0.061915+0.000000j default True \n", - "2 amp () sx 0.250000+0.000000j default True \n", - "3 amp () x 0.500000+0.000000j default True \n", - "\n", - " date_time exp_id \n", - "0 2022-07-14 15:48:35.561000-0400 8975dc2f-4c33-4320-9cad-55078d42a509 \n", - "1 2022-07-14 15:42:25.570000-0400 3473321c-f37a-4933-9c99-821cff0c2f5f \n", - "2 2022-07-14 15:14:51.580671-0400 None \n", - "3 2022-07-14 15:14:51.580614-0400 None " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters=\"amp\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "f7cb5878", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.0308733757+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"sx\", qubit)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "f45f6482", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.0619148338+0j), sigma=80, beta=0.9087453946), DriveChannel(0)), name=\"x\", transform=AlignLeft())" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"x\", qubit)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "68f6e469", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.0619148338j, sigma=80, beta=0.9087453946), DriveChannel(0)), name=\"y\", transform=AlignLeft())" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cals.get_schedule(\"y\", qubit)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "8c8369d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import qiskit.tools.jupyter\n", - "%qiskit_copyright" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index af8cd3752c..bb1d1c979f 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -1,21 +1,20 @@ -####################################################### Calibrations: Schedules and gate parameters from experiments -####################################################### +############################################################ -To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in qiskit-experiments allows users to run experiments to find the pulse shapes and parameter values that maximizes the fidelity of the resulting quantum operations. Calibrations experiments encapsulates the internal processes and allow experimenters do calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). +To produce high fidelity quantum operations, we want to be able to run good gates. The +calibration module in Qiskit Experiments allows users to run experiments to find the +pulse shapes and parameter values that maximizes the fidelity of the resulting quantum +operations. Calibrations experiments encapsulate the internal processes and allow +experimenters to perform calibration operations in a quicker way. Without the experiments +module, we would need to define pulse schedules and plot the resulting measurement +data manually (see also the `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). -Each experiment usually provides additional information about the system used in subsequent experiments. +Calibrating single-qubit gates on a pulse backend +================================================= -.. jupyter-execute:: - - import numpy as np - - import qiskit.pulse as pulse - from qiskit.circuit import Parameter - - from qiskit_experiments.calibration_management import Calibrations - -On our own environment, we may use one of the pulse-enabled real backends for all the experiments like below. +In this tutorial, we demonstrate how to calibrate single-qubit gates using the +calibration framework in Qiskit Experiments. You can run these experiments on any +backend with Pulse enabled: .. jupyter-execute:: :hide-code: @@ -31,266 +30,572 @@ On our own environment, we may use one of the pulse-enabled real backends for al provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') backend = provider.get_backend('ibmq_lima') -We can verify whether the backend supports Pulse features by checking the backend configuration. +We can verify whether the backend supports Pulse features by checking the +backend configuration: .. jupyter-execute:: backend_config = backend.configuration() assert backend_config.open_pulse, "Backend doesn't support Pulse" -On the other hand we can also use a mock backend in case no IBM Quantum Experience credentials found. For this tutorial, we will use mock backends prepared for each experiment. +For the purposes of the tutorial, we will run experiments on our test pulse +backend, ``SingleTransmonTestBackend``, a backend that simulates the underlying pulses +with Qiskit Dynamics on a three-level model of a transmon. We will run experiments to +find the qubit frequency, calibrate the amplitude of DRAG pulses, and choose the value +of the DRAG parameter that minimizes leakage. The calibration framework requires +the user to + +- Setup an instance of Calibrations, -To use in the experiments we first need to define template schedule to calibrate for `x` pulse. +- Run calibration experiments, found in ``qiskit_experiments.library.calibration``. + +Note that the values of the parameters stored in the instance of the ``Calibrations`` class +will automatically be updated by the calibration experiments. +This automatic updating can also be disabled using the ``auto_update`` flag. .. jupyter-execute:: - def setup_cals(backend) -> Calibrations: - """A function to instantiate calibrations and add a couple of template schedules.""" - cals = Calibrations(backend) + import pandas as pd + import numpy as np + import qiskit.pulse as pulse + from qiskit.circuit import Parameter + from qiskit_experiments.calibration_management.calibrations import Calibrations + from qiskit import schedule + from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend - dur = Parameter("dur") - amp = Parameter("amp") - sigma = Parameter("σ") - beta = Parameter("β") - drive = pulse.DriveChannel(Parameter("ch0")) +.. jupyter-execute:: - # Define and add template schedules. - with pulse.build(name="x") as x: - pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) + backend = SingleTransmonTestBackend(5.2e9,-.25e9, 1e9, 0.8e9, noise=False) + qubit = 0 + cals=Calibrations.from_backend(backend) + print(cals.get_inst_map()) - cals.add_schedule(x, num_qubits=1) - - return cals +The two functions below show how to setup an instance of Calibrations. +To do this the user defines the template schedules to calibrate. +These template schedules are fully parameterized, even the channel indices +on which the pulses are played. Furthermore, the name of the parameter in the channel +index must follow the convention laid out in the documentation +of the calibration module. Note that the parameters in the channel indices +are automatically mapped to the channel index when get_schedule is called. - def add_parameter_guesses(cals: Calibrations): - - """Add guesses for the parameter values to the calibrations.""" - for sched in ["x"]: - print(sched) - cals.add_parameter_value(80, "σ", schedule=sched) - cals.add_parameter_value(0.5, "β", schedule=sched) - cals.add_parameter_value(320, "dur", schedule=sched) - cals.add_parameter_value(0.5, "amp", schedule=sched) +.. jupyter-execute:: + + # A function to instantiate calibrations and add a couple of template schedules. + def setup_cals(backend) -> Calibrations: + + cals = Calibrations.from_backend(backend) + + dur = Parameter("dur") + amp = Parameter("amp") + sigma = Parameter("σ") + beta = Parameter("β") + drive = pulse.DriveChannel(Parameter("ch0")) + + # Define and add template schedules. + with pulse.build(name="xp") as xp: + pulse.play(pulse.Drag(dur, amp, sigma, beta), drive) + + with pulse.build(name="xm") as xm: + pulse.play(pulse.Drag(dur, -amp, sigma, beta), drive) + + with pulse.build(name="x90p") as x90p: + pulse.play(pulse.Drag(dur, Parameter("amp"), sigma, Parameter("β")), drive) + + cals.add_schedule(xp, num_qubits=1) + cals.add_schedule(xm, num_qubits=1) + cals.add_schedule(x90p, num_qubits=1) + + return cals + + # Add guesses for the parameter values to the calibrations. + def add_parameter_guesses(cals: Calibrations): + + for sched in ["xp", "x90p"]: + cals.add_parameter_value(80, "σ", schedule=sched) + cals.add_parameter_value(0.5, "β", schedule=sched) + cals.add_parameter_value(320, "dur", schedule=sched) + cals.add_parameter_value(0.5, "amp", schedule=sched) + +When setting up the calibrations we add three pulses: a :math:`\pi`-rotation, +with a schedule named ``xp``, a schedule ``xm`` identical to ``xp`` +but with a nagative amplitude, and a :math:`\pi/2`-rotation, with a schedule +named ``x90p``. Here, we have linked the amplitude of the ``xp`` and ``xm`` pulses. +Therefore, calibrating the parameters of ``xp`` will also calibrate +the parameters of ``xm``. -.. =================================== -.. 1. Finding qubits with spectroscopy -.. =================================== -.. Typically, the first experiment we do is to search for the qubit frequency, which is the difference between the ground and excited states. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit. +.. jupyter-execute:: -.. We start with a mock backend. + cals = setup_cals(backend) + add_parameter_guesses(cals) -.. We then setup calibrations for the backend. +A similar setup is achieved by using a pre-built library of gates. +The library of gates provides a standard set of gates and some initial guesses +for the value of the parameters in the template schedules. +This is shown below using the ``FixedFrequencyTransmon`` library which provides the ``x``, +``y``, ``sx``, and ``sy`` pulses. Note that in the example below +we change the default value of the pulse duration to 320 samples -.. .. jupyter-execute:: +.. jupyter-execute:: -.. cals = setup_cals(spec_backend) # Block until our job and its post processing finish. -.. add_parameter_guesses(cals) + from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon -.. We define the qubit we will work with and prepare the experiment using `RoughFrequencyCal`. + library = FixedFrequencyTransmon(default_values={"duration": 320}) + cals = Calibrations.from_backend(backend, libraries=[library]) + print(library.default_values()) # check what parameter values this library has + print(cals.get_inst_map()) # check the new cals's InstructionScheduleMap made from the library + print(cals.get_schedule('x',(0,))) # check one of the schedules built from the new calibration -.. .. jupyter-execute:: +We are going to run the spectroscopy, Rabi, DRAG, and fine-amplitude calibration experiments +one after another and update the parameters after every experiment, keeping track of +parameter values. -.. from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal +Finding qubits with spectroscopy +-------------------------------- -.. qubit = 0 -.. freq01_estimate = spec_backend.defaults().qubit_freq_est[qubit] -.. frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) -.. spec = RoughFrequencyCal(qubit, cals, frequencies, backend=spec_backend) +Here, we are using a backend for which we already know the qubit frequency. +We will therefore use the spectroscopy experiment to confirm that +there is a resonance at the qubit frequency reported by the backend. -.. .. jupyter-execute:: +.. jupyter-execute:: -.. circuit = spec.circuits()[0] -.. circuit.draw() + from qiskit_experiments.library.calibration.rough_frequency import RoughFrequencyCal -.. We run the experiment. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. +We first show the contents of the calibrations for qubit 0. +Note that the guess values that we added before apply to all qubits on the chip. +We see this in the table below as an empty tuple ``()`` in the qubits column. +Observe that the parameter values of ``y`` do not appear in this table as they are given by the values of ``x``. -.. .. jupyter-execute:: +.. jupyter-execute:: -.. spec_data = spec.run().block_for_results() -.. spec_data.figure(0) + columns_to_show = ["parameter", "qubits", "schedule", "value", "date_time"] + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))[columns_to_show] -.. We can see the analysis results -.. .. jupyter-execute:: +.. jupyter-execute:: -.. print(spec_data.analysis_results("f01")) + freq01_estimate = backend.defaults().qubit_freq_est[qubit] + frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) + spec = RoughFrequencyCal(qubit, cals, frequencies, backend=backend) + spec.set_experiment_options(amp=0.005) -.. ======================================================== -.. 1. Calibrating the pulse amplitudes with Rabi experiment -.. ======================================================== -.. We are going to run a sample Rabi experiment to calibrate rotations between the ground-state \|0\⟩ and the excited state \|1\⟩. We can think of this as a rotation by π radians around the x-axis of the Bloch sphere. Our goal is to seek the amplitude of the pulse needed to achieve this rotation. +.. jupyter-execute:: -.. First we define the mock backend. + circuit = spec.circuits()[0] + circuit.draw(output="mpl") -.. .. jupyter-execute:: +.. jupyter-execute:: -.. from qiskit_experiments.test.mock_iq_backend import RabiBackend -.. rabi_backend = RabiBackend() + next(iter(circuit.calibrations["Spec"].values())).draw() # let's check the schedule + -.. We then setup calibrations for the backend. +.. jupyter-execute:: -.. .. jupyter-execute:: + spec_data = spec.run().block_for_results() + spec_data.figure(0) -.. cals = setup_cals(rabi_backend) -.. add_parameter_guesses(cals) -.. We create a new Rabi experiment instance by providing the qubit index to be calibrated. In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation of a desired angle. +.. jupyter-execute:: -.. We do this with the calibration experiment `RoughAmplitudeCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + print(spec_data.analysis_results("f01")) -.. If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. -.. .. jupyter-execute:: +The instance of ``calibrations`` has been automatically updated with the measured +frequency, as shown below. +In addition to the columns shown below, the calibrations also store the group to which a value belongs, +whether a values is valid or not and the experiment id that produce a value. -.. from qiskit_experiments.library.calibration import RoughAmplitudeCal +.. jupyter-execute:: -.. qubit = 0 + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit]))[columns_to_show] + -.. rabi = RoughAmplitudeCal(qubit, cals) +Calibrating the pulse amplitudes with a Rabi experiment +------------------------------------------------------- -.. The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses with different amplitudes. +In the Rabi experiment we apply a pulse at the frequency of the qubit +and scan its amplitude to find the amplitude that creates a rotation +of a desired angle. We do this with the calibration experiment ``RoughXSXAmplitudeCal``. +This is a specialization of the ``Rabi`` experiment that will update the calibrations +for both the ``X`` pulse and the ``SX`` pulse using a single experiment. -.. .. jupyter-execute:: +.. jupyter-execute:: -.. rabi.circuits()[0].draw() + from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal + rabi = RoughXSXAmplitudeCal(qubit, cals, backend=backend, amplitudes=np.linspace(-0.1, 0.1, 51)) -.. After the experiment completes the value of the amplitudes in the calibrations will automatically be updated. This behaviour can be controlled using the `auto_update` argument given to the calibration experiment at initialization. +The rough amplitude calibration is therefore a Rabi experiment in which +each circuit contains a pulse with a gate. Different circuits correspond to pulses +with different amplitudes. -.. .. jupyter-execute:: - -.. rabi_data = rabi.run(rabi_backend) -.. rabi_data.block_for_results() # Block until our job and its post processing finish. -.. print(rabi_data) +.. jupyter-execute:: -.. .. jupyter-execute:: + rabi.circuits()[0].draw("mpl") -.. rabi_data.figure(0) +After the experiment completes the value of the amplitudes in the calibrations +will automatically be updated. This behaviour can be controlled using the ``auto_update`` +argument given to the calibration experiment at initialization. -.. In the analysis results, ``rabi_rate`` is the unit of frequency which our qubit completes a full cycle by 2π radians around the x-axis of the Bloch sphere. Using this information we calculate one period. However our goal was to seek the amplitude of the pulse needed to achieve a rotation by π radians which will take our qubit from ground-state \|0\⟩ to the excited state \|1\⟩. So we need to divide it by 2. +.. jupyter-execute:: -.. .. jupyter-execute:: - -.. pi_pulse_amplitude = (1/rabi_data.analysis_results("rabi_rate").value.value) / 2 -.. print(pi_pulse_amplitude) + rabi_data = rabi.run().block_for_results() + rabi_data.figure(0) -.. ================================== -.. 3. Saving and loading calibrations -.. ================================== +.. jupyter-execute:: -.. The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. + print(rabi_data.analysis_results("rabi_rate")) -.. .. code-block:: python +.. jupyter-execute:: -.. cals.save(file_type="csv", overwrite=True, file_prefix="RabiBackend") + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] -.. After saving the values of the parameters we may restart our kernel. If we do so, we will only need to run the following cell to recover the state of the calibrations. Since the schedules are currently not stored we need to call our `setup_cals` function to populate an instance of `Calibrations` with the template schedules. By contrast, the value of the parameters will be recovered from the file. +The table above shows that we have now updated the amplitude of our :math:`\pi` pulse +from 0.5 to the value obtained in the most recent Rabi experiment. +Importantly, since we linked the amplitudes of the ``x`` and ``y`` schedules +we will see that the amplitude of the ``y`` schedule has also been updated +as seen when requesting schedules form the ``Calibrations`` instance. +Furthermore, we used the result from the Rabi experiment to also update +the value of the ``sx`` pulse. -.. .. code-block:: python +.. jupyter-execute:: -.. from qiskit_experiments.test.mock_iq_backend import RabiBackend -.. rabi_backend = RabiBackend() -.. cals = BackendCalibrations(rabi_backend) -.. cals.load_parameter_values(file_name="RabiBackendparameter_values.csv") + cals.get_schedule("sx", qubit) -.. ======================================================= -.. 4. Using the Calibrated Amplitude in Another Experiment -.. ======================================================= -.. ------------------------------------------------------ -.. 4.1. Calibrating the value of the DRAG coefficient -.. ------------------------------------------------------ +.. jupyter-execute:: -.. A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage -.. to a neighbouring transition. It is a standard pulse with an additional derivative -.. component. It is designed to reduce the frequency spectrum of a normal pulse near -.. the :math:`|1\rangle - |2\rangle` transition, reducing the chance of leakage -.. to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to -.. minimize both leakage and phase errors resulting from the AC Stark shift. -.. The pulse envelope is :math:`f(t) = \Omega_x(t) + j \beta \frac{\rm d}{{\rm d }t} \Omega_x(t)`. -.. Here, :math:`\Omega_x` is the envelop of the in-phase component of the pulse and -.. $\beta$ is the strength of the quadrature which we refer to as the DRAG -.. parameter and seek to calibrate in this experiment. -.. The DRAG calibration will run -.. several series of circuits. In a given circuit a :math:`Rp(β) - Rm(β)` block is repeated -.. :math:`N` times. Here, Rp is a rotation with a positive angle and Rm is the same rotation -.. with a negative amplitude. + cals.get_schedule("x", qubit) + +.. jupyter-execute:: -.. We use a mock backend in case no IBM credentials found. + cals.get_schedule("y", qubit) -.. .. jupyter-execute:: +Saving and loading calibrations +------------------------------- -.. from qiskit_experiments.test.mock_iq_backend import DragBackend -.. drag_backend = DragBackend(gate_name="Drag(x)") +The values of the calibrated parameters can be saved to a .csv file +and reloaded at a later point in time. -.. We define the template schedule for `x` pulse using previous methods. +.. jupyter-execute:: -.. Note that, if we run the experiments on real backends, we wouldn't need to define template schedules again. + cals.save(file_type="csv", overwrite=True, file_prefix="PulseBackend") -.. .. jupyter-execute:: +After saving the values of the parameters you may restart your kernel. If you do so, +you will only need to run the following cell to recover the state of your calibrations. +Since the schedules are currently not stored we need to call our ``setup_cals`` function +or use a library to populate an instance of Calibrations with the template schedules. +By contrast, the value of the parameters will be recovered from the file. -.. cals = setup_cals(drag_backend) -.. add_parameter_guesses(cals) +.. jupyter-execute:: -.. We create a calibration version of Drag experiment instance by providing the qubit index to be calibrated. We use the calibration version of Drag experiment `RoughDragCal`. This is a calibration version of the Rabi experiment that will update the calibrations for the X pulse automatically. + cals = Calibrations.from_backend(backend, library) + cals.load_parameter_values(file_name="PulseBackendparameter_values.csv") -.. If we do not set any experiment options using `set_experiment_options()` method, experiment will use the default values. Default values can be seen `here `__ under `Experiment Options`. +.. jupyter-execute:: -.. .. jupyter-execute:: + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] + + +Calibrating the value of the DRAG coefficient +--------------------------------------------- + +A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage +and phase errors to a neighbouring transition. It is a standard pulse with an additional +derivative component. It is designed to reduce the frequency spectrum of a +normal pulse near the :math:`|1> - |2>` transition, +reducing the chance of leakage to the :math:`|2>` state. +The optimal value of the DRAG parameter is chosen to minimize both +leakage and phase errors resulting from the AC Stark shift. +The pulse envelope is :math:`f(t)=\Omega_x(t)+j\beta\frac{\rm d}{{\rm d}t}\Omega_x(t)`. +Here, :math:`\Omega_x(t)` is the envelop of the in-phase component +of the pulse and :math:`\beta` is the strength of the quadrature +which we refer to as the DRAG parameter and seek to calibrate +in this experiment. The DRAG calibration will run several +series of circuits. In a given circuit a Rp(β) - Rm(β) block +is repeated :math:`N` times. Here, Rp is a rotation +with a positive angle and Rm is the same rotation with a +negative amplitude. -.. from qiskit_experiments.library import RoughDragCal -.. drag = RoughDragCal(qubit, cals) +.. jupyter-execute:: -.. .. jupyter-execute:: + from qiskit_experiments.library import RoughDragCal + cal_drag = RoughDragCal(qubit, cals, backend=backend, betas=np.linspace(-20, 20, 25)) + cal_drag.set_experiment_options(reps=[3, 5, 7]) + cal_drag.circuits()[5].draw(output='mpl') -.. drag_data = drag.run(drag_backend) -.. drag_data.block_for_results() +.. jupyter-execute:: -.. .. jupyter-execute:: + drag_data = cal_drag.run().block_for_results() + drag_data.figure(0) -.. drag_data.figure(0) +.. jupyter-execute:: -.. ================== -.. 1. Miscalibrations -.. ================== + print(drag_data.analysis_results("beta")) -.. In this section, we will see what if we run a miscalibrated `X` gate - with a false amplitude - on a qubit. After that, we will use the amplitude value we get from the Rabi experiment above to see the difference. +.. jupyter-execute:: -.. Note that, the following lines are for demonstration purposes and should be run on a real backend to see the actual difference. + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="β"))[columns_to_show] -.. We first define a simple circuit that contains an X gate and measurement. +Fine amplitude calibration +-------------------------- -.. .. jupyter-execute:: - -.. from qiskit import QuantumCircuit +The ``FineAmplitude`` calibration experiment repeats :math:`N` times +a gate with a pulse to amplify the under or over-rotations +in the gate to determine the optimal amplitude. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitudeCal + amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name="x") + amp_x_cal.circuits()[5].draw(output="mpl") + +.. jupyter-execute:: + + data_fine = amp_x_cal.run().block_for_results() + data_fine.figure(0) + +.. jupyter-execute:: + + print(data_fine.analysis_results("d_theta")) + +The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the FineXAmplitude experiment. Note that this calculation is automatically done by the Amplitude.update function. + +.. jupyter-execute:: + + dtheta = data_fine.analysis_results("d_theta").value.nominal_value + target_angle = np.pi + scale = target_angle / (target_angle + dtheta) + pulse_amp = cals.get_parameter_value("amp", qubit, "x") + print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") + print(f"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.") + +Observe, once again, that the calibrations have automatically been updated. + +.. jupyter-execute:: + + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] + +To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again. + +.. jupyter-execute:: + + data_fine2 = amp_x_cal.run().block_for_results() + data_fine2.figure(0) + +.. jupyter-execute:: + + print(data_fine2.analysis_results("d_theta")) + +As can be seen from the data above and the analysis result below +we have managed to reduce the error in the rotation angle dtheta. + +Fine amplitude calibration of the :math:`\pi`/2 rotation +-------------------------------------------------------- + +We now wish to calibrate the amplitude of the :math:`\pi/2` rotation. + +.. jupyter-execute:: + + from qiskit_experiments.library.calibration.fine_amplitude import FineSXAmplitudeCal + + amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name="sx") + amp_sx_cal.circuits()[5].draw(output="mpl") + +.. jupyter-execute:: + + data_fine_sx = amp_sx_cal.run().block_for_results() + data_fine_sx.figure(0) + +.. jupyter-execute:: + + print(data_fine_sx.analysis_results(0)) + +.. jupyter-execute:: + + print(data_fine_sx.analysis_results("d_theta")) + +.. jupyter-execute:: + + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] + + +Fine calibrations of a pulse amplitude +====================================== + +The amplitude of a pulse can be precisely calibrated using +error amplifying gate sequences. These gate sequences apply +the same gate a variable number of times. Therefore, if each gate +has a small error :math:`d\theta` in the rotation angle then +a sequence of :math:`n` gates will have a rotation error of :math:`n` * :math:`d\theta`. + +.. jupyter-execute:: + + import numpy as np + from qiskit.pulse import InstructionScheduleMap + import qiskit.pulse as pulse + from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude + from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend + +.. jupyter-execute:: + + backend = SingleTransmonTestBackend() + qubit = 0 + +Fine `X` gate amplitude calibration +----------------------------------- + +We will run the error amplifying experiments with our own pulse schedules +on which we purposefully add over and under rotations. +To do this we create an instruction to schedule map which we populate with +the schedules we wish to work with. This instruction schedule map is then +given to the transpile options of the experiment so that +the Qiskit transpiler can attach the pulse schedules to the gates in the experiments. +We base all our pulses on the default X pulse of "SingleTransmonTestBackend". + +.. jupyter-execute:: + + x_pulse = backend.defaults().instruction_schedule_map.get('x', (qubit,)).instructions[0][1].pulse + d0, inst_map = pulse.DriveChannel(qubit), pulse.InstructionScheduleMap() + + +We now take the ideal x pulse amplitude reported by the backend and +add/subtract a 2% over/underrotation to it by scaling the ideal amplitude and see +if the experiment can detect this over/underrotation. We replace the default X pulse +in the instruction schedule map with this over/underrotated pulse. + +.. jupyter-execute:: -.. circ = QuantumCircuit(1, 1) -.. circ.x(0) -.. circ.measure(0, 0) -.. circ.draw() + ideal_amp = x_pulse.amp + over_amp = ideal_amp*1.02 + under_amp = ideal_amp*0.98 + print(f"The reported amplitude of the X pulse is {ideal_amp:.4f} which we set as ideal_amp.") + print(f"we use {over_amp:.4f} amplitude for overroation pulse and {under_amp:.4f} for underrotation pulse.") + # build the over rotated pulse and add it to the instruction schedule map + with pulse.build(backend=backend, name="x") as x_over: + pulse.play(pulse.Drag(x_pulse.duration, over_amp, x_pulse.sigma, x_pulse.beta), d0) + inst_map.add("x", (qubit,), x_over) + +Let's look at one of the circuits of the FineXAmplitude experiment. +To calibrate the X gate we add an SX gate before the X gates to move the ideal population +to the equator of the Bloch sphere where the sensitivity to over/under rotations is the highest. + +.. jupyter-execute:: + + overamp_cal = FineXAmplitude(qubit, backend=backend) + overamp_cal.set_transpile_options(inst_map=inst_map) + overamp_cal.circuits()[4].draw(output='mpl') + +.. jupyter-execute:: + + # do the experiment + exp_data_over = overamp_cal.run(backend).block_for_results() + print(f"The ping-pong pattern points on the figure below indicate") + print(f"an over rotation which makes the initial state rotate more than pi.") + print(f"Therefore, the miscalibrated X gate makes the qubit stay away from the Bloch sphere equator.") + exp_data_over.figure(0) + +We now look at a pulse with an under rotation to see how the FineXAmplitude experiment +detects this error. We will compare the results to the over rotation above. + +.. jupyter-execute:: + + # build the under rotated pulse and add it to the instruction schedule map + with pulse.build(backend=backend, name="x") as x_under: + pulse.play(pulse.Drag(x_pulse.duration, under_amp, x_pulse.sigma, x_pulse.beta), d0) + inst_map.add("x", (qubit,), x_under) + + # do the experiment + underamp_cal = FineXAmplitude(qubit, backend=backend) + underamp_cal.set_transpile_options(inst_map=inst_map) + + exp_data_under = underamp_cal.run(backend).block_for_results() + exp_data_under.figure(0) + +Similarly to the over rotation, the under rotated pulse creates +qubit populations that do not lie on the equator of the Bloch sphere. +However, compared to the ping-pong pattern of the over rotated pulse, +the under rotated pulse produces a flipped ping-pong pattern. +This allows us to determine not only the magnitude of the rotation error +but also its sign. + +.. jupyter-execute:: + + # analyze the results + target_angle = np.pi + dtheta_over = exp_data_over.analysis_results("d_theta").value.nominal_value + scale_over = target_angle / (target_angle + dtheta_over) + dtheta_under = exp_data_under.analysis_results("d_theta").value.nominal_value + scale_under = target_angle / (target_angle + dtheta_under) + print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta_over:.3f} rad in over-rotated pulse case.") + print(f"Thus, scale the {over_amp:.4f} pulse amplitude by {scale_over:.3f} to obtain {over_amp*scale_over:.5f}.") + print(f"On the other hand, we measued a deviation of {dtheta_under:.3f} rad in under-rotated pulse case.") + print(f"Thus, scale the {under_amp:.4f} pulse amplitude by {scale_under:.3f} to obtain {under_amp*scale_under:.5f}.") + +Analyzing a :math:`\pi`/2 pulse +------------------------------- + +The amplitude of the `SX` gate is calibrated with the FineSXAmplitude experiment. +Unlike the FineXAmplitude experiment, the FineSXAmplitude experiment +does not require other gates than the SX gate since the number of repetitions +can be chosen such that the ideal population is always on the equator of the +Bloch sphere. +To demonstrate the FineSXAmplitude experiment, we now create a SX pulse by +dividing the amplitude of the X pulse by two. +We expect that this pulse might have a small rotation error which we want to correct. + + +.. jupyter-execute:: + + # build sx_pulse with the default x_pulse from defaults and add it to the InstructionScheduleMap + sx_pulse = pulse.Drag(x_pulse.duration, 0.5*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name="SXp_d0") + with pulse.build(name='sx') as sched: + pulse.play(sx_pulse,d0) + inst_map.add("sx", (qubit,), sched) + + # do the expeirment + amp_cal = FineSXAmplitude(qubit, backend) + amp_cal.set_transpile_options(inst_map=inst_map) + exp_data_x90p = amp_cal.run().block_for_results() + exp_data_x90p.figure(0) + +From the analysis result, we can see that there is a small rotation error. + +.. jupyter-execute:: + + # check how much more the given sx_pulse makes over or under roatation + print(exp_data_x90p.analysis_results("d_theta")) + target_angle = np.pi / 2 + dtheta = exp_data_x90p.analysis_results("d_theta").value.nominal_value + scale = target_angle / (target_angle + dtheta) + print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") + print(f"Thus, scale the {sx_pulse.amp:.4f} pulse amplitude by {scale:.3f} to obtain {sx_pulse.amp*scale:.5f}.") + +Let's change the amplitude of the SX pulse by a factor :math:`\pi/2 / (\pi/2 + d\theta)` +to turn it into a sharp :math:`\pi/2` rotation. + +.. jupyter-execute:: + + pulse_amp = sx_pulse.amp*scale + + with pulse.build(backend=backend, name="sx") as sx_new: + pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0) + + inst_map.add("sx", (qubit,), sx_new) + inst_map.get('sx',(qubit,)) + + # do the experiment + data_x90p = amp_cal.run().block_for_results() + data_x90p.figure(0) + +You can now see that the correction to the pulse amplitude has allowed us +to improve our SX gate as shown by the analysis result below. + +.. jupyter-execute:: -.. Then we define a calibration for the `X` gate on qubit 0. For the `amp` parameter we use a default wrong value. + # check the dtheta + print(data_x90p.analysis_results("d_theta")) -.. .. jupyter-execute:: -.. from qiskit import pulse, transpile -.. from qiskit.test.mock import FakeArmonk -.. from qiskit.pulse.library import Constant -.. backend = FakeArmonk() -.. # build a simple circuit that only contain one x gate and measurement -.. circ = QuantumCircuit(1, 1) -.. circ.x(0) -.. circ.measure(0, 0) -.. with pulse.build(backend) as my_schedule: -.. pulse.play(Constant(duration=10, amp=0.1), pulse.drive_channel(0)) # build the constant pulse -.. circ.add_calibration('x', [0], my_schedule) # map x gate in qubit 0 to my_schedule -.. circ = transpile(circ, backend) -.. circ.draw(idle_wires=False) -.. Execute our circuit: -.. .. jupyter-execute:: -.. result = backend.run(transpile(circ, backend), shots=1000).result() -.. counts = result.get_counts(circ) -.. print(counts) \ No newline at end of file diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 94f877df6b..3c5eccc6b2 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -258,13 +258,14 @@ every logic defined in the :class:`AnalysisA`. .. _curve_analysis_workflow: -Cureve Analysis Workflow -======================== +Curve Analysis Workflow +======================= Typically curve analysis performs fitting as follows. This workflow is defined in the method :meth:`CurveAnalysis._run_analysis`. 1. Initialization +----------------- Curve analysis calls :meth:`_initialization` method where it initializes some internal states and optionally populate analysis options @@ -274,6 +275,7 @@ or dynamically generate the fit models (``self._models``) with fresh analysis op A developer can override this method to perform initialization of analysis-specific variables. 2. Data processing +------------------ Curve analysis calls :meth:`_run_data_processing` method where the data processor in the analysis option is internally called. @@ -287,6 +289,7 @@ A developer can inject extra data processing, for example, filtering, smoothing, or elimination of outliers for better fitting. 3. Fitting +---------- Curve analysis calls :meth:`_run_curve_fit` method which is the core functionality of the fitting. The another method :meth:`_generate_fit_guesses` is internally called to @@ -298,6 +301,7 @@ A developer can also override the entire :meth:`_run_curve_fit` method to apply custom fitting algorithms. This method must return :class:`.CurveFitResult` dataclass. 4. Post processing +------------------ Curve analysis runs several postprocessing against to the fit outcome. It calls :meth:`_create_analysis_results` to create :class:`AnalysisResultData` class diff --git a/docs/tutorials/fine_amplitude_calibration.rst b/docs/tutorials/fine_amplitude_calibration.rst deleted file mode 100644 index 1f4799387e..0000000000 --- a/docs/tutorials/fine_amplitude_calibration.rst +++ /dev/null @@ -1,181 +0,0 @@ -================================================ -Fine Calibrations of a pulse amplitude -================================================ -Calibrating quantum gates is the task of finding the parameters of the underlying pulses that best implement the target gate. -The amplitude of a pulse can be precisely calibrated using -error amplifying gate sequences. These gate sequences apply -the same gate a variable number of times. Therefore, if each gate -has a small error :math:`d\theta` in the rotation angle then -a sequence of :math:`n` gates will have a rotation error of :math:`n` * :math:`d\theta`. - -We will illustrate how the `FineXAmplitude` experiment works with the `PulseBackend`, -i.e., a backend that simulates the underlying pulses with Qiskit Dynamics -on a three-level model of a transmon. This simulator backend -can be replaced with a standard hardware IBM Quantum backend. - -.. jupyter-execute:: - - import numpy as np - from qiskit.pulse import InstructionScheduleMap - import qiskit.pulse as pulse - from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude - from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend - -.. jupyter-execute:: - - backend = SingleTransmonTestBackend() - qubit = 0 ------------------------------------------------------ -Fine X gate Amplitude Calibration ------------------------------------------------------ -We will run the error amplifying experiments with our own pulse schedules -on which we purposefully add over and under rotations. -To do this we create an instruction to schedule map which we populate with -the schedules we wish to work with. This instruction schedule map is then -given to the transpile options of the experiment so that -the Qiskit transpiler can attach the pulse schedules to the gates in the experiments. -We base all our pulses on the default X pulse of "SingleTransmonTestBackend". - -.. jupyter-execute:: - - x_pulse = backend.defaults().instruction_schedule_map.get('x', (qubit,)).instructions[0][1].pulse - d0, inst_map = pulse.DriveChannel(qubit), pulse.InstructionScheduleMap() - - -We now take the ideal x pulse amplitude reported by the backend and -add/subtract a 2% over/underrotation to it by scaling the ideal amplitude and see -if the experiment can detect this over/underrotation. We replace the default X pulse -in the instruction schedule map with this over/underrotated pulse. - -.. jupyter-execute:: - - ideal_amp = x_pulse.amp - over_amp = ideal_amp*1.02 - under_amp = ideal_amp*0.98 - print(f"The reported amplitude of the X pulse is {ideal_amp:.4f} which we set as ideal_amp.") - print(f"we use {over_amp:.4f} amplitude for overroation pulse and {under_amp:.4f} for underrotation pulse.") - # build the over rotated pulse and add it to the instruction schedule map - with pulse.build(backend=backend, name="x") as x_over: - pulse.play(pulse.Drag(x_pulse.duration, over_amp, x_pulse.sigma, x_pulse.beta), d0) - inst_map.add("x", (qubit,), x_over) - -Let's look at one of the circuits of the FineXAmplitude experiment. -To calibrate the X gate we add an SX gate before the X gates to move the ideal population -to the equator of the Bloch sphere where the sensitivity to over/under rotations is the highest. - -.. jupyter-execute:: - - overamp_cal = FineXAmplitude(qubit, backend=backend) - overamp_cal.set_transpile_options(inst_map=inst_map) - overamp_cal.circuits()[4].draw(output='mpl') - -.. jupyter-execute:: - - # do the experiment - exp_data_over = overamp_cal.run(backend).block_for_results() - print(f"The ping-pong pattern points on the figure below indicate") - print(f"an over rotation which makes the initial state rotate more than pi.") - print(f"Therefore, the miscalibrated X gate makes the qubit stay away from the Bloch sphere equator.") - exp_data_over.figure(0) - -We now look at a pulse with an under rotation to see how the FineXAmplitude experiment -detects this error. We will compare the results to the over rotation above. - -.. jupyter-execute:: - - # build the under rotated pulse and add it to the instruction schedule map - with pulse.build(backend=backend, name="x") as x_under: - pulse.play(pulse.Drag(x_pulse.duration, under_amp, x_pulse.sigma, x_pulse.beta), d0) - inst_map.add("x", (qubit,), x_under) - - # do the experiment - underamp_cal = FineXAmplitude(qubit, backend=backend) - underamp_cal.set_transpile_options(inst_map=inst_map) - - exp_data_under = underamp_cal.run(backend).block_for_results() - exp_data_under.figure(0) - -Similarly to the over rotation, the under rotated pulse creates -qubit populations that do not lie on the equator of the Bloch sphere. -However, compared to the ping-pong pattern of the over rotated pulse, -the under rotated pulse produces a flipped ping-pong pattern. -This allows us to determine not only the magnitude of the rotation error -but also its sign. - -.. jupyter-execute:: - - # analyze the results - target_angle = np.pi - dtheta_over = exp_data_over.analysis_results("d_theta").value.nominal_value - scale_over = target_angle / (target_angle + dtheta_over) - dtheta_under = exp_data_under.analysis_results("d_theta").value.nominal_value - scale_under = target_angle / (target_angle + dtheta_under) - print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta_over:.3f} rad in over-rotated pulse case.") - print(f"Thus, scale the {over_amp:.4f} pulse amplitude by {scale_over:.3f} to obtain {over_amp*scale_over:.5f}.") - print(f"On the other hand, we measued a deviation of {dtheta_under:.3f} rad in under-rotated pulse case.") - print(f"Thus, scale the {under_amp:.4f} pulse amplitude by {scale_under:.3f} to obtain {under_amp*scale_under:.5f}.") - ------------------------------------------------------------------------------------ -Analyzing a pi/2 pulse ------------------------------------------------------------------------------------ -The amplitude of the SX gate is calibrated with the FineSXAmplitude experiment. -Unlike the FineXAmplitude experiment, the FineSXAmplitude experiment -does not require other gates than the SX gate since the number of repetitions -can be chosen such that the ideal population is always on the equator of the -Bloch sphere. -To demonstrate the FineSXAmplitude experiment, we now create a SX pulse by -dividing the amplitude of the X pulse by two. -We expect that this pulse might have a small rotation error which we want to correct. - - -.. jupyter-execute:: - - # build sx_pulse with the default x_pulse from defaults and add it to the InstructionScheduleMap - sx_pulse = pulse.Drag(x_pulse.duration, 0.5*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name="SXp_d0") - with pulse.build(name='sx') as sched: - pulse.play(sx_pulse,d0) - inst_map.add("sx", (qubit,), sched) - - # do the expeirment - amp_cal = FineSXAmplitude(qubit, backend) - amp_cal.set_transpile_options(inst_map=inst_map) - exp_data_x90p = amp_cal.run().block_for_results() - exp_data_x90p.figure(0) - -From the analysis result, we can see that there is a small rotation error. - -.. jupyter-execute:: - - # check how much more the given sx_pulse makes over or under roatation - print(exp_data_x90p.analysis_results("d_theta")) - target_angle = np.pi / 2 - dtheta = exp_data_x90p.analysis_results("d_theta").value.nominal_value - scale = target_angle / (target_angle + dtheta) - print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") - print(f"Thus, scale the {sx_pulse.amp:.4f} pulse amplitude by {scale:.3f} to obtain {sx_pulse.amp*scale:.5f}.") - -Let's change the amplitude of the SX pulse by a factor :math:`\pi/2 / (\pi/2 + d\theta)` -to turn it into a sharp :math:`\pi/2` rotation. - -.. jupyter-execute:: - - pulse_amp = sx_pulse.amp*scale - - with pulse.build(backend=backend, name="sx") as sx_new: - pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0) - - inst_map.add("sx", (qubit,), sx_new) - inst_map.get('sx',(qubit,)) - - # do the experiment - data_x90p = amp_cal.run().block_for_results() - data_x90p.figure(0) - -You can now see that the correction to the pulse amplitude has allowed us -to improve our SX gate as shown by the analysis result below. - -.. jupyter-execute:: - - # check the dtheta - print(data_x90p.analysis_results("d_theta")) - diff --git a/docs/tutorials/fine_calibrations.ipynb b/docs/tutorials/fine_calibrations.ipynb deleted file mode 100644 index 518d489193..0000000000 --- a/docs/tutorials/fine_calibrations.ipynb +++ /dev/null @@ -1,1109 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "tamil-indonesia", - "metadata": {}, - "source": [ - "# Fine Calibrations" - ] - }, - { - "cell_type": "markdown", - "id": "civilian-radio", - "metadata": {}, - "source": [ - "The amplitude of a pulse can be precisely calibrated using error amplifying gate sequences. These gate sequences apply the same gate a variable number of times. Therefore, if each gate has a small error $\\delta\\theta$ in the rotation angle then a sequence of $n$ gates will have a rotation error of $n\\cdot\\delta\\theta$. We will work with `ibmq_lima` and compare our results to those reported by the backend." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "acoustic-paint", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from qiskit import IBMQ\n", - "from qiskit.pulse import InstructionScheduleMap\n", - "import qiskit.pulse as pulse\n", - "\n", - "from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "accessory-alexandria", - "metadata": {}, - "outputs": [], - "source": [ - "IBMQ.load_account()\n", - "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n", - "backend = provider.get_backend('ibmq_lima')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "unusual-amendment", - "metadata": {}, - "outputs": [], - "source": [ - "qubit = 0" - ] - }, - { - "cell_type": "markdown", - "id": "5b924bfd", - "metadata": {}, - "source": [ - "### Instruction schedule map\n", - "\n", - "We will run the fine calibration experiments with our own pulse schedules. To do this we create an instruction to schedule map which we populate with the schedules we wish to work with. This instruction schedule map is then given to the transpile options of the calibration experiments so that the Qiskit transpiler can attach the pulse schedules to the gates in the experiments. We will base all our pulses on the default `X` pulse of Lima." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "phantom-language", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Drag(duration=160, amp=(0.12494962745493425+0j), sigma=40, beta=0.5729301274879344, name='Xp_d0')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_pulse = backend.defaults().instruction_schedule_map.get('x', (qubit,)).instructions[0][1].pulse\n", - "x_pulse" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "478d07d1", - "metadata": {}, - "outputs": [], - "source": [ - "# create the schedules we need and add them to an instruction schedule map.\n", - "sx_pulse = pulse.Drag(x_pulse.duration, 0.5*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name=\"SXp_d0\")\n", - "y_pulse = pulse.Drag(x_pulse.duration, 1.0j*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name=\"Yp_d0\")\n", - "\n", - "d0, inst_map = pulse.DriveChannel(qubit), InstructionScheduleMap()\n", - "\n", - "for name, pulse_ in [(\"x\", x_pulse), (\"y\", y_pulse), (\"sx\", sx_pulse)]:\n", - " with pulse.build(name=name) as sched:\n", - " pulse.play(pulse_, d0)\n", - " \n", - " inst_map.add(name, (qubit,), sched)" - ] - }, - { - "cell_type": "markdown", - "id": "grand-color", - "metadata": {}, - "source": [ - "## Fine Amplitude Calibration" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "adjusted-religious", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The reported amplitude of the X pulse is 0.1249+0.0000j.\n" - ] - } - ], - "source": [ - "ideal_amp = x_pulse.amp\n", - "print(f\"The reported amplitude of the X pulse is {ideal_amp:.4f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "distributed-official", - "metadata": {}, - "source": [ - "### Detecting an over-rotated pulse\n", - "\n", - "We now take the x pulse reported by the backend and add a 2% overrotation to it by scaling the amplitude and see if the experiment can detect this overrotation. We replace the default `X` pulse in the instruction schedule map with this overrotated pulse." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "wanted-soundtrack", - "metadata": {}, - "outputs": [], - "source": [ - "pulse_amp = ideal_amp*1.02\n", - "target_angle = np.pi\n", - "\n", - "with pulse.build(backend=backend, name=\"x\") as x_over:\n", - " pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0)\n", - " \n", - "inst_map.add(\"x\", (qubit,), x_over)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "former-quest", - "metadata": {}, - "outputs": [], - "source": [ - "amp_cal = FineXAmplitude(qubit, backend=backend)\n", - "amp_cal.set_transpile_options(inst_map=inst_map)" - ] - }, - { - "cell_type": "markdown", - "id": "available-bread", - "metadata": {}, - "source": [ - "Observe here that we added a square-root of X pulse before appyling the error amplifying sequence. This is done to be able to distinguish between over-rotated and under-rotated pulses." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "subsequent-composite", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "amp_cal.circuits()[5].draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "numeric-motion", - "metadata": {}, - "outputs": [], - "source": [ - "data_over = amp_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cooperative-division", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_over.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "outer-growing", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.0405+/-0.0005\n", - "- χ²: 13.353155002912484\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_over.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "convinced-juice", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of 0.040 rad.\n", - "Thus, scale the 0.1274+0.0000j pulse amplitude by 0.987 to obtain 0.12583+0.00000j.\n", - "Amplitude reported by the backend 0.1249+0.0000j.\n" - ] - } - ], - "source": [ - "dtheta = data_over.analysis_results(\"d_theta\").value.nominal_value\n", - "scale = target_angle / (target_angle + dtheta)\n", - "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n", - "print(f\"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.\")\n", - "print(f\"Amplitude reported by the backend {ideal_amp:.4f}.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6b9c2476", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.04045058362020325+/-0.000541620229372572" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_over.analysis_results(\"d_theta\").value" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8417b182", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_items([(< amp = 0.0+/-0.7479661744812719 >, 0.00010580796839217384), (< base = 0.0+/-0.9418237321009111 >, 0.0004354677743070045), (< d_theta = 0.0+/-1.2464006817566182 >, 0.00030417916468821794)])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(data_over.analysis_results(\"d_theta\").value)\n", - "data_over.analysis_results(\"d_theta\").value.error_components().items()" - ] - }, - { - "cell_type": "markdown", - "id": "leading-partition", - "metadata": {}, - "source": [ - "### Detecting an under-rotated pulse" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "unable-deficit", - "metadata": {}, - "outputs": [], - "source": [ - "pulse_amp = ideal_amp*0.98\n", - "target_angle = np.pi\n", - "\n", - "with pulse.build(backend=backend, name=\"xp\") as x_under:\n", - " pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0)\n", - " \n", - "inst_map.add(\"x\", (qubit,), x_under)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "double-difference", - "metadata": {}, - "outputs": [], - "source": [ - "amp_cal = FineXAmplitude(qubit, backend=backend)\n", - "amp_cal.set_transpile_options(inst_map=inst_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "worth-basis", - "metadata": {}, - "outputs": [], - "source": [ - "data_under = amp_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "proud-commission", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_under.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "separated-niger", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: -0.0821+/-0.0006\n", - "- χ²: 9.970967124462884\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_under.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "equal-exploration", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of -0.082 rad.\n", - "Thus, scale the 0.1225+0.0000j pulse amplitude by 1.027 to obtain 0.12574+0.00000j.\n", - "Amplitude reported by the backend 0.1249+0.0000j.\n" - ] - } - ], - "source": [ - "dtheta = data_under.analysis_results(\"d_theta\").value.nominal_value\n", - "scale = target_angle / (target_angle + dtheta)\n", - "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n", - "print(f\"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.\")\n", - "print(f\"Amplitude reported by the backend {ideal_amp:.4f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "novel-booking", - "metadata": {}, - "source": [ - "### Analyzing a $\\frac{\\pi}{2}$ pulse" - ] - }, - { - "cell_type": "markdown", - "id": "usual-battle", - "metadata": {}, - "source": [ - "We now consider the $\\frac{\\pi}{2}$ rotation. Note that in this case we do not need to add a $\\frac{\\pi}{2}$ rotation to the circuits." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "coordinated-education", - "metadata": {}, - "outputs": [], - "source": [ - "# restore the x_pulse\n", - "inst_map.add(\"x\", (qubit,), backend.defaults().instruction_schedule_map.get('x', (qubit,)))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "boring-shaft", - "metadata": {}, - "outputs": [], - "source": [ - "amp_cal = FineSXAmplitude(qubit, backend)\n", - "amp_cal.set_transpile_options(inst_map=inst_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "interior-schedule", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "amp_cal.circuits()[5].draw(output=\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "normal-content", - "metadata": {}, - "outputs": [], - "source": [ - "data_x90p = amp_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "naval-franklin", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_x90p.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "indonesian-tribe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.00428+/-0.00033\n", - "- χ²: 11.230245902959357\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_x90p.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "swiss-prayer", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ideal angle is 1.57 rad. We measured a deviation of 0.004 rad.\n", - "Thus, scale the 0.0625+0.0000j pulse amplitude by 0.997 to obtain 0.06231+0.00000j.\n", - "Amplitude reported by the backend 0.0618+0.0013j.\n" - ] - } - ], - "source": [ - "sx = backend.defaults().instruction_schedule_map.get('sx', (qubit,))\n", - "sx_ideal_amp = sx.instructions[0][1].pulse.amp\n", - "\n", - "target_angle = np.pi / 2\n", - "dtheta = data_x90p.analysis_results(\"d_theta\").value.nominal_value\n", - "scale = target_angle / (target_angle + dtheta)\n", - "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n", - "print(f\"Thus, scale the {sx_pulse.amp:.4f} pulse amplitude by {scale:.3f} to obtain {sx_pulse.amp*scale:.5f}.\")\n", - "print(f\"Amplitude reported by the backend {sx_ideal_amp:.4f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "specified-english", - "metadata": {}, - "source": [ - "Let's rerun this calibration using the updated value of the amplitude of the $\\frac{\\pi}{2}$ pulse." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "extended-wedding", - "metadata": {}, - "outputs": [], - "source": [ - "pulse_amp = sx_pulse.amp*scale\n", - "\n", - "with pulse.build(backend=backend, name=\"sx\") as sx_new:\n", - " pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0)\n", - " \n", - "inst_map.add(\"sx\", (qubit,), sx_new)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "artistic-stand", - "metadata": {}, - "outputs": [], - "source": [ - "data_x90p = amp_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "excessive-transformation", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_x90p.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "liquid-details", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: d_theta\n", - "- value: 0.00663+/-0.00033\n", - "- χ²: 12.911096523942346\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_x90p.analysis_results(\"d_theta\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "driving-density", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The ideal angle is 1.57 rad. We measured a deviation of 0.007 rad.\n", - "Thus, scale the 0.0623+0.0000j pulse amplitude by 0.996 to obtain 0.06204+0.00000j.\n", - "Amplitude reported by the backend 0.0618+0.0013j.\n" - ] - } - ], - "source": [ - "dtheta = data_x90p.analysis_results(\"d_theta\").value.nominal_value\n", - "scale = target_angle / (target_angle + dtheta)\n", - "print(f\"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.\")\n", - "print(f\"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.\")\n", - "print(f\"Amplitude reported by the backend {sx_ideal_amp:.4f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "restricted-brick", - "metadata": {}, - "source": [ - "## Fine DRAG Calibrations" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "billion-calibration", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library import FineXDrag" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "aging-volunteer", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The reported beta of the X pulse is 0.5729.\n" - ] - } - ], - "source": [ - "ideal_beta = x_pulse.beta\n", - "print(f\"The reported beta of the X pulse is {ideal_beta:.4f}.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "asian-anniversary", - "metadata": {}, - "outputs": [], - "source": [ - "pulse_beta = ideal_beta*1.25\n", - "target_angle = np.pi\n", - "\n", - "with pulse.build(backend=backend, name=\"x\") as x_over:\n", - " pulse.play(pulse.Drag(x_pulse.duration, x_pulse.amp, x_pulse.sigma, pulse_beta), d0)\n", - " \n", - "inst_map.add(\"x\", (qubit,), x_over)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "continuing-arkansas", - "metadata": {}, - "outputs": [], - "source": [ - "drag_cal = FineXDrag(qubit, backend)\n", - "drag_cal.set_transpile_options(inst_map=inst_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "independent-extraction", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drag_cal.circuits()[2].draw(\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "excited-eleven", - "metadata": {}, - "outputs": [], - "source": [ - "data_drag_x = drag_cal.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "driven-fiber", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_drag_x.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "pacific-bible", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: @Parameters_ErrorAmplificationAnalysis\n", - "- value: CurveFitResult:\n", - " - fitting method: least_squares\n", - " - number of sub-models: 1\n", - " * F_ping_pong(x) = amp / 2 * cos((d_theta + angle_per_gate) * x - phase_offset)...\n", - " - success: True\n", - " - number of function evals: 6\n", - " - degree of freedom: 18\n", - " - chi-square: 242.2756979579215\n", - " - reduced chi-square: 13.459760997662306\n", - " - Akaike info crit.: 53.886881038248454\n", - " - Bayesian info crit.: 55.878345585356435\n", - " - init params:\n", - " * amp = 1.0\n", - " * d_theta = 0.03930267433141715\n", - " * angle_per_gate = 0.0\n", - " * phase_offset = 1.5707963267948966\n", - " * base = 0.5678580354911272\n", - " - fit params:\n", - " * amp = 1.0 ± 0.0\n", - " * d_theta = 0.016856868773894 ± 0.0006311372319886394\n", - " * angle_per_gate = 0.0 ± 0.0\n", - " * phase_offset = 1.5707963267948966 ± 0.0\n", - " * base = 0.48688791053418623 ± 0.0034365498800017027\n", - " - correlations:\n", - " * (d_theta, base) = -0.8622992458490701\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(data_drag_x.analysis_results(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "geographic-terminology", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.016856868773894" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_drag_x.analysis_results(\"d_theta\").value.nominal_value" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "cutting-firewall", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adjust β=0.716 by ddelta=-0.121 to get 0.595 as new β.\n", - "The backend reports β=0.573\n" - ] - } - ], - "source": [ - "dtheta = data_drag_x.analysis_results(\"d_theta\").value.nominal_value\n", - "\n", - "ddelta = -0.25 * np.sqrt(np.pi) * dtheta * x_pulse.sigma / ((target_angle**2) / 4)\n", - "\n", - "print(f\"Adjust β={pulse_beta:.3f} by ddelta={ddelta:.3f} to get {ddelta + pulse_beta:.3f} as new β.\")\n", - "print(f\"The backend reports β={x_pulse.beta:.3f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "05ffc1e1", - "metadata": {}, - "source": [ - "## Half angle calibrations\n", - "\n", - "Phase errors imply that it is possible for the `sx` and `x` pulse to be misaligned. This can occure, for example, due to non-linearities in the mixer skew. The half angle experiment allows us to measure such issues." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "adc1e987", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_experiments.library import HalfAngle" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "7b15ae90", - "metadata": {}, - "outputs": [], - "source": [ - "hac = HalfAngle(qubit, backend)\n", - "hac.set_transpile_options(inst_map=inst_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "46785cdb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hac.circuits()[5].draw(\"mpl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "adf28640", - "metadata": {}, - "outputs": [], - "source": [ - "exp_data = hac.run().block_for_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "b1a1e889", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp_data.figure(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "7b14a270", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AnalysisResult\n", - "- name: @Parameters_ErrorAmplificationAnalysis\n", - "- value: CurveFitResult:\n", - " - fitting method: least_squares\n", - " - number of sub-models: 1\n", - " * F_ping_pong(x) = amp / 2 * cos((d_theta + angle_per_gate) * x - phase_offset)...\n", - " - success: True\n", - " - number of function evals: 6\n", - " - degree of freedom: 13\n", - " - chi-square: 129.66478153568548\n", - " - reduced chi-square: 9.974213964283498\n", - " - Akaike info crit.: 36.35353473185864\n", - " - Bayesian info crit.: 37.76963513406306\n", - " - init params:\n", - " * amp = 1.0\n", - " * d_theta = 0.0551290748741386\n", - " * angle_per_gate = 3.141592653589793\n", - " * phase_offset = -1.5707963267948966\n", - " * base = 0.5037490627343164\n", - " - fit params:\n", - " * amp = 1.0 ± 0.0\n", - " * d_theta = -0.040472139117284534 ± 0.0005019078548224959\n", - " * angle_per_gate = 3.141592653589793 ± 0.0\n", - " * phase_offset = -1.5707963267948966 ± 0.0\n", - " * base = 0.4856279605000334 ± 0.0019244621149488855\n", - " - correlations:\n", - " * (d_theta, base) = 0.04624866542183945\n", - "- quality: bad\n", - "- device_components: ['Q0']\n", - "- verified: False\n" - ] - } - ], - "source": [ - "print(exp_data.analysis_results(0))" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "e994b6a6", - "metadata": {}, - "outputs": [], - "source": [ - "dhac = exp_data.analysis_results(\"d_hac\").value.nominal_value" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "315fab21", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adjust the phase of 0.0 of the sx pulse by 0.020 rad.\n", - "The backend reports an angle of 0.021 for the sx pulse.\n" - ] - } - ], - "source": [ - "sx = backend.defaults().instruction_schedule_map.get('sx', (qubit,))\n", - "sx_amp = sx.instructions[0][1].pulse.amp\n", - "\n", - "print(f\"Adjust the phase of {np.angle(sx_pulse.amp)} of the sx pulse by {-dhac/2:.3f} rad.\")\n", - "print(f\"The backend reports an angle of {np.angle(sx_amp):.3f} for the sx pulse.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "divided-messaging", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import qiskit.tools.jupyter\n", - "%qiskit_copyright" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index ba34dd6d36..e5821978b1 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -23,7 +23,7 @@ Getting started learning Exploring modules -======= +================= .. toctree:: :maxdepth: 1 diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst index 7c27562482..263daabadd 100644 --- a/docs/tutorials/intro.rst +++ b/docs/tutorials/intro.rst @@ -20,6 +20,7 @@ What Qiskit Experiments can do * Run built-in or customized experiments with all the options that Terra has * Specify fit series and parameters in the analysis * Transform the data through the data processor +* Visualize data with support for custom drawing backends * Flexible visualization, storage, and retrieval of data A quick primer diff --git a/docs/tutorials/learning.rst b/docs/tutorials/learning.rst index c86f77d9aa..5dc45a2978 100644 --- a/docs/tutorials/learning.rst +++ b/docs/tutorials/learning.rst @@ -1,5 +1,8 @@ +Getting Started +=============== + Installation -============= +============ Official Qiskit Experiments releases can be installed via the python package manager ``pip``. @@ -57,7 +60,6 @@ this example, but you can use any IBM backend that you can access through Qiskit delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) exp = T1(qubit=0, delays=delays) - exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() Run and display results: @@ -70,6 +72,8 @@ Run and display results: for result in exp_data.analysis_results(): print(result) +The `block_for_results` method is optional here, and is used to block execution +of subsequent code until the experiment has fully completed execution and analysis. Setting experiment options ========================== @@ -77,34 +81,61 @@ Setting experiment options Often it's insufficient to run an experiment with only the default options. There are four types of options one can set for an experiment: -* **Run options**, for passing to the experiment's ``run()`` method. Any run option that - Terra supports can be set and will be passed to the jobs at run time: +Run options +----------- + +These options are passed to the experiment's ``run()`` method and then to the ``run()`` +method of your specified backend. Any run option that your backend supports can be set: .. code-block:: - exp.set_run_options(meas_level=MeasLevel.CLASSIFIED) + exp.set_run_options(shots=1000, + meas_level=MeasLevel.CLASSIFIED, + meas_return="avg") -* **Transpile options**, for passing to the Terra transpiler: +Consult the documentation of :meth:`qiskit.providers.ibmq.IBMQBackend.run` or +your specific backend type for valid options. + +Transpile options +----------------- +These options are passed to the Terra transpiler to transpile the experiment circuits +before execution: .. code-block:: - exp.set_transpile_options(scheduling_method='asap') + exp.set_transpile_options(scheduling_method='asap', + optimization_level=3, + basis_gates=["x", "sx", "rz"]) -* **Experiment options**, unique to the experiment class. Consult the API references - for the options for each experiment. - -.. code-block:: +Consult the documentation of :func:`qiskit.compiler.transpile` for valid options. - exp.set_experiment_options(delays=fields["delays_t1"]) +Experiment options +------------------ +These options are unique to each experiment class. Many experiment options can be set +upon experiment instantiation, but can also be explicitly set via :meth:`set_experiment_options`: -* **Analysis options**, unique to the analysis class. Consult the API references for the - options for each experiment analysis. Unlike the other options, this one is - not set via the experiment object but via analysis instead: +.. code-block:: + + exp = T1(qubit=i, delays=delays) + exp.set_experiment_options(delays=new_delays) + +Consult the :doc:`API documentation ` for the options of each experiment class. + +Analysis options +---------------- +These options are unique to each analysis class. Unlike the other options, this one is not set via the experiment object +but via analysis instead: .. code-block:: + exp = rb.StandardRB(qubits=(0,), + lengths=list(range(1, 300, 30)), + seed=123, + backend=backend) exp.analysis.set_options(gate_error_ratio=None) +Consult the :doc:`API documentation ` for the options of each +experiment's analysis class. Running experiments on multiple qubits ====================================== @@ -116,8 +147,8 @@ There are two core types of composite experiments: qubits used. * **Batch experiments** run consecutively in time. These circuits can overlap in qubits. -Here's an example of measuring :math:`T_1` of multiple qubits in the same experiment, by -creating a parallel experiment: +Here's an example of measuring :math:`T_1` of two qubits on the same device simultaneously +in a parallel experiment: .. jupyter-execute:: @@ -132,12 +163,13 @@ creating a parallel experiment: for result in parallel_data.analysis_results(): print(result) +Parallel and batch experiments can be nested arbitrarily to make Viewing sub experiment data =========================== -The experiment data returned from a batched experiment also contains -individual experiment data for each sub experiment which can be accessed +The experiment data returned from a composite experiment also contains +individual experiment data for each sub experiment, which can be accessed using ``child_data``. .. jupyter-execute:: @@ -149,3 +181,6 @@ using ``child_data``. for result in sub_data.analysis_results(): print(result) +You can use the `flatten_results` flag to flatten the results of all +component experiments into one level. + From 5879d41f1e7ff718429ccd209819700d68eab815 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 18 Jan 2023 10:02:04 -0500 Subject: [PATCH 19/64] addressed comments --- docs/guides/randomized_benchmarking.rst | 56 +--- docs/howtos/index.rst | 10 - docs/tutorials/calibrations.rst | 6 +- docs/tutorials/curve_analysis.rst | 5 +- docs/tutorials/custom_experiment.rst | 299 +++++++++++++++--- docs/tutorials/data_processing.rst | 186 ----------- docs/tutorials/data_processor.rst | 185 +++++++++++ .../tutorials/images/compositeexperiments.png | Bin 0 -> 673448 bytes docs/tutorials/intro.rst | 3 +- docs/tutorials/learning.rst | 45 ++- docs/tutorials/visualization.rst | 295 +++++++++++++---- qiskit_experiments/library/__init__.py | 2 +- .../library/characterization/drag.py | 2 +- .../characterization/fine_amplitude.py | 2 +- 14 files changed, 738 insertions(+), 358 deletions(-) delete mode 100644 docs/tutorials/data_processing.rst create mode 100644 docs/tutorials/images/compositeexperiments.png diff --git a/docs/guides/randomized_benchmarking.rst b/docs/guides/randomized_benchmarking.rst index 492d65c68d..411196361c 100644 --- a/docs/guides/randomized_benchmarking.rst +++ b/docs/guides/randomized_benchmarking.rst @@ -1,14 +1,15 @@ Randomized Benchmarking ======================= -A randomized benchmarking (RB) experiment consists of the generation of +Randomized benchmarking (RB) is a popular protocol for characterizing the +error rate of quantum processors. An RB experiment consists of the generation of random Clifford circuits on the given qubits such that the unitary computed by the circuits is the identity. After running the circuits, the number of shots resulting in an error (i.e. an output different than the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford. -See `Qiskit -Textbook `__ +See the `Qiskit +Textbook `__ for an explanation on the RB method, which is based on Ref. [1, 2]. .. jupyter-execute:: @@ -57,7 +58,7 @@ The analysis results of the RB Experiment may include: or 2-qubit quantum gates (see Ref. [3]) Running a 1-qubit RB experiment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------- Standard RB experiment will provide you gate errors for every basis gates constituting averaged Clifford gate. Note that you can only obtain a single EPC value :math:`\cal E` @@ -114,7 +115,7 @@ interleaved RB experiment will always give you accurate error value :math:`e_i`. Running a 2-qubit RB experiment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------- In the same way we can compute EPC for two-qubit RB experiment. However, the EPC value obtained by the experiment indicates a depolarization @@ -205,7 +206,7 @@ contribution of depolarization from single-qubit error channels. Displaying the RB circuits -~~~~~~~~~~~~~~~~~~~~~~~~~~ +-------------------------- Generating an example RB circuit: @@ -228,9 +229,8 @@ Interleaved RB experiment ------------------------- Interleaved RB experiment is used to estimate the gate error of the -interleaved gate (see Ref. [4]). - -In addition to the usual RB parameters, we also need to provide: +interleaved gate (see Ref. [4]). In addition to the usual RB parameters, we also +need to provide: - ``interleaved_element``: the element to interleave, given either as a group element or as an instruction/circuit @@ -250,35 +250,7 @@ Extra analysis results include - ``EPC_systematic_bounds``: The systematic error bounds of the interleaved gate error (see Ref. [4]) -Running a 1-qubit interleaved RB experiment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. jupyter-execute:: - - lengths = np.arange(1, 800, 200) - num_samples = 10 - seed = 1010 - qubits = [0] - - # Run an Interleaved RB experiment on qubit 0 - # The interleaved gate is the x gate - int_exp1 = InterleavedRB( - circuits.XGate(), qubits, lengths, num_samples=num_samples, seed=seed) - - # Run - int_expdata1 = int_exp1.run(backend).block_for_results() - int_results1 = int_expdata1.analysis_results() - -.. jupyter-execute:: - - # View result data - display(int_expdata1.figure(0)) - for result in int_results1: - print(result) - - -Running a 2-qubit interleaved RB experiment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Let's run an interleaved RB experiment on two qubits: .. jupyter-execute:: @@ -369,7 +341,9 @@ A. Ohki, Mark B. Ketchen, and M. Steffen, *Characterization of addressability by simultaneous randomized benchmarking*, https://arxiv.org/pdf/1204.6308 -.. jupyter-execute:: +See also +-------- + +* API documentation: :mod:`qiskit_experiments.library.randomized_benchmarking` +* `Qiskit Textbook `__ - import qiskit.tools.jupyter - %qiskit_copyright diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index 0c886fb659..97463e36d7 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -11,16 +11,6 @@ useful actions in Qiskit Experiments. * -.. toctree:: - :includehidden: - - tutorials/index - howtos/index - guides/index - apidocs/index - release_notes - - diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index bb1d1c979f..f7274f37c3 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -3,8 +3,8 @@ Calibrations: Schedules and gate parameters from experiments To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in Qiskit Experiments allows users to run experiments to find the -pulse shapes and parameter values that maximizes the fidelity of the resulting quantum -operations. Calibrations experiments encapsulate the internal processes and allow +pulse shapes and parameter values that maximize the fidelity of the resulting quantum +operations. Calibration experiments encapsulate the internal processes and allow experimenters to perform calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement data manually (see also the `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). @@ -334,6 +334,8 @@ negative amplitude. pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="β"))[columns_to_show] +.. _fine-amplitude-cal: + Fine amplitude calibration -------------------------- diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 3c5eccc6b2..24bdf4e233 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -1,6 +1,5 @@ -========================================================= Curve Analysis: Fitting your data -========================================================= +================================= .. currentmodule:: qiskit_experiments.curve_analysis @@ -8,7 +7,7 @@ Curve analysis provides the analysis base class for a variety of experiments wit a single experimental parameter sweep. This analysis subclasses can override several class attributes to customize the behavior from data processing to post-processing, including providing systematic initial guess for parameters tailored to the experiment. -Here we describe how code developers can create new analysis inheriting from the base class. +Here we describe how code developers can create new analysis that inherits from the base class. .. _curve_analysis_overview: diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index b78d1a7353..744f370496 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -1,7 +1,7 @@ Writing a custom experiment =========================== -Qiskit-Experiments is designed to be easily customizable. To create an experiment subclass +Qiskit Experiments is designed to be easily customizable. To create an experiment subclass based on either the :class:`.BaseExperiment` class or an existing experiment, you should: - Implement the abstract :meth:`.BaseExperiment.circuits` method. @@ -29,9 +29,9 @@ allow configuring various experiment and execution options to set default backend options for running the transpiled circuits on a backend. - :meth:`.BaseExperiment._default_analysis_options` - to set default values for configurable options for the experiments analysis class. + to set default values for configurable options for the experiment's analysis class. Note that these should generally be set by overriding the :class:`.BaseAnalysis` - method :meth:`.BaseAnalysis._default_options` instead of this method except in the + method :meth:`.BaseAnalysis._default_options` instead of this method, except in the case where the experiment requires different defaults to the used analysis class. - :meth:`.BaseExperiment._transpiled_circuits` @@ -46,16 +46,16 @@ immediately after the previous measurement. Here, the :class:`.RestlessMixin` ca to set the appropriate run options and data processing chain. Analysis Subclasses -~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^ To create an analysis subclass, one only needs to implement the abstract -:meth:`.BaseAnalysis._run_analysis` method. This method takes a +:meth:`.BaseAnalysis._run_analysis` method. This method takes an :class:`.ExperimentData` container and kwarg analysis options. If any kwargs are used the :meth:`.BaseAnalysis._default_options` method should be overriden to define default values for these options. The :meth:`.BaseAnalysis._run_analysis` method should return a pair -``(results, figures)`` where ``results`` is a list of +``(results, figures)``, where ``results`` is a list of :class:`.AnalysisResultData` and ``figures`` is a list of :class:`matplotlib.figure.Figure`. @@ -63,25 +63,23 @@ The :mod:`qiskit_experiments.data_processing` module contains classes for building data processor workflows to help with advanced analysis of experiment data. -================================== Subclassing an Existing Experiment -================================== +---------------------------------- -This document will take you step-by-step through the process of subclassing an existing experiment in the Qiskit Experiment module. -The example in this guide focuses on adjusting the FineAmplitude experiment to calibrate on higher order transitions. -However, a similar process can be followed for other experiments. +Let's walk through the process of subclassing an existing experiment in the Qiskit +Experiments library. The FineAmplitude Experiment -============================ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The ``FineAmplitude`` calibration experiment repeats N times per gate with a pulse to amplify the under-/over-rotations in the gate to determine the optimal amplitude. -This experiment can be performed for a variety of rotations and subclasses are provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as ``FineXAmplitude`` and ``FineSXAmplitude`` respectively. -These provided subclasses focus on the 0 <-> 1 transition, however this experiment can also be performed for higher order transitions. +The ``FineAmplitude`` calibration experiment optimizes gate amplitude by repeating the +gate pulse N times, hence amplifying the under- or over-rotations. +This experiment can be performed for a variety of rotations, and subclasses are +provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as ``FineXAmplitude`` and ``FineSXAmplitude`` respectively. +These provided subclasses focus on the 0 <-> 1 transition, however, this experiment can also be performed for higher order transitions. -Subclassing the Experiment -========================== - -Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. +Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates +schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. In order to do this, we need to create a subclass, shown below. .. code-block:: @@ -113,7 +111,10 @@ Using the Subclass ================== Now, we can use our new subclass as we would the original parent class. -Pictured below are the results from following the Fine amplitude calibration tutorial for detecting an over-rotated pulse using our new ``HigherOrderFineXAmplitude`` class in place of the original ``FineXAmplitude`` class. +Shown below are results from following the fine amplitude calibration +tutorial :ref:`fine-amplitude-cal` for detecting an over-rotated pulse using our new +``HigherOrderFineXAmplitude`` class in place of the original +``FineXAmplitude`` class. You can try this for yourself and verify that your results are similar. .. code-block:: @@ -126,25 +127,241 @@ You can try this for yourself and verify that your results are similar. - device_components: ['Q0'] - verified: False -.. Writing a custom experiment -.. =========================== - -.. In this tutorial, we'll use what we've learned so far to make a full experiment from -.. the :class:`.BaseExperiment` template. - -.. A randomized measurement experiment -.. =================================== - - -.. This experiment creates a list of copies of an input circuit -.. and randomly samples an N-qubit Paulis to apply to each one before -.. a final N-qubit Z-basis measurement to randomized the expected -.. ideal output bitstring in the measured. - -.. The analysis uses the applied Pauli frame of a randomized -.. measurement experiment to de-randomize the measured counts -.. and combine across samples to return a single counts dictionary -.. the original circuit. +Writing a custom experiment +=========================== -.. This has the effect of Pauli-twirling and symmetrizing the -.. measurement readout error. \ No newline at end of file +In this tutorial, we'll use what we've learned so far to make a full experiment from +the :class:`.BaseExperiment` template. + +A randomized measurement experiment +=================================== + +This experiment creates a list of copies of an input circuit +and randomly samples an N-qubit Pauli to apply to each one before +a final N-qubit Z-basis measurement to randomize the expected +ideal output bitstring in the measurement. + +The analysis uses the applied Pauli frame of a randomized +measurement experiment to de-randomize the measured counts +and combine across samples to return a single counts dictionary +the original circuit. + +This has the effect of Pauli-twirling and symmetrizing the +measurement readout error. + +.. jupyter-execute:: + + from numpy.random import default_rng, Generator + from qiskit import QuantumCircuit + from qiskit_experiments.framework import BaseExperiment + from qiskit.quantum_info import random_pauli_list + + class RandomizedMeasurement(BaseExperiment): + """Randomized measurement experiment. + """ + + def __init__( + self, + circuit, + measured_qubits=None, + physical_qubits=None, + backend=None, + **experiment_options + ): + """Basic randomize Z-basis measuremenent via a Pauli frame transformation + + Note this will just append a new set of measurment at the end of a circuit. + A more advanced version of this experiment would be use a transpiler pass to + replace all exisiting measurements in a circuit with randomized measurements. + """ + if physical_qubits is None: + physical_qubits = tuple(range(circuit.num_qubits)) + if measured_qubits is None: + measured_qubits = tuple(range(circuit.num_qubits)) + + # Initialize BaseExperiment + analysis = RandomizedMeasurementAnalysis() + super().__init__(physical_qubits, analysis=analysis, backend=backend) + + # Add experiment properties + self._circuit = circuit + self._measured_qubits = measured_qubits + + # Set any init optinos + self.set_experiment_options(**experiment_options) + + @classmethod + def _default_experiment_options(cls): + options = super()._default_experiment_options() + options.num_samples = "default" + options.seed = None + return options + + def circuits(self): + # Number of classical bits for original circuit and added measurements + circ_nc = self._circuit.num_clbits + meas_nc = len(self._measured_qubits) + circ_qubits = list(range(self.num_qubits)) + circ_clbits = list(range(circ_nc)) + meas_qubits = self._measured_qubits + meas_clbits = list(range(circ_nc, circ_nc + meas_nc)) + + # Get number of samples from options + num_samples = self.experiment_options.num_samples + if num_samples == "default": + num_samples = 2 ** self.num_qubits + + # Get rng seed + seed = self.experiment_options.seed + if isinstance(seed, Generator): + rng = seed + else: + rng = default_rng(seed) + + # Sample Paulis this might have duplicates, but we don't really + # have any easy way of running different number of shots per circuit + # so we just run repeat circuits multiple times + paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) + + # Construct circuits + circuits = [] + orig_metadata = self._circuit.metadata or {} + for pauli in paulis: + name = f"{self._circuit.name}_{str(pauli)}" + circ = QuantumCircuit( + self.num_qubits, circ_nc + meas_nc, + name=name + ) + # Append original circuit + circ.compose( + self._circuit, circ_qubits, circ_clbits, inplace=True + ) + + # Add Pauli frame + circ.compose(pauli, meas_qubits, inplace=True) + + # Add final Measurement + circ.measure(meas_qubits, meas_clbits) + + # Add metadata + circ.metadata = orig_metadata.copy() + circ.metadata["rm_bits"] = meas_clbits + circ.metadata["rm_frame"] = str(pauli) + circ.metadata["rm_sig"] = pauli.x.astype(int).tolist() + + circuits.append(circ) + + return circuits + +And the corresponding analysis class: + +.. jupyter-execute:: + + from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData + + class RandomizedMeasurementAnalysis(BaseAnalysis): + """Analysis for randomized measurement experiment.""" + + # Helper dict to swap a clbit value + _swap_bit = {"0": "1", "1": "0"} + + def _run_analysis(self, experiment_data): + + combined_counts = {} + for datum in experiment_data.data(): + # Get counts + counts = datum["counts"] + num_bits = len(next(iter(counts))) + + # Get metadata + metadata = datum["metadata"] + clbits = metadata["rm_bits"] + sig = metadata["rm_sig"] + + # Construct full signature + full_sig = num_bits * [0] + for bit, val in zip(clbits, sig): + full_sig[bit] = val + + # Combine dicts + for key, val in counts.items(): + bitstring = self._swap_bitstring(key, full_sig) + if bitstring in combined_counts: + combined_counts[bitstring] += val + else: + combined_counts[bitstring] = val + + + result = AnalysisResultData("counts", combined_counts) + return [result], [] + + @classmethod + def _swap_bitstring(cls, bitstring, sig): + """Swap a bitstring based signature to flip bits at.""" + # This is very inefficient but demonstrates the basic idea + # Really should do with bitwise operations of integer counts rep + return "".join(reversed( + [cls._swap_bit[b] if sig[- 1 - i] else b for i, b in enumerate(bitstring)] + )) + +To test our code, we first simulate a noisy backend with asymmetric readout error in Aer: + +.. jupyter-execute:: + + from qiskit.providers.aer import AerSimulator, noise + + backend_ideal = AerSimulator() + + # Backend with asymetric readout error + p0g1 = 0.3 + p1g0 = 0.05 + noise_model = noise.NoiseModel() + noise_model.add_all_qubit_readout_error([[1 - p1g0, p1g0], [p0g1, 1 - p0g1]]) + noise_backend = AerSimulator(noise_model=noise_model) + +We now run the experiment with a GHZ circuit on an ideal backend: + +.. jupyter-execute:: + + # GHZ Circuit + nq = 4 + qc = QuantumCircuit(nq) + qc.h(0) + for i in range(1, nq): + qc.cx(i-1, i) + + # Experiment parameters + total_shots = 100000 + num_samples = 50 + shots = total_shots // num_samples + + # Run ideal randomized meas experiment + exp = RandomizedMeasurement(qc, num_samples=num_samples) + expdata_ideal = exp.run(AerSimulator(), shots=shots) + counts_ideal = expdata_ideal.analysis_results("counts").value + print(counts_ideal) + +Now we repeat the experiment on the backend with readout error: + +.. jupyter-execute:: + + # Run noisy randomized meas experiment with readout error + expdata_noise = exp.run(noise_backend, shots=shots) + counts_noise = expdata_noise.analysis_results("counts").value + + # Run noisy direct simulation of original circuit without randomization + meas_circ = qc.copy() + meas_circ.measure_all() + result = noise_backend.run(meas_circ, shots=total_shots).result() + counts_direct = result.get_counts(0) + + from qiskit.visualization import plot_histogram + + # Plot counts, ideally randomized one should be more symmetric in noise + # than direct one with asymmetric readout error + plot_histogram([counts_ideal, counts_direct, counts_noise], + legend=["Ideal", + "Asymmetric meas error (Direct)", + "Asymmetric meas error (Randomized)"]) + +We see that the direct asymmetric measurement is symmetrized by the application of randomized measurement. \ No newline at end of file diff --git a/docs/tutorials/data_processing.rst b/docs/tutorials/data_processing.rst deleted file mode 100644 index 339c561754..0000000000 --- a/docs/tutorials/data_processing.rst +++ /dev/null @@ -1,186 +0,0 @@ -Data processing -=============== - -In this tutorial we describe how to manipulate the different -types of data that quantum computers can return. -The tutorial covers key aspects of the ``data_processing`` package -such as how to initialize an instance of ``DataProcessor`` and how -to create the ``DataAction`` nodes that process the data. - -Data types on IBM Quantum backends ----------------------------------- - -IBM Quantum backends can return different types of data. There is -counts data and IQ data [1], referred to as level 2 and level 1 data, -respectively. Level 2 data corresponds -to a dictionary with bit-strings as keys and the number of -times the bit-string was measured as a value. Importantly -for some experiments, the backends can return a lower data level -known as IQ data. Here, I and Q stand -for in phase and quadrature. The IQ are points in the complex plane -corresponding to a time integrated measurement signal which is -reflected or transmitted through the readout resonator depending -on the setup. IQ data can be returned as "single" or "averaged" data. -Here, single means that the outcome of each single shot is returned -while average only returns the average of the IQ points over the -measured shots. The type of data that an experiment should return -is specified by the ``run_options`` of an experiment. - -Processing data of different types ----------------------------------- - -An experiment should work with the different data levels. -Crucially, the analysis, such as a curve analysis, expects the -same data format no matter the run options of the experiment. -Transforming the data returned by the backend into the format -that the analysis accepts is done by the ``data_processing`` library. -The key class here is the ``DataProcessor``. It is initialized from -two arguments. The first, is the ``input_key`` which is typically -"memory" or "counts" and identifies the key in the experiment data -where the data is located. The second argument ``data_actions`` -is a list of ``nodes`` where each node performs a processing step -of the data processor. Crucially, the output of one node in the -list is the input to the next node in the list. - -To illustrate the data processing module we consider an example -in which we measure a rabi oscillation with different data levels. -The code below sets up the Rabi experiment. - - -.. jupyter-execute:: - - import numpy as np - - from qiskit import pulse - from qiskit.circuit import Parameter - - from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend - from qiskit_experiments.data_processing import DataProcessor, nodes - from qiskit_experiments.library import Rabi - - with pulse.build() as sched: - pulse.play( - pulse.Gaussian(160, Parameter("amp"), sigma=40), - pulse.DriveChannel(0) - ) - - backend = SingleTransmonTestBackend() - - exp = Rabi( - qubit=0, - backend=backend, - schedule=sched, - amplitudes=np.linspace(-0.1, 0.1, 21) - ) - -We now run the Rabi experiment twice, once with level 1 data and -once with level 2 data. Here, we manually configure two data -processors but note that typically you do not need to do this -yourself. We begin with single-shot IQ data. - -.. jupyter-execute:: - - data_nodes = [nodes.SVD(), nodes.AverageData(axis=1), nodes.MinMaxNormalize()] - iq_processor = DataProcessor("memory", data_nodes) - exp.analysis.set_options(data_processor=iq_processor) - - exp_data = exp.run(meas_level=1, meas_return="single").block_for_results() - - display(exp_data.figure(0)) - -Since we requested IQ data we set the input key to "memory" which is -the key under which the data is located in the experiment data. The -``iq_processor`` contains three nodes. The first node ``SVD`` is a -singular value decomposition which projects the two-dimensional IQ -data on its main axis. The second node averages the single-shot -data. The output is a single float per quantum circuit. Finally, -the last node ``MinMaxNormalize`` normalizes the measured signal to -the interval [0, 1]. The ``iq_dataprocessor`` is then set as an option -of the analysis class. For those who are wondering what single-shot IQ -data looks like we plot the data returned by the zeroth and sixth circuit -in the code block below. - -.. jupyter-execute:: - - %matplotlib inline - - from qiskit_experiments.visualization import IQPlotter, MplDrawer - - plotter = IQPlotter(MplDrawer()) - - for idx in [0, 6]: - plotter.set_series_data( - f"Circuit {idx}", - points=np.array(exp_data.data(idx)["memory"]).squeeze(), - ) - - plotter.figure() - -Now we turn to counts data and see how the -data processor needs to be changed. - -.. jupyter-execute:: - - data_nodes = [nodes.Probability(outcome="1")] - count_processor = DataProcessor("counts", data_nodes) - exp.analysis.set_options(data_processor=count_processor) - - exp_data = exp.run(meas_level=2).block_for_results() - - display(exp_data.figure(0)) - -Now, the ``input_key`` is "counts" since that is the key under which the counts -data is saved in instances of ``ExperimentData``. The list of nodes -comprises a single data action which converts the counts to an estimation -of the probability of measuring the outcome "1". - -Writing data actions ---------------------- - -The nodes in a data processor are all sub-classes of ``DataAction``. -Users who wish to write their own data actions must (i) sub-class -``DataAction`` and (ii) implement the internal ``_process`` method -called by instances of ``DataProcessor``. This method is the -processing step that the node implements. It takes a numpy array as -input and returns the processed numpy array as output. This output -serves as the input for the next node in the data processing chain. -Here, the input and output numpy arrays can have a different shape. - -In addition to the standard ``DataAction`` the data processing package -also supports trainable data actions as subclasses of ``TrainableDataAction``. -These nodes must first be trained on the data before they can -process the data. An example of a ``TrainableDataAction`` is the -``SVD`` node which must first learn the main axis of the data before -it can project a data point onto this axis. To implement trainable nodes -developers must also implement the ``train`` method. This method is -called when ``DataProcessor.train`` is called. - -Conclusion ----------- - -In this tutorial you learnt about the data processing module in Qiskit -Experiments. Data is processed by data processors that -call a list of nodes each acting once on the data. Data -processing connects the data returned by the backend to the data that -the analysis classes need. Typically, you will not need to implement -the data processing yourself since Qiskit Experiments has built-in -methods that determine the correct instance of ``DataProcessor`` for -your data. More advanced data processing includes, for example, handling -restless measurements [2, 3], see also the ``Restless Measurements`` tutorial. - -References -~~~~~~~~~~ - -[1] Thomas Alexander, Naoki Kanazawa, Daniel J. Egger, Lauren Capelluto, -Christopher J. Wood, Ali Javadi-Abhari, David McKay, Qiskit Pulse: -Programming Quantum Computers Through the Cloud with Pulses, Quantum -Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755 - -[2] Caroline Tornow, Naoki Kanazawa, William E. Shanks, Daniel J. Egger, -Minimum quantum run-time characterization and calibration via restless -measurements with dynamic repetition rates, Physics Review Applied **17**, -064061 (2022). https://arxiv.org/abs/2202.06981 - -[3] Max Werninghaus, Daniel J. Egger, Stefan Filipp, High-speed calibration and -characterization of superconducting quantum processors without qubit reset, -PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 \ No newline at end of file diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 4f430de95f..cefb047a1e 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -26,3 +26,188 @@ Uncertainties that arise from quantum measurements or finite sampling can be tak in the nodes: a standard error can be generated in a node and can be propagated through the subsequent nodes in the data processor. Correlation between computed values is also considered. + +Let's look at an example to see how to initialize an instance of ``DataProcessor`` and +create the ``DataAction`` nodes that process the data. + +Data types on IBM Quantum backends +---------------------------------- + +IBM Quantum backends can return different types of data. There is +counts data and IQ data [1], referred to as level 2 and level 1 data, +respectively. Level 2 data corresponds +to a dictionary with bit-strings as keys and the number of +times the bit-string was measured as a value. Importantly +for some experiments, the backends can return a lower data level +known as IQ data. Here, I and Q stand +for in phase and quadrature. The IQ are points in the complex plane +corresponding to a time integrated measurement signal which is +reflected or transmitted through the readout resonator depending +on the setup. IQ data can be returned as "single" or "averaged" data. +Here, single means that the outcome of each single shot is returned +while average only returns the average of the IQ points over the +measured shots. The type of data that an experiment should return +is specified by the ``run_options`` of an experiment. + +Processing data of different types +---------------------------------- + +An experiment should work with the different data levels. +Crucially, the analysis, such as a curve analysis, expects the +same data format no matter the run options of the experiment. +Transforming the data returned by the backend into the format +that the analysis accepts is done by the ``data_processing`` library. +The key class here is the ``DataProcessor``. It is initialized from +two arguments. The first, is the ``input_key`` which is typically +"memory" or "counts" and identifies the key in the experiment data +where the data is located. The second argument ``data_actions`` +is a list of ``nodes`` where each node performs a processing step +of the data processor. Crucially, the output of one node in the +list is the input to the next node in the list. + +To illustrate the data processing module we consider an example +in which we measure a rabi oscillation with different data levels. +The code below sets up the Rabi experiment. + + +.. jupyter-execute:: + + import numpy as np + + from qiskit import pulse + from qiskit.circuit import Parameter + + from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend + from qiskit_experiments.data_processing import DataProcessor, nodes + from qiskit_experiments.library import Rabi + + with pulse.build() as sched: + pulse.play( + pulse.Gaussian(160, Parameter("amp"), sigma=40), + pulse.DriveChannel(0) + ) + + backend = SingleTransmonTestBackend() + + exp = Rabi( + qubit=0, + backend=backend, + schedule=sched, + amplitudes=np.linspace(-0.1, 0.1, 21) + ) + +We now run the Rabi experiment twice, once with level 1 data and +once with level 2 data. Here, we manually configure two data +processors but note that typically you do not need to do this +yourself. We begin with single-shot IQ data. + +.. jupyter-execute:: + + data_nodes = [nodes.SVD(), nodes.AverageData(axis=1), nodes.MinMaxNormalize()] + iq_processor = DataProcessor("memory", data_nodes) + exp.analysis.set_options(data_processor=iq_processor) + + exp_data = exp.run(meas_level=1, meas_return="single").block_for_results() + + display(exp_data.figure(0)) + +Since we requested IQ data we set the input key to "memory" which is +the key under which the data is located in the experiment data. The +``iq_processor`` contains three nodes. The first node ``SVD`` is a +singular value decomposition which projects the two-dimensional IQ +data on its main axis. The second node averages the single-shot +data. The output is a single float per quantum circuit. Finally, +the last node ``MinMaxNormalize`` normalizes the measured signal to +the interval [0, 1]. The ``iq_dataprocessor`` is then set as an option +of the analysis class. For those who are wondering what single-shot IQ +data looks like we plot the data returned by the zeroth and sixth circuit +in the code block below. + +.. jupyter-execute:: + :hide-code: + :hide-output: + + %matplotlib inline + +.. jupyter-execute:: + + from qiskit_experiments.visualization import IQPlotter, MplDrawer + + plotter = IQPlotter(MplDrawer()) + + for idx in [0, 6]: + plotter.set_series_data( + f"Circuit {idx}", + points=np.array(exp_data.data(idx)["memory"]).squeeze(), + ) + + plotter.figure() + +Now we turn to counts data and see how the +data processor needs to be changed. + +.. jupyter-execute:: + + data_nodes = [nodes.Probability(outcome="1")] + count_processor = DataProcessor("counts", data_nodes) + exp.analysis.set_options(data_processor=count_processor) + + exp_data = exp.run(meas_level=2).block_for_results() + + display(exp_data.figure(0)) + +Now, the ``input_key`` is "counts" since that is the key under which the counts +data is saved in instances of ``ExperimentData``. The list of nodes +comprises a single data action which converts the counts to an estimation +of the probability of measuring the outcome "1". + +Writing data actions +-------------------- + +The nodes in a data processor are all sub-classes of ``DataAction``. +Users who wish to write their own data actions must (i) sub-class +``DataAction`` and (ii) implement the internal ``_process`` method +called by instances of ``DataProcessor``. This method is the +processing step that the node implements. It takes a numpy array as +input and returns the processed numpy array as output. This output +serves as the input for the next node in the data processing chain. +Here, the input and output numpy arrays can have a different shape. + +In addition to the standard ``DataAction`` the data processing package +also supports trainable data actions as subclasses of ``TrainableDataAction``. +These nodes must first be trained on the data before they can +process the data. An example of a ``TrainableDataAction`` is the +``SVD`` node which must first learn the main axis of the data before +it can project a data point onto this axis. To implement trainable nodes +developers must also implement the ``train`` method. This method is +called when ``DataProcessor.train`` is called. + +Conclusion +---------- + +In this tutorial you learnt about the data processing module in Qiskit +Experiments. Data is processed by data processors that +call a list of nodes each acting once on the data. Data +processing connects the data returned by the backend to the data that +the analysis classes need. Typically, you will not need to implement +the data processing yourself since Qiskit Experiments has built-in +methods that determine the correct instance of ``DataProcessor`` for +your data. More advanced data processing includes, for example, handling +restless measurements [2, 3], see also the ``Restless Measurements`` tutorial. + +References +~~~~~~~~~~ + +[1] Thomas Alexander, Naoki Kanazawa, Daniel J. Egger, Lauren Capelluto, +Christopher J. Wood, Ali Javadi-Abhari, David McKay, Qiskit Pulse: +Programming Quantum Computers Through the Cloud with Pulses, Quantum +Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755 + +[2] Caroline Tornow, Naoki Kanazawa, William E. Shanks, Daniel J. Egger, +Minimum quantum run-time characterization and calibration via restless +measurements with dynamic repetition rates, Physics Review Applied **17**, +064061 (2022). https://arxiv.org/abs/2202.06981 + +[3] Max Werninghaus, Daniel J. 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The framework itself consists of ``Experiment`` and ``Analysis`` classe latter of which uses the Data Processor, Curve Analysis, and Visualization modules to process the data, fit it to specified models, and plot the results, respectively. -.. figure:: ./images/experimentarch.png +.. figure:: images/experimentarch.png :width: 400 :align: center diff --git a/docs/tutorials/learning.rst b/docs/tutorials/learning.rst index 5dc45a2978..0dd1d67458 100644 --- a/docs/tutorials/learning.rst +++ b/docs/tutorials/learning.rst @@ -32,7 +32,9 @@ changes. Running your first experiment ============================= -Let's run a T1 experiment. First, we have to import the T1 experiment from the +Let's run a T1 experiment. This experiment estimates T1, the characteristic relaxation +time of a qubit from the excited state to the ground state, by measuring the +excited state population after varying delays. First, we have to import the T1 experiment from the Qiskit Experiments library: .. jupyter-execute:: @@ -119,7 +121,7 @@ upon experiment instantiation, but can also be explicitly set via :meth:`set_exp exp = T1(qubit=i, delays=delays) exp.set_experiment_options(delays=new_delays) -Consult the :doc:`API documentation ` for the options of each experiment class. +Consult the :doc:`API documentation ` for the options of each experiment class. Analysis options ---------------- @@ -134,7 +136,7 @@ but via analysis instead: backend=backend) exp.analysis.set_options(gate_error_ratio=None) -Consult the :doc:`API documentation ` for the options of each +Consult the :doc:`API documentation ` for the options of each experiment's analysis class. Running experiments on multiple qubits @@ -143,9 +145,10 @@ Running experiments on multiple qubits To run experiments across many qubits of the same device, we use **composite experiments**. There are two core types of composite experiments: -* **Parallel experiments** run across qubits simultaneously. The circuits cannot overlap in - qubits used. -* **Batch experiments** run consecutively in time. These circuits can overlap in qubits. +* **Parallel experiments** run across qubits simultaneously as set by the user. New circuits + are constructed that parallelize circuits of the sub-experiments to be executed simultaneously. + Therefore, the sub-circuits cannot overlap in qubits used. +* **Batch experiments** run consecutively in time. These circuits can overlap in qubits used. Here's an example of measuring :math:`T_1` of two qubits on the same device simultaneously in a parallel experiment: @@ -158,19 +161,19 @@ in a parallel experiment: parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)]) parallel_exp.set_transpile_options(scheduling_method='asap') parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() - - # View result data - for result in parallel_data.analysis_results(): - print(result) -Parallel and batch experiments can be nested arbitrarily to make +Parallel and batch experiments can be nested arbitrarily to make complex composite +experiments. + +.. figure:: ./images/compositeexperiments.png + :align: center Viewing sub experiment data =========================== -The experiment data returned from a composite experiment also contains -individual experiment data for each sub experiment, which can be accessed -using ``child_data``. +The experiment data returned from a composite experiment contains +individual analysis results for each sub experiment that can be accessed +using ``child_data``. By default, the parent data object does not contain analysis results. .. jupyter-execute:: @@ -181,6 +184,16 @@ using ``child_data``. for result in sub_data.analysis_results(): print(result) -You can use the `flatten_results` flag to flatten the results of all -component experiments into one level. +If you want the parent data object to contain the analysis results instead, +you can set the ``flatten_results`` flag to true to flatten the results of all component +experiments into one level: + +.. jupyter-execute:: + parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)], + flatten_results=True) + parallel_exp.set_transpile_options(scheduling_method='asap') + parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() + + for result in parallel_data.analysis_results(): + print(result) \ No newline at end of file diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index 1468280f2d..86f4c6571c 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -1,9 +1,19 @@ -=============================== Visualization: Creating figures =============================== The Visualization module provides plotting functionality for creating figures from experiment and analysis results. -This includes plotter and drawer classes to plot data in :py:class:`CurveAnalysis` and its subclasses. +This includes `plotter` and `drawer` classes to plot data in :py:class:`CurveAnalysis` and its subclasses. +Plotters define the kind of figures to plot, while drawers are backends that enable them to be visualized. + +How much you will interact directly with the visualization module depends on your use case: + +- **Running library experiments as-is:** You won't need to interact with the visualization module. +- **Running library experiments with custom styling for figures**: You will be setting figure options through the plotter. +- **Making plots using a plotting library other than Matplotlib**: You will need to define a custom drawer class. +- **Writing your own analysis class**: You don't need to interact with the visualization module. + if you want to use the the default plotter and drawer settings, or you can customize them + your plotter and drawer. + Plotters inherit from :class:`BasePlotter` and define a type of figure that may be generated from experiment or analysis data. For example, the results from :class:`CurveAnalysis` --- or any other experiment where results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted @@ -12,71 +22,248 @@ using the :class:`CurvePlotter` class, which plots X-Y-like values. These plotter classes act as a bridge (from the common bridge pattern in software development) between analysis classes (or even users) and plotting backends such as Matplotlib. Drawers are the backends, with a common interface defined in :class:`BaseDrawer`. Though Matplotlib is the only officially supported -plotting backend in Qiskit Experiments (i.e., through :class:`MplDrawer`), custom drawers can be +plotting backend in Qiskit Experiments through :class:`MplDrawer`, custom drawers can be implemented by users to use alternative backends. As long as the backend is a subclass of :class:`BaseDrawer`, and implements all the necessary functionality, all plotters should be able to generate figures with the alternative backend. -Data to feed into the module is split into `series` and `supplementary`: - -- **Series Data**: Values from experiment data or analysis instances, or values to be plotted as points, - lines, etc. A good rule-of-thumb: if it could have a legend entry, it's series data. -- **Supplementary Data**: Values unrelated to a series or curve, only related to the figure. Examples - include fit-reports, figure-wide text, or metadata. - -You can think of the structure of series and supplementary data as dictionaries with the data-keys and -series-names. This is an example for CurvePlotter, representing the dummy data we generated. - -.. code-block:: python - - series_data = { - "A": { # Series-name 'A' - "x": ..., # Data for data-key 'x' and series 'A' - "y": ..., - "x_interp": ..., - "y_interp": ..., - "y_interp_err": ..., - }, - "B": { # Series-name 'B' - "x": ..., # Data for data-key 'x' and series 'B' - "y": ..., - "x_interp": ..., - "y_interp": ..., - } - } +Generating and customizing a figure using a plotter +--------------------------------------------------- + +First, we display the default figure from a :class:`Rabi` experiment as a starting point: + +.. jupyter-execute:: + + import numpy as np + + from qiskit import pulse + from qiskit.circuit import Parameter + + from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend + from qiskit_experiments.data_processing import DataProcessor, nodes + from qiskit_experiments.library import Rabi + + with pulse.build() as sched: + pulse.play( + pulse.Gaussian(160, Parameter("amp"), sigma=40), + pulse.DriveChannel(0) + ) + + backend = SingleTransmonTestBackend() + + rabi = Rabi( + qubit=0, + backend=backend, + schedule=sched, + amplitudes=np.linspace(-0.1, 0.1, 21) + ) + + rabi_data = rabi.run().block_for_results() + rabi_data.figure(0) + +This is the default figure generated by :class:`OscillationAnalysis`, the data analysis +class for the Rabi experiment. The fitted cosine is shown as a blue line, with the +individual measurements from the experiment shown as data points with error bars corresponding +to their uncertainties. We are also given a small fit report in the caption showing the +``rabi_rate``. + +The plotter that generated the figure can be accessed through the analysis instance, +and customizing the figure can be done by setting the plotter's options. We now modify +the color, symbols, and size of our plot, as well as change the axis labels for the amplitude units: + +.. jupyter-execute:: + :hide-code: + :hide-output: + + %matplotlib inline + +.. jupyter-execute:: + + # Retrieve the plotter from the analysis instance + plotter = rabi.analysis.plotter + + # Change the x-axis unit values + plotter.set_figure_options( + xval_unit="arb.", + xval_unit_scale=False # Don't scale the unit with SI prefixes + ) + + # Change the color and symbol for the cosine + plotter.figure_options.series_params.update( + {"cos": {"symbol": "x", "color": "r"}} + ) + + # Set figsize directly so we don't overwrite the entire style + plotter.options.style["figsize"] = (6,4) -.. code-block:: python + # Generate the new figure + plotter.figure() - supplementary_data = { - "primary_results": ..., # Supplementary data only has a data-key, no series-name. - "fit_red_chi": ..., - } +Plotters have two sets of options that customize their behavior and figure content: +``options``, which have class-specific parameters that define how an instance behaves, +and ``figure_options``, which have figure-specific parameters that control aspects of the +figure itself, such as axis labels and series colors. +Here is a more complicated experiment in which we customize the figure of a DRAG +experiment before it's run, so that we don't need to regenerate the figure like in +the previous example: -Data consists of the ``{'y_interp_err', 'x', 'x_interp', 'y_interp', 'y'}`` keys. +.. jupyter-execute:: + from qiskit_experiments.library import RoughDrag + from qiskit_experiments.visualization import PlotStyle -Generating a figure using a plotter -=================================== + beta = Parameter("beta") + with pulse.build(name="xp") as xp: + pulse.play(pulse.Drag(64, 0.66, 16, beta), pulse.DriveChannel(0)) -.. code-block:: python + drag = RoughDrag(0, xp, backend=backend) - # Create plotter and set options and style. - plotter = CurvePlotter(MplDrawer()) - plotter.set_options( - plot_sigma=[ - (1.0, 0.5) - ], # Controls confidence-intervals for `y_interp_err` data-keys. + # Set plotter options + plotter = drag.analysis.plotter + + # Update series parameters + plotter.figure_options.series_params.update( + { + "nrep=1": { + "color": (27 / 255, 158 / 255, 119 / 255), + "symbol": "^", + }, + "nrep=3": { + "color": (217 / 255, 95 / 255, 2 / 255), + "symbol": "s", + }, + "nrep=5": { + "color": (117 / 255, 112 / 255, 179 / 255), + "symbol": "o", + }, + } ) + + # Set figure options plotter.set_figure_options( - series_params={ - "A": {"symbol": "o", "color": "C0", "label": "Qubit 0"}, - "B": {"symbol": "X", "color": "C1", "label": "Qubit 1"}, - "C": {"symbol": "v", "color": "k", "label": "Ideal 0"}, - "D": {"symbol": "^", "color": "k", "label": "Ideal 1"}, - }, - xlabel="Parameter", - ylabel="${\\langle{}Z\\rangle{}}$", - figure_title="Expectation Values", + xval_unit="arb.", + xval_unit_scale=False, + figure_title="Rough DRAG Experiment on Qubit 0", ) + # Set style parameters + plotter.options.style["symbol_size"] = 10 + plotter.options.style["legend_loc"] = "upper center" + + drag_data = drag.run().block_for_results() + + drag_data.figure(0) + +As can be seen in the figure, the different series generated by the experiment +were styled differently according to the ``series_params`` attribute of ``figure_options``. + + +Customizing plotting in your experiment +--------------------------------------- + +Plotters are easily integrated into custom analysis classes. To add a plotter instance +to such a class, we define a new ``plotter`` property, pass it relevant data in the +analysis class's ``_run_analysis`` method, and return the generated figure alongside our +analysis results. We use the ``IQPlotter`` class to illustrate how this is done for an arbitrary analysis class. + +To ensure that we have an interface simlar to existing analysis classes, we make our plotter +accessible as an ``analysis.plotter`` property and analysis.options.plotter option. +The code below accomplishes this for our example ``MyIQAnalysis`` analysis class. We +set the drawer to MplDrawer to use Matplotlib by default. The plotter property of our +analysis class makes it easier to access the plotter instance; i.e., using ``self.plotter`` + and ``analysis.plotter``. We set default options and figure options in + ``_default_options``, but you can still override them as we did above. + +The ``MyIQAnalysis`` class accepts single-shot level 1 IQ data, which consists of an +in-phase and quadrature measurement for each shot and circuit. ``_run_analysis`` is +passed an ``ExperimentData`` instance which contains IQ data as a list of dictionaries +(one per circuit) where their "memory" entries are lists of IQ values (one per shot). +Each dictionary has a "metadata" entry, with the name of a prepared state: "0", "1", +or "2". These are our series names. + +Our goal is to create a figure that displays the single-shot IQ values of each +prepared-state (one per circuit). We process the "memory" data passed to the +analysis class and set the points and centroid series data in the plotter. +This is accomplished in the code below, where we also train a discriminator +to label the IQ points as one of the three prepared states. IQPlotter supports +plotting a discriminator as optional supplementary data, which will show predicted +series over the axis area. + +.. jupyter-execute:: + + from qiskit_experiments.framework import BaseAnalysis, Options + from qiskit_experiments.visualization import ( + BasePlotter, + IQPlotter, + MplDrawer, + PlotStyle, + ) + + class MYIQAnalysis(BaseAnalysis): + @classmethod + def _default_options(cls) -> Options: + options = super()._default_options() + # We create the plotter and create an option for it. + options.plotter = IQPlotter(MplDrawer()) + options.plotter.set_figure_options( + xlabel="In-phase", + ylabel="Quadrature", + figure_title="My IQ Analysis Figure", + series_params={ + "0": {"label": "|0>"}, + "1": {"label": "|1>"}, + "2": {"label": "|2>"}, + }, + ) + return options + + @property + def plotter(self) -> BasePlotter: + return self.options.plotter + + def _run_analysis(self, experiment_data): + data = experiment_data.data() + analysis_results = [] + for datum in data: + # Analysis code + analysis_results.append(self._analysis_result(datum)) + + # Plotting code + series_name = datum["metadata"]["name"] + points = datum["memory"] + centroid = np.mean(points, axis=0) + self.plotter.set_series_data( + series_name, + points=points, + centroid=centroid, + ) + + # Add discriminator to IQPlotter + discriminator = self._train_discriminator(data) + self.plotter.set_supplementary_data(discriminator=discriminator) + + return analysis_results, [self.plotter.figure()] + +If we run the above analysis on some appropriate experiment data, as previously +described, our class will generate a figure showing IQ points and their centroids. + +Creating your own plotter +------------------------- + +You can create a custom figure plotter by subclassing ``BasePlotter`` and overriding +``expected_series_data_keys``, ``expected_supplementary_data_keys``, and ``_plot_figure``. + +The first two methods allow you to define a list of supported data-keys, +as strings, which identify the different data to plot. The third method, +``_plot_figure``, must contain your code to generate a figure by calling methods +on the plotter's drawer instance (self.drawer). When ``plotter.figure()`` is called +by an analysis class, the plotter calls _plot_figure and then returns your figure +object which is added to the experiment data instance. It is also good practice to +set default values for figure options, such as axis labels. You can do this by +overriding the ``_default_figure_options`` method in your plotter subclass. + +See also +-------- + +API documentation: :doc:`qiskit_experiments.visualization` \ No newline at end of file diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 5f4b9cb000..cfadbd07ac 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -92,7 +92,7 @@ are usually run with a :py:class:`~qiskit_experiments.calibration_management.Calibrations` class instance to manage parameters and pulse schedules. -See :doc:`/tutorials/calibrating_real_device` for example. +See :doc:`/tutorials/calibrations` for examples. .. autosummary:: :toctree: ../stubs/ diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index 8c190f22bf..55ea762bbd 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -71,7 +71,7 @@ class RoughDrag(BaseExperiment, RestlessMixin): .. ref_arxiv:: 3 1509.05470 # section: tutorial - :doc:`/tutorials/calibrating_real_device` + :doc:`/tutorials/calibrations` """ diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index 4580392771..72e9074d83 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -87,7 +87,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): .. ref_arxiv:: 1 1504.06597 # section: tutorial - :doc:`/tutorials/fine_calibrations` + :doc:`/tutorials/calibrations` """ From 98c68306c346107d87754d88dd2aeae15fb49c54 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Feb 2023 23:32:05 -0500 Subject: [PATCH 20/64] requirement changes and minor edits --- docs/guides/randomized_benchmarking.rst | 4 ++-- docs/guides/readout_mitigation.rst | 10 ++++------ docs/howtos/cloud_service.rst | 4 ++-- requirements-dev.txt | 5 ++--- 4 files changed, 10 insertions(+), 13 deletions(-) diff --git a/docs/guides/randomized_benchmarking.rst b/docs/guides/randomized_benchmarking.rst index 411196361c..38621bb2b2 100644 --- a/docs/guides/randomized_benchmarking.rst +++ b/docs/guides/randomized_benchmarking.rst @@ -335,7 +335,7 @@ Rothwell, Thomas A. Ohki, Mark B. Ketchen, M. Steffen, *Efficient measurement of quantum gate error by interleaved randomized benchmarking*, https://arxiv.org/pdf/1203.4550 -[5] Jay M. Gambetta, A. D. C´orcoles, S. T. Merkel, B. R. Johnson, John +[5] Jay M. Gambetta, A. D. Córcoles, S. T. Merkel, B. R. Johnson, John A. Smolin, Jerry M. Chow, Colm A. Ryan, Chad Rigetti, S. Poletto, Thomas A. Ohki, Mark B. Ketchen, and M. Steffen, *Characterization of addressability by simultaneous randomized benchmarking*, @@ -345,5 +345,5 @@ See also -------- * API documentation: :mod:`qiskit_experiments.library.randomized_benchmarking` -* `Qiskit Textbook `__ +* Qiskit Textbook: `Randomized Benchmarking `__ diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/readout_mitigation.rst index d7f518f11d..785d156867 100644 --- a/docs/guides/readout_mitigation.rst +++ b/docs/guides/readout_mitigation.rst @@ -19,15 +19,13 @@ are independent of each other. In this case, the assignment matrix is the tensor product of :math:`n` :math:`2 \times 2` matrices, one for each qubit, making it practical to store the assignment matrix in implicit form, by storing the individual :math:`2 \times 2` assignment -matrices. The corresponding class in Qiskit is the `Local readout -mitigator `__ -in ``qiskit-terra``. +matrices. The corresponding class in Qiskit is the +:class:`~qiskit.result.LocalReadoutMitigator` in Qiskit Terra. A *Correlated readout mitigator* uses the full :math:`2^n \times 2^n` assignment matrix, meaning it can only be used for small values of -:math:`n`. The corresponding class in Qiskit is the `Correlated readout -mitigator `__ -in ``qiskit-terra``. +:math:`n`. The corresponding class in Qiskit is the +:class:`~qiskit.result.CorrelatedReadoutMitigator` in Qiskit Terra. This notebook demonstrates the usage of both the local and correlated experiments to generate the corresponding mitigators. diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index e10f5c83d9..00355f9bbe 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -3,8 +3,8 @@ How to save and load experiment data with the cloud service .. note:: This recipe is only for those who have access to the cloud service. You can - check whether you have access by logging into the Quantum Cloud interface - and seeing whether you can access the `database `__. + check whether you do by logging into the IBM Quantum interface + and seeing if you can see the `database `__. Problem ------- diff --git a/requirements-dev.txt b/requirements-dev.txt index 4b4c1ebf82..6927da391c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -4,12 +4,11 @@ astroid==2.5 pylint==2.7.1 jinja2==3.0.3 sphinx==5.2.3 -qiskit-sphinx-theme>=1.10.1 -sphinx-autodoc-typehints +qiskit-sphinx-theme>=1.10.2 +sphinx-autodoc-typehints<=1.20.2 sphinx-design pygments>=2.4 reno>=3.4.0 -sphinx-panels nbsphinx arxiv ddt>=1.6.0 From 6b7fcd606078441e452dacf2c193233b2c6ccdbc Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 14 Feb 2023 08:06:42 -0500 Subject: [PATCH 21/64] doc updates --- docs/_ext/custom_styles/utils.py | 2 +- docs/conf.py | 2 +- docs/guides/randomized_benchmarking.rst | 8 +- docs/guides/state_tomography.rst | 2 +- docs/guides/t2hahn_characterization.rst | 59 ++-- docs/howtos/newexperimentdata.rst | 7 - docs/tutorials/curve_analysis.rst | 39 +-- docs/tutorials/custom_experiment.rst | 34 ++- docs/tutorials/data_processor.rst | 50 ++-- docs/tutorials/getting_started.rst | 269 ++++++++++++++++++ docs/tutorials/index.rst | 18 +- docs/tutorials/intro.rst | 15 +- docs/tutorials/learning.rst | 199 ------------- docs/tutorials/visualization.rst | 59 +++- qiskit_experiments/__init__.py | 3 + qiskit_experiments/framework/__init__.py | 8 +- .../correlated_readout_error.py | 3 +- .../library/characterization/t1.py | 6 +- 18 files changed, 443 insertions(+), 340 deletions(-) create mode 100644 docs/tutorials/getting_started.rst delete mode 100644 docs/tutorials/learning.rst diff --git a/docs/_ext/custom_styles/utils.py b/docs/_ext/custom_styles/utils.py index f86cd250d7..bee6be7ecc 100644 --- a/docs/_ext/custom_styles/utils.py +++ b/docs/_ext/custom_styles/utils.py @@ -181,7 +181,7 @@ def _format_default_options(defaults: Dict[str, Any], indent: str = "") -> List[ if not defaults: docstring_lines.append(indent + "No default options are set.") else: - docstring_lines.append(indent + "Following values are set by default.") + docstring_lines.append(indent + "The following values are set by default.") docstring_lines.append("") docstring_lines.append(indent + ".. parsed-literal::") docstring_lines.append("") diff --git a/docs/conf.py b/docs/conf.py index 40f3d64898..52e63c8db9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -94,7 +94,6 @@ "jupyter_sphinx", "sphinx_autodoc_typehints", "reno.sphinxext", - "sphinx_panels", "sphinx_design", "sphinx.ext.intersphinx", "nbsphinx", @@ -195,6 +194,7 @@ intersphinx_mapping = { "matplotlib": ("https://matplotlib.org/stable/", None), "qiskit": ("https://qiskit.org/documentation/", None), + "uncertainties": ("https://pythonhosted.org/uncertainties", None), } diff --git a/docs/guides/randomized_benchmarking.rst b/docs/guides/randomized_benchmarking.rst index 38621bb2b2..a97355ce2e 100644 --- a/docs/guides/randomized_benchmarking.rst +++ b/docs/guides/randomized_benchmarking.rst @@ -64,7 +64,7 @@ Standard RB experiment will provide you gate errors for every basis gates constituting averaged Clifford gate. Note that you can only obtain a single EPC value :math:`\cal E` from a single RB experiment. As such, computing the error values for multiple gates :math:`\{g_i\}` requires some assumption of contribution of each gate to the total depolarizing error. -This is so called ``gate_error_ratio`` option you can find in analysis options. +This is the so called ``gate_error_ratio`` option you can find in analysis options. Provided that we have :math:`n_i` gates with independent error :math:`e_i` per Clifford, the total EPC is estimated by the composition of error from every basis gate, @@ -83,14 +83,14 @@ some standard value :math:`e_0`, we can compute EPG :math:`e_i` for each basis g {\cal E} \sim e_0 \sum_{i} n_i r_i -The EPG of :math:`i` th basis gate will be +The EPG of the :math:`i` th basis gate will be .. math:: e_i \sim r_i e_0 = \dfrac{r_i{\cal E}}{\sum_{i} n_i r_i}. Because EPGs are computed based on this simple assumption, -this is not necessary representing the true gate error on the hardware. +this is not necessarily representing the true gate error on the hardware. If you have multiple kinds of basis gates with unclear error ratio :math:`r_i`, interleaved RB experiment will always give you accurate error value :math:`e_i`. @@ -344,6 +344,6 @@ https://arxiv.org/pdf/1204.6308 See also -------- -* API documentation: :mod:`qiskit_experiments.library.randomized_benchmarking` +* API documentation: :mod:`~qiskit_experiments.library.randomized_benchmarking` * Qiskit Textbook: `Randomized Benchmarking `__ diff --git a/docs/guides/state_tomography.rst b/docs/guides/state_tomography.rst index 7b7c1cd368..0d947d7248 100644 --- a/docs/guides/state_tomography.rst +++ b/docs/guides/state_tomography.rst @@ -159,7 +159,7 @@ For example if we want to perform 1-qubit QST on several qubits at once: for i in range(num_qubits)] subexps = [ - StateTomography(gate, qubits=[i]) + StateTomography(gate, physical_qubits=[i]) for i, gate in enumerate(gates) ] parexp = ParallelExperiment(subexps) diff --git a/docs/guides/t2hahn_characterization.rst b/docs/guides/t2hahn_characterization.rst index 0b420cd7ae..a7f340e765 100644 --- a/docs/guides/t2hahn_characterization.rst +++ b/docs/guides/t2hahn_characterization.rst @@ -1,30 +1,30 @@ T2 Hahn Characterization (CPMG) =============================== -The purpose of the :math:`T_2` Hahn Echo experiment is to determine +The purpose of the :math:`T_2` Hahn Echo experiment is to determine the :math:`T_2` qubit property. In this experiment, we would like to get a more precise estimate of the qubit’s decay time. :math:`T_2` represents the amount of time required -for a single qubit Bloch vector projection on the XY plane, to fall to -approximately 37% (:math:`\frac{1}{e}`) of its initial amplitude. In -Ramsey Experiment we were introduced to the term detuning frequency (The +for a single qubit's Bloch vector projection on the XY plane to fall to +approximately 37% (:math:`\frac{1}{e}`) of its initial amplitude. In the +Ramsey Experiment, we were introduced to the term detuning frequency (The difference between the frequency used for the control rotation, and the -precise frequency). Hahn Echo experiment and CPMG sequence are +precise frequency). Hahn Echo experiment and the CPMG sequence are experiments to estimate :math:`T_2` which are robust to the detuning frequency. The decay in amplitude causes the probability function to take the following form: .. math:: f(t) = A \cdot e^{-\frac{t}{T_2}}+ B -The difference between Hahn Echo and CPMG sequence is that in Hahn Echo +The difference between the Hahn Echo and CPMG sequence is that in the Hahn Echo experiment, there is only one echo sequence while in CPMG there are multiple echo sequences. -1. Decoherence Time -------------------- +Decoherence Time +---------------- -Decoherence time is the time taken for off-diagonal components of the +The decoherence time is the time taken for off-diagonal components of the density matrix to fall to approximately 37% (:math:`\frac{1}{e}`). For :math:`t\gg T_2`, the qubit statistics behave like a random bit. It gets the value of ``0`` with probability of :math:`p` and the value of ``1`` @@ -42,13 +42,13 @@ for the qubit frequency estimation. The circuit used for an experiment with :math:`N` echoes comprises the following components: -1. :math:`Rx\left(\frac{\pi}{2} \right)` gate -2. :math:`N` times echo sequence: - (a) :math:`Delay \left(t_{0} \right)` gate - (b) :math:`Rx \left(\pi \right)` gate - (c) :math:`Delay \left(t_{0} \right)` gate -3. :math:`Rx \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the number of echoes) -4. Measurement gate + 1. :math:`Rx\left(\frac{\pi}{2} \right)` gate + 2. :math:`N` times echo sequence: + (a) :math:`Delay \left(t_{0} \right)` gate + (b) :math:`Rx \left(\pi \right)` gate + (c) :math:`Delay \left(t_{0} \right)` gate + 3. :math:`Rx \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the number of echoes) + 4. Measurement gate The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the @@ -72,7 +72,7 @@ and can analytically extract the desired values. print(exp1.circuits()[0]) -We run the experiment on a simple, simulated backend, tailored +We run the experiment on a simple simulated backend tailored specifically for this experiment. .. jupyter-execute:: @@ -109,8 +109,8 @@ The resulting graph will have the form: print(result) -1. Providing initial user estimates -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Providing initial user estimates +-------------------------------- The user can provide initial estimates for the parameters to help the analysis process. In the initial guess, the keys ``{amp, tau, base}`` @@ -141,8 +141,8 @@ computed for other qubits. -3. Number of echoes -~~~~~~~~~~~~~~~~~~~ +Number of echoes +---------------- The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of @@ -164,9 +164,12 @@ total delay time. # set the desired delays conversion_factor = 1e-6 - # The delays aren't equally spaced due the behavior of exponential decay curve where the change in the result - # in earlier times is larger than later times. In addition, since the total delay is 'delay * 2 * num_of_echoes', - # the construction of the delays for each experiment will be different, such that their total length will be the same. + # The delays aren't equally spaced due the behavior of the exponential + # decay curve where the change in the result during earlier times is + # larger than later times. In addition, since the total delay is + # 'delay * 2 * num_of_echoes', the construction of the delays for + # each experiment will be different such that their total length + # will be the same. # Delays for Hahn Echo Experiment with 0 echoes delays2 = np.append( @@ -231,9 +234,9 @@ total delay time. We see that the estimate :math:`T_2` is different in the two plots. The mock backend for this experiment used :math:`T_{2} = 30[\mu s]`, which -is close to the estimate of the 1 echo experiment. +is close to the estimate of the one echo experiment. -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :class:`.T2Hahn` \ No newline at end of file diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst index 6a1c282ab0..4b2ebc07cb 100644 --- a/docs/howtos/newexperimentdata.rst +++ b/docs/howtos/newexperimentdata.rst @@ -30,14 +30,7 @@ ran and its options, as well as the IDs of the jobs that were executed. ``data`` will be the new experiment data class. -Discussion ----------- - -The job IDs can be retrieved from the original experiment data object using the -``job_ids`` attribute. - See Also -------- -* `Saving and loading experiment data locally `_ * `Saving and loading experiment data with the cloud service `_ diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 24bdf4e233..f35899f177 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -3,17 +3,20 @@ Curve Analysis: Fitting your data .. currentmodule:: qiskit_experiments.curve_analysis -Curve analysis provides the analysis base class for a variety of experiments with +For most experiments, we are interested in fitting our results to a pre-defined +mathematical model. +The Curve Analysis module provides the analysis base class for a variety of experiments with a single experimental parameter sweep. This analysis subclasses can override several class attributes to customize the behavior from data processing to post-processing, including providing systematic initial guess for parameters tailored to the experiment. -Here we describe how code developers can create new analysis that inherits from the base class. +Here we describe how the Curve Analysis module works and how you can create new +analyses that inherits from the base class. .. _curve_analysis_overview: -Curve Analysis Overview -======================= +Curve analysis overview +----------------------- The base class :class:`CurveAnalysis` implements the multi-objective optimization on different sets of experiment results. A single experiment can define sub-experiments @@ -91,21 +94,21 @@ More specifically, the curve analysis defines following data model. fit parameters. Fit functions in the group are simultaneously fit to generate a single fit result. -Once the group is assigned, a curve analysis instance internally builds -a proper optimization routine. +Once the group is assigned, a curve analysis instance builds +a proper internal optimization routine. Finally, the analysis outputs a set of :class:`AnalysisResultData` entries for important fit outcomes along with a single Matplotlib figure of the fit curves with the measured data points. -With this baseclass a developer can avoid writing boilerplate code in -various curve analyses subclass and one can quickly write up +With this baseclass, a developer can avoid writing boilerplate code in +various curve analyses subclass and can quickly write up the analysis code for a particular experiment. .. _curve_analysis_define_group: Defining New Group -================== +------------------ The fit model is defined by the `LMFIT`_ ``Model``. If you are familiar with this package, you can skip this section. @@ -206,7 +209,7 @@ with different trigonometric functions. .. _curve_analysis_fixed_param: Fitting with Fixed Parameters -============================= +----------------------------- You can also remain certain parameters unchanged during the fitting by specifying the parameter names in the analysis option ``fixed_parameters``. @@ -258,13 +261,13 @@ every logic defined in the :class:`AnalysisA`. .. _curve_analysis_workflow: Curve Analysis Workflow -======================= +----------------------- Typically curve analysis performs fitting as follows. This workflow is defined in the method :meth:`CurveAnalysis._run_analysis`. 1. Initialization ------------------ +^^^^^^^^^^^^^^^^^ Curve analysis calls :meth:`_initialization` method where it initializes some internal states and optionally populate analysis options @@ -274,7 +277,7 @@ or dynamically generate the fit models (``self._models``) with fresh analysis op A developer can override this method to perform initialization of analysis-specific variables. 2. Data processing ------------------- +^^^^^^^^^^^^^^^^^^ Curve analysis calls :meth:`_run_data_processing` method where the data processor in the analysis option is internally called. @@ -288,7 +291,7 @@ A developer can inject extra data processing, for example, filtering, smoothing, or elimination of outliers for better fitting. 3. Fitting ----------- +^^^^^^^^^^ Curve analysis calls :meth:`_run_curve_fit` method which is the core functionality of the fitting. The another method :meth:`_generate_fit_guesses` is internally called to @@ -300,7 +303,7 @@ A developer can also override the entire :meth:`_run_curve_fit` method to apply custom fitting algorithms. This method must return :class:`.CurveFitResult` dataclass. 4. Post processing ------------------- +^^^^^^^^^^^^^^^^^^ Curve analysis runs several postprocessing against to the fit outcome. It calls :meth:`_create_analysis_results` to create :class:`AnalysisResultData` class @@ -316,7 +319,7 @@ Finally, it returns the list of created analysis results and Matplotlib figure. .. _curve_analysis_init_guess: Providing Initial Guesses -========================= +------------------------- When fit is performed without any prior information of parameters, it usually falls into unsatisfactory result. @@ -376,7 +379,7 @@ This allows you to avoid analysis failure with the poor initial guesses. .. _curve_analysis_quality: Evaluate Fit Quality -==================== +-------------------- A subclass can override :meth:`_evaluate_quality` method to provide an algorithm to evaluate quality of the fitting. @@ -389,7 +392,7 @@ Qiskit Experiments often uses the empirical criterion chi-squared < 3 as a good .. _curve_analysis_results: Curve Analysis Results -====================== +---------------------- Once the best fit parameters are found, the :meth:`_create_analysis_results` method is called with the same :class:`.CurveFitResult` object. diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 744f370496..5e603d9eee 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -4,7 +4,7 @@ Writing a custom experiment Qiskit Experiments is designed to be easily customizable. To create an experiment subclass based on either the :class:`.BaseExperiment` class or an existing experiment, you should: -- Implement the abstract :meth:`.BaseExperiment.circuits` method. +- Implement the abstract :meth:`~.BaseExperiment.circuits` method. This should return a list of ``QuantumCircuit`` objects defining the experiment payload. @@ -15,8 +15,8 @@ based on either the :class:`.BaseExperiment` class or an existing experiment, yo Arguments in the constructor can be overridden so that a subclass can be initialized with some experiment configuration. -Optionally the following methods can also be overridden in the subclass to -allow configuring various experiment and execution options +Optionally, the following methods can also be overridden in the subclass to +allow configuring various experiment and execution options: - :meth:`.BaseExperiment._default_experiment_options` to set default values for configurable option parameters for the experiment. @@ -31,7 +31,7 @@ allow configuring various experiment and execution options - :meth:`.BaseExperiment._default_analysis_options` to set default values for configurable options for the experiment's analysis class. Note that these should generally be set by overriding the :class:`.BaseAnalysis` - method :meth:`.BaseAnalysis._default_options` instead of this method, except in the + method :meth:`~.BaseAnalysis._default_options` instead of this method, except in the case where the experiment requires different defaults to the used analysis class. - :meth:`.BaseExperiment._transpiled_circuits` @@ -46,12 +46,12 @@ immediately after the previous measurement. Here, the :class:`.RestlessMixin` ca to set the appropriate run options and data processing chain. Analysis Subclasses -^^^^^^^^^^^^^^^^^^^ +------------------- To create an analysis subclass, one only needs to implement the abstract :meth:`.BaseAnalysis._run_analysis` method. This method takes an :class:`.ExperimentData` container and kwarg analysis options. If any -kwargs are used the :meth:`.BaseAnalysis._default_options` method should be +kwargs are used, the :meth:`.BaseAnalysis._default_options` method should be overriden to define default values for these options. The :meth:`.BaseAnalysis._run_analysis` method should return a pair @@ -59,7 +59,7 @@ The :meth:`.BaseAnalysis._run_analysis` method should return a pair :class:`.AnalysisResultData` and ``figures`` is a list of :class:`matplotlib.figure.Figure`. -The :mod:`qiskit_experiments.data_processing` module contains classes for +The :mod:`~qiskit_experiments.data_processing` module contains classes for building data processor workflows to help with advanced analysis of experiment data. @@ -80,7 +80,7 @@ These provided subclasses focus on the 0 <-> 1 transition, however, this experim Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. -In order to do this, we need to create a subclass, shown below. +In order to do this, we need to create a subclass as shown below. .. code-block:: @@ -108,13 +108,13 @@ In order to do this, we need to create a subclass, shown below. In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate on higher energy transitions by using an initial X gate to populate the first excited state. Using the Subclass -================== +------------------ Now, we can use our new subclass as we would the original parent class. -Shown below are results from following the fine amplitude calibration -tutorial :ref:`fine-amplitude-cal` for detecting an over-rotated pulse using our new +Shown below are results from following the :ref:`fine-amplitude-cal` tutorial +for detecting an over-rotated pulse using our new ``HigherOrderFineXAmplitude`` class in place of the original -``FineXAmplitude`` class. +:class:`.FineXAmplitude` class. You can try this for yourself and verify that your results are similar. .. code-block:: @@ -128,13 +128,13 @@ You can try this for yourself and verify that your results are similar. - verified: False Writing a custom experiment -=========================== +--------------------------- -In this tutorial, we'll use what we've learned so far to make a full experiment from +Now we'll use what we've learned so far to make a full custom experiment from the :class:`.BaseExperiment` template. A randomized measurement experiment -=================================== +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This experiment creates a list of copies of an input circuit and randomly samples an N-qubit Pauli to apply to each one before @@ -144,9 +144,7 @@ ideal output bitstring in the measurement. The analysis uses the applied Pauli frame of a randomized measurement experiment to de-randomize the measured counts and combine across samples to return a single counts dictionary -the original circuit. - -This has the effect of Pauli-twirling and symmetrizing the +the original circuit. This has the effect of Pauli-twirling and symmetrizing the measurement readout error. .. jupyter-execute:: diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index cefb047a1e..651bae61f1 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -5,10 +5,8 @@ Data processing is the act of taking the data returned by the backend and converting it into a format that can be analyzed. It is implemented as a chain of data processing steps that transform various input data, e.g. IQ data, into a desired format, e.g. population, which can be analyzed. - These data transformations may consist of multiple steps, such as kerneling and discrimination. -Each step is implemented by a :class:`~qiskit_experiments.data_processing.data_action.DataAction` -also called a `node`. +Each step is implemented by a member of the :class:`~.DataAction` class, also called a `node`. The data processor implements the :meth:`__call__` method. Once initialized, it can thus be used as a standard python function: @@ -27,8 +25,8 @@ in the nodes: a standard error can be generated in a node and can be propagated through the subsequent nodes in the data processor. Correlation between computed values is also considered. -Let's look at an example to see how to initialize an instance of ``DataProcessor`` and -create the ``DataAction`` nodes that process the data. +Let's look at an example to see how to initialize an instance of :class:`.DataProcessor` and +create the :class:`.DataAction` nodes that process the data. Data types on IBM Quantum backends ---------------------------------- @@ -47,7 +45,7 @@ on the setup. IQ data can be returned as "single" or "averaged" data. Here, single means that the outcome of each single shot is returned while average only returns the average of the IQ points over the measured shots. The type of data that an experiment should return -is specified by the ``run_options`` of an experiment. +is specified by the :meth:`~.BaseExperiment.run_options` of an experiment. Processing data of different types ---------------------------------- @@ -57,15 +55,15 @@ Crucially, the analysis, such as a curve analysis, expects the same data format no matter the run options of the experiment. Transforming the data returned by the backend into the format that the analysis accepts is done by the ``data_processing`` library. -The key class here is the ``DataProcessor``. It is initialized from -two arguments. The first, is the ``input_key`` which is typically -"memory" or "counts" and identifies the key in the experiment data +The key class here is the :class:`.DataProcessor`. It is initialized from +two arguments. The first is the ``input_key``, which is typically +"memory" or "counts", and identifies the key in the experiment data where the data is located. The second argument ``data_actions`` is a list of ``nodes`` where each node performs a processing step of the data processor. Crucially, the output of one node in the list is the input to the next node in the list. -To illustrate the data processing module we consider an example +To illustrate the data processing module, we consider an example in which we measure a rabi oscillation with different data levels. The code below sets up the Rabi experiment. @@ -164,36 +162,35 @@ of the probability of measuring the outcome "1". Writing data actions -------------------- -The nodes in a data processor are all sub-classes of ``DataAction``. +The nodes in a data processor are all sub-classes of :class:`.DataAction`. Users who wish to write their own data actions must (i) sub-class -``DataAction`` and (ii) implement the internal ``_process`` method -called by instances of ``DataProcessor``. This method is the +:class:`.DataAction` and (ii) implement the internal ``_process`` method +called by instances of :class:`.DataProcessor`. This method is the processing step that the node implements. It takes a numpy array as input and returns the processed numpy array as output. This output serves as the input for the next node in the data processing chain. Here, the input and output numpy arrays can have a different shape. -In addition to the standard ``DataAction`` the data processing package -also supports trainable data actions as subclasses of ``TrainableDataAction``. +In addition to the standard :class:`.DataAction` the data processing package +also supports trainable data actions as subclasses of :class:`.TrainableDataAction`. These nodes must first be trained on the data before they can -process the data. An example of a ``TrainableDataAction`` is the -``SVD`` node which must first learn the main axis of the data before +process the data. An example of a :class:`.TrainableDataAction` is the +:class:`.SVD` node which must first learn the main axis of the data before it can project a data point onto this axis. To implement trainable nodes -developers must also implement the ``train`` method. This method is -called when ``DataProcessor.train`` is called. +developers must also implement the :meth:`~.DataProcessor.train` method. This method is +called when :meth:`~.DataProcessor.train` is called. Conclusion ---------- -In this tutorial you learnt about the data processing module in Qiskit -Experiments. Data is processed by data processors that +Data is processed by data processors that call a list of nodes each acting once on the data. Data processing connects the data returned by the backend to the data that the analysis classes need. Typically, you will not need to implement the data processing yourself since Qiskit Experiments has built-in -methods that determine the correct instance of ``DataProcessor`` for +methods that determine the correct instance of :class:`.DataProcessor` for your data. More advanced data processing includes, for example, handling -restless measurements [2, 3], see also the ``Restless Measurements`` tutorial. +restless measurements [2, 3]. References ~~~~~~~~~~ @@ -210,4 +207,9 @@ measurements with dynamic repetition rates, Physics Review Applied **17**, [3] Max Werninghaus, Daniel J. Egger, Stefan Filipp, High-speed calibration and characterization of superconducting quantum processors without qubit reset, -PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 \ No newline at end of file +PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 + +See also +-------- + +Experiment guide: :doc:`/guides/restless_measurements` diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst new file mode 100644 index 0000000000..71f41f5b94 --- /dev/null +++ b/docs/tutorials/getting_started.rst @@ -0,0 +1,269 @@ +=============== +Getting Started +=============== + +Installation +============ + +Qiskit Experiments is built on top of Qiskit, so we recommend that you first install +Qiskit following its :external+qiskit:doc:`installation guide `. +Qiskit Experiments supports the same platforms and Python versions (currently 3.7+) as +Qiskit itself. + +Qiskit Experiments releases can be installed via the python package manager +``pip``: + +.. code-block:: console + + python -m pip install qiskit-experiments + +If you want to run the most up-to-date version instead (may not be stable), you can +install the latest main branch: + +.. code-block:: console + + python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git + +If you want to develop the package, you can install Qiskit Experiments from source by +cloning the repository: + +.. code-block:: console + + git clone https://github.com/Qiskit/qiskit-experiments.git + python -m pip install -e qiskit-experiments + +The ``-e`` option will keep your installed package up to date as you make or pull new +changes. + +Running your first experiment +============================= + +Let's run a :class:`.T1` Experiment, which estimates the characteristic relaxation +time of a qubit from the excited state to the ground state, also known as :math:`T_1`, by measuring the +excited state population after varying delays. First, we have to import the experiment from the +Qiskit Experiments library: + +.. jupyter-execute:: + + from qiskit_experiments.library import T1 + +Experiments must be run on a backend. We're going to use a simulator, +:class:`qiskit.providers.fake_provider.FakeVigo`, for +this example, but you can use any IBM backend, real or simulated, that you can access +through Qiskit. + +.. jupyter-execute:: + + from qiskit.providers.fake_provider import FakeVigo + from qiskit_aer import AerSimulator + from qiskit.providers.aer.noise import NoiseModel + import numpy as np + + # Create a pure relaxation noise model for AerSimulator + noise_model = NoiseModel.from_backend( + FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False + ) + + backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) + +All experiments require a ``physical_qubits`` parameter as input that specifies which +physical qubit or qubits the circuits will be executed on. The qubits must be given as a +Python sequence (usually a tuple or a list). In addition, the :math:`T_1` experiment has a second +required parameter, ``delays``, which is a list of times in seconds at which to measure +the excited state population. In this example, we'll run the :math:`T_1` experiment on qubit 0, +and use the ``t1`` backend property of this qubit to give us a good +estimate for the sweep range of the delays. + +.. jupyter-execute:: + + qubit0_t1 = backend.properties().t1(0) + + delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) + exp = T1(physical_qubits=(0,), delays=delays) + +After instantiating the experiment, we run the experiment by calling :meth:`.run` with our specified backend. +This returns the :class:`.ExperimentData` class containing the results of the experiment, +so it's crucial that we assign the output to a data variable. We could have also provided the backend +at the instantiation of the experiment, but specifying the backend at run time +allows us to run the same exact experiment on different backends should we choose to do so. + +.. jupyter-execute:: + + exp_data = exp.run(backend=backend).block_for_results() + +The :meth:`~.ExperimentData.block_for_results` method is optional and is used to block execution +of subsequent code until the experiment has fully completed execution and analysis. If +``exp_data = exp.run(backend=backend)`` is executed instead, the statement will finish +running as soon as the jobs are submitted, but the analysis callback won't populate +``exp_data`` with results until the entire process has finished. In this case, there are +two useful methods in the :class:`.ExperimentData`, :meth:`~.ExperimentData.job_status` +and :meth:`~.ExperimentData.analysis_status`, that return the current status of the job +and analysis, respectively: + +.. jupyter-execute:: + + print(exp_data.job_status()) + print(exp_data.analysis_status()) + +Once the analysis is complete, figures are retrieved using the :meth:`~.ExperimentData.figure` method. See the +:doc:`visualization` tutorial on how to customize figures for an experiment. For our :math:`T_1` +experiment, we have a single figure showing the raw data and fit to the exponential +decay model of the :math:`T_1` experiment: + +.. jupyter-execute:: + + display(exp_data.figure(0)) + +The fit results and associated parameters are accessed with +:meth:`~.ExperimentData.analysis_results`: + +.. jupyter-execute:: + + for result in exp_data.analysis_results(): + print(result) + +Results can be indexed numerically or using their name. Each analysis result value is a ``UFloat`` object from the ``uncertainties`` package. +The nominal value and standard deviation of each value can be accessed as follows: + +.. jupyter-execute:: + + print(exp_data.analysis_results("T1").value.nominal_value) + print(exp_data.analysis_results("T1").value.std_dev) + +For further documentation on how to work with UFloats, consult the ``uncertainties`` +:external+uncertainties:doc:`user_guide`. + +The raw data and metadata can be accessed with the :meth:`~.ExperimentData.data` method +and the :meth:`~.ExperimentData.metadata` property: + +.. jupyter-execute:: + + print(exp_data.data(0)['counts']) + print(exp_data.metadata) + +Setting experiment options +========================== + +It's often insufficient to run an experiment with only its default options. +There are four types of options one can set for an experiment: + +Run options +----------- + +These options are passed to the experiment's ``run()`` method and then to the ``run()`` +method of your specified backend. Any run option that your backend supports can be set: + +.. code-block:: + + exp.set_run_options(shots=1000, + meas_level=MeasLevel.CLASSIFIED, + meas_return="avg") + +Consult the documentation of :meth:`qiskit.providers.ibmq.IBMQBackend.run` or +your specific backend type for valid options. + +Transpile options +----------------- +These options are passed to the Terra transpiler to transpile the experiment circuits +before execution: + +.. code-block:: + + exp.set_transpile_options(scheduling_method='asap', + optimization_level=3, + basis_gates=["x", "sx", "rz"]) + +Consult the documentation of :func:`qiskit.compiler.transpile` for valid options. + +Experiment options +------------------ +These options are unique to each experiment class. Many experiment options can be set +upon experiment instantiation, but can also be explicitly set via +:meth:`~BaseExperiment.set_experiment_options`: + +.. code-block:: + + exp = T1(physical_qubits=(i,), delays=delays) + exp.set_experiment_options(delays=new_delays) + +Consult the :doc:`API documentation ` for the options of each experiment class. + +Analysis options +---------------- + +These options are unique to each analysis class. Unlike the other options, analyis options +are not directly set via the experiment object +but use instead a method of the associated ``analysis``: + +.. code-block:: + + exp = rb.StandardRB(physical_qubits=(0,), + lengths=list(range(1, 300, 30)), + seed=123, + backend=backend) + exp.analysis.set_options(gate_error_ratio=None) + +Consult the :doc:`API documentation ` for the options of each +experiment's analysis class. + +Running experiments on multiple qubits +====================================== + +To run experiments across many qubits of the same device, we use **composite experiments**. +A composite experiment is a parent object that contains one or more child +experiments, which may themselves be composite. There are two core types of composite experiments: + +* **Parallel experiments** run across qubits simultaneously as set by the user. New circuits + are constructed such that parallelize circuits of the child experiments are executed + simultaneously. Therefore, the circuits in child experiments *cannot* overlap in qubits + used. The marginalization of measurement data for analysis of each child experiment is handled automatically. +* **Batch experiments** run consecutively in time. These child circuits *can* overlap in qubits used. + +Here's an example of measuring the :math:`T_1` of two qubits on the same device simultaneously +in a parallel experiment: + +.. jupyter-execute:: + + from qiskit_experiments.framework import ParallelExperiment + + parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)]) + parallel_exp.set_transpile_options(scheduling_method='asap') + parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() + +Note that when options are set for a composite experiment, the child +experiments's options are also set recursively. + +:class:`.ParallelExperiment` and :class:`.BatchExperiment` classes can be nested +arbitrarily to make complex composite experiments. + +.. figure:: ./images/compositeexperiments.png + :align: center + +Viewing sub experiment data +=========================== + +The experiment data returned from a composite experiment contains +individual analysis results for each sub experiment that can be accessed +using :meth:`~.ExperimentData.child_data`. By default, the parent data object does not contain analysis results. + +.. jupyter-execute:: + + for i, sub_data in enumerate(parallel_data.child_data()): + print("Component experiment",i) + display(sub_data.figure(0)) + for result in sub_data.analysis_results(): + print(result) + +If you want the parent data object to contain the analysis results instead, +you can set the ``flatten_results`` flag to true to flatten the results of all component +experiments into one level: + +.. jupyter-execute:: + + parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)], + flatten_results=True) + parallel_exp.set_transpile_options(scheduling_method='asap') + parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() + + for result in parallel_data.analysis_results(): + print(result) \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index e5821978b1..53d30950eb 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -5,22 +5,14 @@ Tutorials These tutorials assume some familiarity with Qiskit, but no knowledge of Qiskit Experiments. They're suitable for beginners who want to get started with the package. -Introduction and overview -========================= +The Basics +========== .. toctree:: :maxdepth: 2 intro - - -Getting started -=============== - -.. toctree:: - :maxdepth: 1 - - learning + getting_started Exploring modules ================= @@ -33,8 +25,8 @@ Exploring modules curve_analysis visualization -Write your own experiment -========================= +Customizing experiments +======================= .. toctree:: :maxdepth: 1 diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst index 18e784bff2..d5ec8048f4 100644 --- a/docs/tutorials/intro.rst +++ b/docs/tutorials/intro.rst @@ -1,13 +1,17 @@ +============ +Introduction +============ + What is Qiskit Experiments? =========================== Qiskit Experiments is a package for running device characterization and calibration experiments on top of the core functionality of Qiskit Terra. -An **experiment** is a series of circuits, executed on a device, and analysis of -of the subsequent data. Once an experiment is executed on a quantum backend, either -real or simulated, through a series of jobs, analysis is run automatically and results -in the form of data and figures are generated. +An **experiment** comprises a series of circuits and associated metadata. +Once the experiment circuits are executed on a quantum backend, either +real or simulated, analysis is run automatically on the jobs and results +in the form of data, fit parameters, and figures are generated. In addition to the experiment framework itself, Qiskit Experiments also has a rich library of experiments for calibrating and characterizing qubits. @@ -17,10 +21,11 @@ What Qiskit Experiments can do * Run characterization and calibration experiments such as quantum volume and randomized benchmarking -* Run built-in or customized experiments with all the options that Terra has +* Run built-in or customized experiments with all the options available in Qiskit Terra * Specify fit series and parameters in the analysis * Transform the data through the data processor * Visualize data with support for custom drawing backends +* Save and retrieve timestamped calibration parameters for physical backends A quick primer ============== diff --git a/docs/tutorials/learning.rst b/docs/tutorials/learning.rst deleted file mode 100644 index 0dd1d67458..0000000000 --- a/docs/tutorials/learning.rst +++ /dev/null @@ -1,199 +0,0 @@ -Getting Started -=============== - -Installation -============ - -Official Qiskit Experiments releases can be installed via the python package manager -``pip``. - -.. code-block:: console - - python -m pip install qiskit-experiments - -If you want to install the most up-to-date version instead (may not be stable), you can -install the latest main branch: - -.. code-block:: console - - python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git - -If you want to develop the package, you can install Qiskit Experiments from source by -cloning the repository: - -.. code-block:: console - - git clone https://github.com/Qiskit/qiskit-experiments.git - python -m pip install -e qiskit-experiments - -The ``-e`` option will keep your installed package up to date as you make or pull new -changes. - -Running your first experiment -============================= - -Let's run a T1 experiment. This experiment estimates T1, the characteristic relaxation -time of a qubit from the excited state to the ground state, by measuring the -excited state population after varying delays. First, we have to import the T1 experiment from the -Qiskit Experiments library: - -.. jupyter-execute:: - - from qiskit_experiments.library import T1 - -Experiments must be run on a backend. We're going to use a simulator, -:class:`qiskit.providers.fake_provider.FakeVigo`, for -this example, but you can use any IBM backend that you can access through Qiskit. - -.. jupyter-execute:: - - from qiskit.providers.fake_provider import FakeVigo - from qiskit_aer import AerSimulator - from qiskit.providers.aer.noise import NoiseModel - import numpy as np - - # Create a pure relaxation noise model for AerSimulator - noise_model = NoiseModel.from_backend( - FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False - ) - - backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) - qubit0_t1 = backend.properties().t1(0) - - delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) - exp = T1(qubit=0, delays=delays) - -Run and display results: - -.. jupyter-execute:: - - exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() - - # Print the result - display(exp_data.figure(0)) - for result in exp_data.analysis_results(): - print(result) - -The `block_for_results` method is optional here, and is used to block execution -of subsequent code until the experiment has fully completed execution and analysis. - -Setting experiment options -========================== - -Often it's insufficient to run an experiment with only the default options. -There are four types of options one can set for an experiment: - -Run options ------------ - -These options are passed to the experiment's ``run()`` method and then to the ``run()`` -method of your specified backend. Any run option that your backend supports can be set: - -.. code-block:: - - exp.set_run_options(shots=1000, - meas_level=MeasLevel.CLASSIFIED, - meas_return="avg") - -Consult the documentation of :meth:`qiskit.providers.ibmq.IBMQBackend.run` or -your specific backend type for valid options. - -Transpile options ------------------ -These options are passed to the Terra transpiler to transpile the experiment circuits -before execution: - -.. code-block:: - - exp.set_transpile_options(scheduling_method='asap', - optimization_level=3, - basis_gates=["x", "sx", "rz"]) - -Consult the documentation of :func:`qiskit.compiler.transpile` for valid options. - -Experiment options ------------------- -These options are unique to each experiment class. Many experiment options can be set -upon experiment instantiation, but can also be explicitly set via :meth:`set_experiment_options`: - -.. code-block:: - - exp = T1(qubit=i, delays=delays) - exp.set_experiment_options(delays=new_delays) - -Consult the :doc:`API documentation ` for the options of each experiment class. - -Analysis options ----------------- -These options are unique to each analysis class. Unlike the other options, this one is not set via the experiment object -but via analysis instead: - -.. code-block:: - - exp = rb.StandardRB(qubits=(0,), - lengths=list(range(1, 300, 30)), - seed=123, - backend=backend) - exp.analysis.set_options(gate_error_ratio=None) - -Consult the :doc:`API documentation ` for the options of each -experiment's analysis class. - -Running experiments on multiple qubits -====================================== - -To run experiments across many qubits of the same device, we use **composite experiments**. -There are two core types of composite experiments: - -* **Parallel experiments** run across qubits simultaneously as set by the user. New circuits - are constructed that parallelize circuits of the sub-experiments to be executed simultaneously. - Therefore, the sub-circuits cannot overlap in qubits used. -* **Batch experiments** run consecutively in time. These circuits can overlap in qubits used. - -Here's an example of measuring :math:`T_1` of two qubits on the same device simultaneously -in a parallel experiment: - -.. jupyter-execute:: - - from qiskit_experiments.framework import ParallelExperiment - - # Create a parallel T1 experiment - parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)]) - parallel_exp.set_transpile_options(scheduling_method='asap') - parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() - -Parallel and batch experiments can be nested arbitrarily to make complex composite -experiments. - -.. figure:: ./images/compositeexperiments.png - :align: center - -Viewing sub experiment data -=========================== - -The experiment data returned from a composite experiment contains -individual analysis results for each sub experiment that can be accessed -using ``child_data``. By default, the parent data object does not contain analysis results. - -.. jupyter-execute:: - - # Print sub-experiment data - for i, sub_data in enumerate(parallel_data.child_data()): - print("Component experiment",i) - display(sub_data.figure(0)) - for result in sub_data.analysis_results(): - print(result) - -If you want the parent data object to contain the analysis results instead, -you can set the ``flatten_results`` flag to true to flatten the results of all component -experiments into one level: - -.. jupyter-execute:: - - parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)], - flatten_results=True) - parallel_exp.set_transpile_options(scheduling_method='asap') - parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() - - for result in parallel_data.analysis_results(): - print(result) \ No newline at end of file diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index 86f4c6571c..98b5c2b024 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -107,17 +107,36 @@ figure itself, such as axis labels and series colors. Here is a more complicated experiment in which we customize the figure of a DRAG experiment before it's run, so that we don't need to regenerate the figure like in -the previous example: +the previous example. First, we run the experiment without customizing the options +to see what the default figure looks like: .. jupyter-execute:: from qiskit_experiments.library import RoughDrag from qiskit_experiments.visualization import PlotStyle + from qiskit_experiments.test.mock_iq_helpers import MockIQDragHelper as DragHelper + from qiskit_experiments.test.mock_iq_backend import MockIQBackend + from qiskit.circuit import Parameter + from qiskit import pulse + from qiskit.pulse import DriveChannel, Drag + beta = Parameter("beta") with pulse.build(name="xp") as xp: pulse.play(pulse.Drag(64, 0.66, 16, beta), pulse.DriveChannel(0)) + drag_experiment_helper = DragHelper(gate_name="Drag(xp)") + backend = MockIQBackend(drag_experiment_helper) + + drag = RoughDrag(0, xp, backend=backend) + + drag_data = drag.run().block_for_results() + drag_data.figure(0) + +Now we specify the figure options before running the experiment for a second time: + +.. jupyter-execute:: + drag = RoughDrag(0, xp, backend=backend) # Set plotter options @@ -127,15 +146,15 @@ the previous example: plotter.figure_options.series_params.update( { "nrep=1": { - "color": (27 / 255, 158 / 255, 119 / 255), + "color": (27/255, 158/255, 119/255), "symbol": "^", }, "nrep=3": { - "color": (217 / 255, 95 / 255, 2 / 255), + "color": (217/255, 95/255, 2/255), "symbol": "s", }, "nrep=5": { - "color": (117 / 255, 112 / 255, 179 / 255), + "color": (117/255, 112/255, 179/255), "symbol": "o", }, } @@ -147,12 +166,12 @@ the previous example: xval_unit_scale=False, figure_title="Rough DRAG Experiment on Qubit 0", ) + # Set style parameters plotter.options.style["symbol_size"] = 10 plotter.options.style["legend_loc"] = "upper center" drag_data = drag.run().block_for_results() - drag_data.figure(0) As can be seen in the figure, the different series generated by the experiment @@ -165,15 +184,16 @@ Customizing plotting in your experiment Plotters are easily integrated into custom analysis classes. To add a plotter instance to such a class, we define a new ``plotter`` property, pass it relevant data in the analysis class's ``_run_analysis`` method, and return the generated figure alongside our -analysis results. We use the ``IQPlotter`` class to illustrate how this is done for an arbitrary analysis class. +analysis results. We use the ``IQPlotter`` class to illustrate how this is done for an +arbitrary analysis class. To ensure that we have an interface simlar to existing analysis classes, we make our plotter accessible as an ``analysis.plotter`` property and analysis.options.plotter option. The code below accomplishes this for our example ``MyIQAnalysis`` analysis class. We set the drawer to MplDrawer to use Matplotlib by default. The plotter property of our analysis class makes it easier to access the plotter instance; i.e., using ``self.plotter`` - and ``analysis.plotter``. We set default options and figure options in - ``_default_options``, but you can still override them as we did above. +and ``analysis.plotter``. We set default options and figure options in +``_default_options``, but you can still override them as we did above. The ``MyIQAnalysis`` class accepts single-shot level 1 IQ data, which consists of an in-phase and quadrature measurement for each shot and circuit. ``_run_analysis`` is @@ -192,6 +212,17 @@ series over the axis area. .. jupyter-execute:: + + drag_experiment_helper = DragHelper(gate_name="Drag(xp)") + backend = MockIQBackend(drag_experiment_helper) + with pulse.build(name="xp") as xp: + pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) + + x_plus = xp + drag = RoughDrag(1, x_plus) + + expdata = drag.run(backend) + from qiskit_experiments.framework import BaseAnalysis, Options from qiskit_experiments.visualization import ( BasePlotter, @@ -251,19 +282,21 @@ described, our class will generate a figure showing IQ points and their centroid Creating your own plotter ------------------------- -You can create a custom figure plotter by subclassing ``BasePlotter`` and overriding -``expected_series_data_keys``, ``expected_supplementary_data_keys``, and ``_plot_figure``. +You can create a custom figure plotter by subclassing :class:`.BasePlotter` and overriding +:meth:`~.BasePlotter.expected_series_data_keys`, +:meth:`~.BasePlotter.expected_supplementary_data_keys`, and +:meth:`~.BasePlotter._plot_figure`. The first two methods allow you to define a list of supported data-keys, as strings, which identify the different data to plot. The third method, -``_plot_figure``, must contain your code to generate a figure by calling methods +:meth:`~.BasePlotter._plot_figure`, must contain your code to generate a figure by calling methods on the plotter's drawer instance (self.drawer). When ``plotter.figure()`` is called by an analysis class, the plotter calls _plot_figure and then returns your figure object which is added to the experiment data instance. It is also good practice to set default values for figure options, such as axis labels. You can do this by -overriding the ``_default_figure_options`` method in your plotter subclass. +overriding the :meth:`~.BasePlotter._default_figure_options` method in your plotter subclass. See also -------- -API documentation: :doc:`qiskit_experiments.visualization` \ No newline at end of file +API documentation: :doc:`/apidocs/visualization` \ No newline at end of file diff --git a/qiskit_experiments/__init__.py b/qiskit_experiments/__init__.py index 783dee8caa..ed6c15223a 100644 --- a/qiskit_experiments/__init__.py +++ b/qiskit_experiments/__init__.py @@ -51,6 +51,8 @@ * - :mod:`~qiskit_experiments.database_service` - Classes for saving and retrieving experiment and analysis results from a database. + * - :mod:`~qiskit_experiments.visualization` + - Classes for creating figures from experiment results. Certain experiments also have additional utilities contained which can be accessed by importing the following modules. @@ -70,3 +72,4 @@ from . import calibration_management from . import data_processing from . import database_service +from . import visualization diff --git a/qiskit_experiments/framework/__init__.py b/qiskit_experiments/framework/__init__.py index db697b6b1b..2c14bbf032 100644 --- a/qiskit_experiments/framework/__init__.py +++ b/qiskit_experiments/framework/__init__.py @@ -25,12 +25,12 @@ Overview ======== -The experiment framework broadly defines an experiment as the execution of 1 or more +The experiment framework broadly defines an experiment as the execution of one or more circuits on a device, and analysis of the resulting measurement data -to return 1 or more derived results. +to return one or more derived results. The interface for running an experiment is through the *Experiment* classes, -such as those contained in the :mod:`qiskit_experiments.library` +such as those contained in the :mod:`~qiskit_experiments.library`. The following pseudo-code illustrates the typical workflow in Qiskit Experiments for @@ -45,7 +45,7 @@ from qiskit_experiments.library import SomeExperiment # Initialize with desired qubits and options - exp = SomeExperiment(qubits, **options) + exp = SomeExperiment(physical_qubits, **options) # Run on a backend exp_data = exp.run(backend) diff --git a/qiskit_experiments/library/characterization/correlated_readout_error.py b/qiskit_experiments/library/characterization/correlated_readout_error.py index 6ec7e6ff83..35c344e761 100644 --- a/qiskit_experiments/library/characterization/correlated_readout_error.py +++ b/qiskit_experiments/library/characterization/correlated_readout_error.py @@ -40,7 +40,8 @@ def calibration_circuit(num_qubits: int, state_label: str) -> QuantumCircuit: class CorrelatedReadoutError(BaseExperiment): - r"""Class for correlated readout error characterization experiment + r"""Correlated readout error characterization experiment. + # section: overview This class constructs the a :class:`~qiskit.result.CorrelatedReadoutMitigator` containing the full assignment matrix :math:`A` characterizing the readout error diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index 8e02193b8d..28252f0131 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -24,8 +24,7 @@ class T1(BaseExperiment): - r""" - T1 experiment class + r"""T1 experiment. # section: overview @@ -43,7 +42,8 @@ class T1(BaseExperiment): by fitting to an exponential curve. # section: analysis_ref - :py:class:`T1Analysis` + :py:class:`.T1Analysis` + """ @classmethod From 63545d99ba65f211608c8b0bdf5a09562386ea6b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 14 Feb 2023 08:20:42 -0500 Subject: [PATCH 22/64] deleted duplicate documentation --- docs/tutorials/custom_experiment.rst | 9 +- qiskit_experiments/curve_analysis/__init__.py | 440 ------------------ qiskit_experiments/framework/__init__.py | 128 ----- 3 files changed, 3 insertions(+), 574 deletions(-) diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 5e603d9eee..86441ae183 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -22,17 +22,14 @@ allow configuring various experiment and execution options: to set default values for configurable option parameters for the experiment. - :meth:`.BaseExperiment._default_transpile_options` - to set custom default values for the ``qiskit.transpile`` used to + to set custom default values for the :func:`qiskit.compiler.transpile` method used to transpile the generated circuits before execution. - :meth:`.BaseExperiment._default_run_options` to set default backend options for running the transpiled circuits on a backend. -- :meth:`.BaseExperiment._default_analysis_options` +- :meth:`.BaseAnalysis._default_options` to set default values for configurable options for the experiment's analysis class. - Note that these should generally be set by overriding the :class:`.BaseAnalysis` - method :meth:`~.BaseAnalysis._default_options` instead of this method, except in the - case where the experiment requires different defaults to the used analysis class. - :meth:`.BaseExperiment._transpiled_circuits` to override the default transpilation of circuits before execution. @@ -166,7 +163,7 @@ measurement readout error. backend=None, **experiment_options ): - """Basic randomize Z-basis measuremenent via a Pauli frame transformation + """Basic randomize Z-basis measurement via a Pauli frame transformation Note this will just append a new set of measurment at the end of a circuit. A more advanced version of this experiment would be use a transpiler pass to diff --git a/qiskit_experiments/curve_analysis/__init__.py b/qiskit_experiments/curve_analysis/__init__.py index 220c65ee5e..e0e3451c19 100644 --- a/qiskit_experiments/curve_analysis/__init__.py +++ b/qiskit_experiments/curve_analysis/__init__.py @@ -21,446 +21,6 @@ a single experimental parameter sweep. This analysis subclasses can override several class attributes to customize the behavior from data processing to post-processing, including providing systematic initial guess for parameters tailored to the experiment. -Here we describe how code developers can create new analysis inheriting from the base class. - - -.. _curve_analysis_overview: - -Curve Analysis Overview -======================= - -The base class :class:`CurveAnalysis` implements the multi-objective optimization on -different sets of experiment results. A single experiment can define sub-experiments -consisting of multiple circuits which are tagged with common metadata, -and curve analysis sorts the experiment results based on the circuit metadata. - -This is an example of showing the abstract data structure of typical curve analysis experiment: - -.. code-block:: none - :emphasize-lines: 1,10,19 - - "experiment" - - circuits[0] (x=x1_A, "series_A") - - circuits[1] (x=x1_B, "series_B") - - circuits[2] (x=x2_A, "series_A") - - circuits[3] (x=x2_B, "series_B") - - circuits[4] (x=x3_A, "series_A") - - circuits[5] (x=x3_B, "series_B") - - ... - - "experiment data" - - data[0] (y1_A, "series_A") - - data[1] (y1_B, "series_B") - - data[2] (y2_A, "series_A") - - data[3] (y2_B, "series_B") - - data[4] (y3_A, "series_A") - - data[5] (y3_B, "series_B") - - ... - - "analysis" - - "series_A": y_A = f_A(x_A; p0, p1, p2) - - "series_B": y_B = f_B(x_B; p0, p1, p2) - - fixed parameters {p1: v} - -Here the experiment runs two subset of experiments, namely, series A and series B. -The analysis defines corresponding fit models :math:`f_A(x_A)` and :math:`f_B(x_B)`. -Data extraction function in the analysis creates two datasets, :math:`(x_A, y_A)` -for the series A and :math:`(x_B, y_B)` for the series B, from the experiment data. -Optionally, the curve analysis can fix certain parameters during the fitting. -In this example, :math:`p_1 = v` remains unchanged during the fitting. - -The curve analysis aims at solving the following optimization problem: - -.. math:: - - \Theta_{\mbox{opt}} = \arg\min_{\Theta_{\rm fit}} \sigma^{-2} (F(X, \Theta)-Y)^2, - -where :math:`F` is the composite objective function defined on the full experiment data -:math:`(X, Y)`, where :math:`X = x_A \oplus x_B` and :math:`Y = y_A \oplus y_B`. -This objective function can be described by two fit functions as follows. - -.. math:: - - F(X, \Theta) = f_A(x_A, \theta_A) \oplus f_B(x_B, \theta_B). - -The solver conducts the least square curve fitting against this objective function -and returns the estimated parameters :math:`\Theta_{\mbox{opt}}` -that minimizes the reduced chi-squared value. -The parameters to be evaluated are :math:`\Theta = \Theta_{\rm fit} \cup \Theta_{\rm fix}`, -where :math:`\Theta_{\rm fit} = \theta_A \cup \theta_B`. -Since series A and B share the parameters in this example, :math:`\Theta_{\rm fit} = \{p_0, p_2\}`, -and the fixed parameters are :math:`\Theta_{\rm fix} = \{ p_1 \}` as mentioned. -Thus, :math:`\Theta = \{ p_0, p_1, p_2 \}`. - -Experiment for each series can perform individual parameter sweep for :math:`x_A` and :math:`x_B`, -and experiment data yield outcomes :math:`y_A` and :math:`y_B`, which might be different size. -Data processing function may also compute :math:`\sigma_A` and :math:`\sigma_B` which are -the uncertainty of outcomes arising from the sampling error or measurement error. - -More specifically, the curve analysis defines following data model. - -- Model: Definition of a single curve that is a function of reserved parameter "x". - -- Group: List of models. Fit functions defined under the same group must share the - fit parameters. Fit functions in the group are simultaneously fit to - generate a single fit result. - -Once the group is assigned, a curve analysis instance internally builds -a proper optimization routine. -Finally, the analysis outputs a set of :class:`AnalysisResultData` entries -for important fit outcomes along with a single Matplotlib figure of the fit curves -with the measured data points. - -With this baseclass a developer can avoid writing boilerplate code in -various curve analyses subclass and one can quickly write up -the analysis code for a particular experiment. - - -.. _curve_analysis_define_group: - -Defining New Group -================== - -The fit model is defined by the `LMFIT`_ ``Model``. -If you are familiar with this package, you can skip this section. -The LMFIT package manages complicated fit function and offers several algorithms -to solve non-linear least-square problems. -Basically the Qiskit curve analysis delegates the core fitting functionality to this package. - -You can intuitively write the definition of model, as shown below: - -.. code-block:: python3 - - import lmfit - - models = [ - lmfit.models.ExpressionModel( - expr="amp * exp(-alpha * x) + base", - name="exp_decay", - ) - ] - -Note that ``x`` is the reserved name to represent a parameter -that is scanned during the experiment. In above example, the fit function -consists of three parameters (``amp``, ``alpha``, ``base``), and ``exp`` indicates -a universal function in Python's math module. -Alternatively, you can take a callable to define the model object. - -.. code-block:: python3 - - import lmfit - import numpy as np - - def exp_decay(x, amp, alpha, base): - return amp * np.exp(-alpha * x) + base - - models = [lmfit.Model(func=exp_decay)] - -See `LMFIT`_ documentation for detailed user guide. They also provide preset models. - -If the :class:`.CurveAnalysis` is instantiated with multiple models, -it internally builds a cost function to simultaneously minimize the residuals of -all fit functions. -The names of the parameters in the fit function are important since they are used -in the analysis result, and potentially in your experiment database as a fit result. - -Here is another example how to implement multi-objective optimization task: - -.. code-block:: python3 - - import lmfit - - models = [ - lmfit.models.ExpressionModel( - expr="amp * exp(-alpha1 * x) + base", - name="my_experiment1", - ), - lmfit.models.ExpressionModel( - expr="amp * exp(-alpha2 * x) + base", - name="my_experiment2", - ), - ] - -In addition, you need to provide ``data_subfit_map`` analysis option, which may look like - -.. code-block:: python3 - - data_subfit_map = { - "my_experiment1": {"tag": 1}, - "my_experiment2": {"tag": 2}, - } - -This option specifies the metadata of your experiment circuit -that is tied to the fit model. If multiple models are provided without this option, -the curve fitter cannot prepare data to fit. -In this model, you have four parameters (``amp``, ``alpha1``, ``alpha2``, ``base``) -and the two curves share ``amp`` (``base``) for the amplitude (baseline) in -the exponential decay function. -Here one should expect the experiment data will have two classes of data with metadata -``"tag": 1`` and ``"tag": 2`` for ``my_experiment1`` and ``my_experiment2``, respectively. - -By using this model, one can flexibly set up your fit model. Here is another example: - -.. code-block:: python3 - - import lmfit - - models = [ - lmfit.models.ExpressionModel( - expr="amp * cos(2 * pi * freq * x + phi) + base", - name="my_experiment1", - ), - lmfit.models.ExpressionModel( - expr="amp * sin(2 * pi * freq * x + phi) + base", - name="my_experiment2", - ), - ] - -You have the same set of fit parameters in two models, but now you fit two datasets -with different trigonometric functions. - -.. _LMFIT: https://lmfit.github.io/lmfit-py/intro.html - -.. _curve_analysis_fixed_param: - -Fitting with Fixed Parameters -============================= - -You can also remain certain parameters unchanged during the fitting by specifying -the parameter names in the analysis option ``fixed_parameters``. -This feature is useful especially when you want to define a subclass of -a particular analysis class. - -.. code-block:: python3 - - class AnalysisA(CurveAnalysis): - - def __init__(self): - super().__init__( - models=[ - lmfit.models.ExpressionModel( - expr="amp * exp(-alpha * x) + base", name="my_model" - ) - ] - ) - - class AnalysisB(AnalysisA): - - @classmethod - def _default_options(cls) -> Options: - options = super()._default_options() - options.fixed_parameters = {"amp": 3.0} - - return options - -The parameter specified in ``fixed_parameters`` is excluded from the fitting. -This code will give you identical fit model to the one defined in the following class: - -.. code-block:: python3 - - class AnalysisB(CurveAnalysis): - - super().__init__( - models=[ - lmfit.models.ExpressionModel( - expr="3.0 * exp(-alpha * x) + base", name="my_model" - ) - ] - ) - -However, note that you can also inherit other features, e.g. the algorithm to -generate initial guesses for parameters, from the :class:`AnalysisA` in the first example. -On the other hand, in the latter case, you need to manually copy and paste -every logic defined in the :class:`AnalysisA`. - -.. _curve_analysis_workflow: - -Cureve Analysis Workflow -======================== - -Typically curve analysis performs fitting as follows. -This workflow is defined in the method :meth:`CurveAnalysis._run_analysis`. - -1. Initialization - -Curve analysis calls :meth:`_initialization` method where it initializes -some internal states and optionally populate analysis options -with the input experiment data. -In some case it may train the data processor with fresh outcomes, -or dynamically generate the fit models (``self._models``) with fresh analysis options. -A developer can override this method to perform initialization of analysis-specific variables. - -2. Data processing - -Curve analysis calls :meth:`_run_data_processing` method where -the data processor in the analysis option is internally called. -This consumes input experiment results and creates :class:`CurveData` dataclass. -Then :meth:`_format_data` method is called with the processed dataset to format it. -By default, the formatter takes average of the outcomes in the processed dataset -over the same x values, followed by the sorting in the ascending order of x values. -This allows the analysis to easily estimate the slope of the curves to -create algorithmic initial guess of fit parameters. -A developer can inject extra data processing, for example, filtering, smoothing, -or elimination of outliers for better fitting. - -3. Fitting - -Curve analysis calls :meth:`_run_curve_fit` method which is the core functionality of the fitting. -The another method :meth:`_generate_fit_guesses` is internally called to -prepare the initial guess and parameter boundary with respect to the formatted data. -A developer usually override this method to provide better initial guess -tailored to the defined fit model or type of the associated experiment. -See :ref:`curve_analysis_init_guess` for more details. -A developer can also override the entire :meth:`_run_curve_fit` method to apply -custom fitting algorithms. This method must return :class:`.CurveFitResult` dataclass. - -4. Post processing - -Curve analysis runs several postprocessing against to the fit outcome. -It calls :meth:`_create_analysis_results` to create :class:`AnalysisResultData` class -for the fitting parameters of interest. A developer can inject a custom code to -compute custom quantities based on the raw fit parameters. -See :ref:`curve_analysis_results` for details. -Afterwards, the analysis draws several curves in the Matplotlib figure. -Users can set a custom plotter in :class:`CurveAnalysis` classes, to customize -figures, by setting the :attr:`~CurveAnalysis.plotter` attribute. -Finally, it returns the list of created analysis results and Matplotlib figure. - - -.. _curve_analysis_init_guess: - -Providing Initial Guesses -========================= - -When fit is performed without any prior information of parameters, it usually -falls into unsatisfactory result. -User can provide initial guesses and boundaries for the fit parameters -through analysis options ``p0`` and ``bounds``. -These values are the dictionary keyed on the parameter name, -and one can get the list of parameters with the :attr:`CurveAnalysis.parameters`. -Each boundary value can be a tuple of float representing min and max value. - -Apart from user provided guesses, the analysis can systematically generate -those values with the method :meth:`_generate_fit_guesses` which is called with -:class:`CurveData` dataclass. If the analysis contains multiple models definitions, -we can get the subset of curve data with :meth:`CurveData.get_subset_of` with -the name of the series. -A developer can implement the algorithm to generate initial guesses and boundaries -by using this curve data object, which will be provided to the fitter. -Note that there are several common initial guess estimators available in -:mod:`qiskit_experiments.curve_analysis.guess`. - -The :meth:`_generate_fit_guesses` also receives :class:`FitOptions` instance ``user_opt``, -which contains user provided guesses and boundaries. -This is dictionary-like object consisting of sub-dictionaries for -initial guess ``.p0``, boundary ``.bounds``, and extra options for the fitter. -Note that :class:`CurveAnalysis` uses SciPy `curve_fit`_ as the least square solver. -See the API documentation for available options. - -The :class:`FitOptions` class implements convenient method :meth:`set_if_empty` to manage -conflict with user provided values, i.e. user provided values have higher priority, -thus systematically generated values cannot override user values. - -.. code-block:: python3 - - def _generate_fit_guesses(self, user_opt, curve_data): - - opt1 = user_opt.copy() - opt1.p0.set_if_empty(p1=3) - opt1.bounds = set_if_empty(p1=(0, 10)) - opt1.add_extra_options(method="lm") - - opt2 = user_opt.copy() - opt2.p0.set_if_empty(p1=4) - - return [opt1, opt2] - -Here you created two options with different ``p1`` values. -If multiple options are returned like this, the :meth:`_run_curve_fit` method -attempts to fit with all provided options and finds the best outcome with -the minimum reduced chi-square value. -When the fit model contains some parameter that cannot be easily estimated from the -curve data, you can create multiple options with varying the initial guess to -let the fitter find the most reasonable parameters to explain the model. -This allows you to avoid analysis failure with the poor initial guesses. - -.. _curve_fit: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html - - -.. _curve_analysis_quality: - -Evaluate Fit Quality -==================== - -A subclass can override :meth:`_evaluate_quality` method to -provide an algorithm to evaluate quality of the fitting. -This method is called with the :class:`.CurveFitResult` object which contains -fit parameters and the reduced chi-squared value, -in addition to the several statistics on the fitting. -Qiskit Experiments often uses the empirical criterion chi-squared < 3 as a good fitting. - - -.. _curve_analysis_results: - -Curve Analysis Results -====================== - -Once the best fit parameters are found, the :meth:`_create_analysis_results` method is -called with the same :class:`.CurveFitResult` object. - -If you want to create an analysis result entry for the particular parameter, -you can override the analysis options ``result_parameters``. -By using :class:`ParameterRepr` representation, you can rename the parameter in the entry. - -.. code-block:: python3 - - from qiskit_experiments.curve_analysis import ParameterRepr - - def _default_options(cls) -> Options: - options = super()._default_options() - options.result_parameters = [ParameterRepr("p0", "amp", "Hz")] - - return options - -Here the first argument ``p0`` is the target parameter defined in the series definition, -``amp`` is the representation of ``p0`` in the result entry, -and ``Hz`` is the optional string for the unit of the value if available. - -Not only returning the fit parameters, you can also compute new quantities -by combining multiple fit parameters. -This can be done by overriding the :meth:`_create_analysis_results` method. - -.. code-block:: python3 - - from qiskit_experiments.framework import AnalysisResultData - - def _create_analysis_results(self, fit_data, quality, **metadata): - - outcomes = super()._create_analysis_results(fit_data, **metadata) - - p0 = fit_data.ufloat_params["p0"] - p1 = fit_data.ufloat_params["p1"] - - extra_entry = AnalysisResultData( - name="p01", - value=p0 * p1, - quality=quality, - extra=metadata, - ) - outcomes.append(extra_entry) - - return outcomes - -Note that both ``p0`` and ``p1`` are `UFloat`_ object consisting of -a nominal value and an error value which assumes the standard deviation. -Since this object natively supports error propagation, -you don't need to manually recompute the error of new value. - -.. _ufloat: https://pythonhosted.org/uncertainties/user_guide.html - - -If there is any missing feature, you can write a feature request as an issue in our -`GitHub `_. Base Classes diff --git a/qiskit_experiments/framework/__init__.py b/qiskit_experiments/framework/__init__.py index 2c14bbf032..7c8b9cc9a5 100644 --- a/qiskit_experiments/framework/__init__.py +++ b/qiskit_experiments/framework/__init__.py @@ -17,11 +17,6 @@ .. currentmodule:: qiskit_experiments.framework -.. note:: - - This page provides useful information for developers to implement new - experiments. - Overview ======== @@ -78,129 +73,6 @@ for the experiment itself can be added via :meth:`ExperimentData.metadata`. -Analysis/plotting is done in a separate child thread, so it doesn't block the -main thread. Since matplotlib doesn't support GUI mode in a child threads, the -figures generated during analysis need to use a non-GUI canvas. The default is -:class:`~matplotlib.backends.backend_svg.FigureCanvasSVG`, but you can change it to a different -`non-interactive backend -`_ -by setting the ``qiskit_experiments.framework.matplotlib.default_figure_canvas`` -attribute. For example, you can set ``default_figure_canvas`` to -:class:`~matplotlib.backends.backend_agg.FigureCanvasAgg` to use the -``AGG`` backend. - -For experiments run through a compatible provider such as the -`IBMQ provider `_ -the :class:`ExperimentData` object can be saved to an online experiment -database by calling the :meth:`ExperimentData.save` method. This data can -later be retrieved by its unique :attr:`~ExperimentData.experiment_id`* string -using :meth:`ExperimentData.load`. - - -Composite Experiments -===================== - -The experiment classes :class:`ParallelExperiment` and :class:`BatchExperiment` -provide a way of combining separate component experiments for execution as a -single composite experiment. - -- A :class:`ParallelExperiment` combines all the sub experiment circuits - into circuits which run the component gates in parallel on the - respective qubits. The marginalization of measurement data for analysis - of each sub-experiment is handled automatically. To run as a parallel - experiment each sub experiment must be defined on a independent subset - of device qubits. - -- A :class:`BatchExperiment` combines the sub-experiment circuits into a - single large job that runs all the circuits for each experiment in series. - Filtering the batch result data for analysis for each sub-experiment is - handled automatically. - - -Creating Custom Experiments -=========================== - -Qiskit experiments provides a framework for creating custom experiments which -can be through Qiskit and stored in the online database when run through the IBMQ -provider. You may use this framework to release your own module of experiments -subject to the requirements of the Apache 2.0 license. - -Creating a custom experiment is done by subclassing the -:class:`BaseExperiment` and :class:`BaseAnalysis` classes. - -- The *experiment* class generates the list of circuits to be executed on the - backend and any corresponding metadata that is required for the analysis - of measurement results. - -- The *analysis* class performs post-processing of the measurement results - after execution. Analysis classes can be re-used between experiments so - you can either use one of the included analysis classes if appropriate or - implement your own. - -Experiment Subclasses -********************* - -To create an experiment subclass - -- Implement the abstract :meth:`BaseExperiment.circuits` method. - This should return a list of ``QuantumCircuit`` objects defining - the experiment payload. - -- Call the :meth:`BaseExperiment.__init__` method during the subclass - constructor with a list of physical qubits. The length of this list must - be equal to the number of qubits in each circuit and is used to map these - circuits to this layout during execution. - Arguments in the constructor can be overridden so that a subclass can - be initialized with some experiment configuration. - -Optionally the following methods can also be overridden in the subclass to -allow configuring various experiment and execution options - -- :meth:`BaseExperiment._default_experiment_options` - to set default values for configurable option parameters for the experiment. - -- :meth:`BaseExperiment._default_transpile_options` - to set custom default values for the ``qiskit.transpile`` used to - transpile the generated circuits before execution. - -- :meth:`BaseExperiment._default_run_options` - to set default backend options for running the transpiled circuits on a backend. - -- :meth:`BaseExperiment._default_analysis_options` - to set default values for configurable options for the experiments analysis class. - Note that these should generally be set by overriding the :class:`BaseAnalysis` - method :meth:`BaseAnalysis._default_options` instead of this method except in the - case where the experiment requires different defaults to the used analysis class. - -- :meth:`BaseExperiment._transpiled_circuits` - to override the default transpilation of circuits before execution. - -- :meth:`BaseExperiment._metadata` - to add any experiment metadata to the result data. - -Furthermore, some characterization and calibration experiments can be run with restless -measurements, i.e. measurements where the qubits are not reset and circuits are executed -immediately after the previous measurement. Here, the :class:`.RestlessMixin` can help -to set the appropriate run options and data processing chain. - -Analysis Subclasses -******************* - -To create an analysis subclass one only needs to implement the abstract -:meth:`BaseAnalysis._run_analysis` method. This method takes a -:class:`ExperimentData` container and kwarg analysis options. If any -kwargs are used the :meth:`BaseAnalysis._default_options` method should be -overriden to define default values for these options. - -The :meth:`BaseAnalysis._run_analysis` method should return a pair -``(results, figures)`` where ``results`` is a list of -:class:`AnalysisResultData` and ``figures`` is a list of -:class:`matplotlib.figure.Figure`. - -The :mod:`qiskit_experiments.data_processing` module contains classes for -building data processor workflows to help with advanced analysis of -experiment data. - Classes ======= From 270e1503cfa8a8aa8b3b802b3da4422bce3eebf2 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 14 Feb 2023 08:33:59 -0500 Subject: [PATCH 23/64] added circuit visualization --- docs/tutorials/getting_started.rst | 33 +++++++++++++++++++++++++----- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 71f41f5b94..02f8870596 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -81,6 +81,22 @@ estimate for the sweep range of the delays. delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) exp = T1(physical_qubits=(0,), delays=delays) +The circuits encapsulated by the experiment can be accessed using the experiment's +:meth:`.BaseExperiment.circuits` method, which returns a list of circuits that can +be run on a backend. Let's print the range of delay times we're sweeping over and +draw the first and last circuits for our T1 experiment: + +.. jupyter-execute:: + + print(delays) + exp.circuits()[0].draw(output='mpl') + +.. jupyter-execute:: + + exp.circuits()[-1].draw(output='mpl') + +As expected, the delay block spans the full range of time values that we specified. + After instantiating the experiment, we run the experiment by calling :meth:`.run` with our specified backend. This returns the :class:`.ExperimentData` class containing the results of the experiment, so it's crucial that we assign the output to a data variable. We could have also provided the backend @@ -226,12 +242,19 @@ in a parallel experiment: from qiskit_experiments.framework import ParallelExperiment - parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)]) + parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)]) parallel_exp.set_transpile_options(scheduling_method='asap') parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() Note that when options are set for a composite experiment, the child -experiments's options are also set recursively. +experiments's options are also set recursively. Let's examine how the parallel +experiment is constructed by visualizing the circuits: + +.. jupyter-execute:: + + parallel_exp.circuits()[0].draw(output='mpl') + +We see that the T1 circuits on qubits 0 and 1 have been parallelized to run simultaneously. :class:`.ParallelExperiment` and :class:`.BatchExperiment` classes can be nested arbitrarily to make complex composite experiments. @@ -239,11 +262,11 @@ arbitrarily to make complex composite experiments. .. figure:: ./images/compositeexperiments.png :align: center -Viewing sub experiment data -=========================== +Viewing child experiment data +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The experiment data returned from a composite experiment contains -individual analysis results for each sub experiment that can be accessed +individual analysis results for each child experiment that can be accessed using :meth:`~.ExperimentData.child_data`. By default, the parent data object does not contain analysis results. .. jupyter-execute:: From 72541e65f803062f63c8cceb63e3b3af79bafc46 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 14 Feb 2023 09:13:22 -0500 Subject: [PATCH 24/64] updated options --- docs/guides/t1_experiment.rst | 2 +- docs/tutorials/getting_started.rst | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_experiment.rst index 9bbeccdb16..e3c6e1337c 100644 --- a/docs/guides/t1_experiment.rst +++ b/docs/guides/t1_experiment.rst @@ -80,7 +80,7 @@ for qubit 0. :math:`T_1` experiments with kerneled measurement -============================================ +================================================= :math:`T_1` experiments can also be done with kerneled measurements. If we set the run option `meas_level=MeasLevel.KERNELED`, the job diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 02f8870596..8ed4538fc2 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -243,7 +243,6 @@ in a parallel experiment: from qiskit_experiments.framework import ParallelExperiment parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)]) - parallel_exp.set_transpile_options(scheduling_method='asap') parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() Note that when options are set for a composite experiment, the child @@ -285,7 +284,6 @@ experiments into one level: parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)], flatten_results=True) - parallel_exp.set_transpile_options(scheduling_method='asap') parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() for result in parallel_data.analysis_results(): From 789ef9f388ea553cee369a44ab16ff4b8760a91b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Fri, 17 Feb 2023 13:41:44 -0500 Subject: [PATCH 25/64] updated see also syntax and custom experiment tutorial --- docs/_ext/custom_styles/formatter.py | 11 +- docs/conf.py | 28 +- docs/guides/quantum_volume.rst | 6 +- docs/guides/restless_measurements.rst | 10 +- docs/release_notes.rst | 2 +- docs/tutorials/calibrations.rst | 4 +- docs/tutorials/curve_analysis.rst | 18 +- docs/tutorials/custom_experiment.rst | 276 +++++++++++++++--- docs/tutorials/getting_started.rst | 6 +- qiskit_experiments/__init__.py | 4 +- .../calibration_management/__init__.py | 27 +- .../base_calibration_experiment.py | 24 +- .../basis_gate_library.py | 6 +- .../calibration_management/calibrations.py | 4 +- .../calibration_management/update_library.py | 6 +- .../curve_analysis/base_curve_analysis.py | 2 +- .../curve_analysis/data_processing.py | 2 +- qiskit_experiments/curve_analysis/guess.py | 2 +- .../standard_analysis/bloch_trajectory.py | 4 +- .../curve_analysis/standard_analysis/decay.py | 4 +- .../standard_analysis/oscillation.py | 6 +- qiskit_experiments/framework/__init__.py | 4 +- .../framework/restless_mixin.py | 13 +- qiskit_experiments/library/__init__.py | 2 +- .../library/calibration/__init__.py | 6 +- .../library/calibration/fine_amplitude.py | 12 +- .../library/calibration/fine_drag_cal.py | 8 +- .../library/calibration/fine_frequency_cal.py | 4 +- .../library/calibration/frequency_cal.py | 2 +- .../library/calibration/half_angle_cal.py | 2 +- .../calibration/rough_amplitude_cal.py | 6 +- .../library/calibration/rough_drag_cal.py | 2 +- .../library/calibration/rough_frequency.py | 4 +- .../analysis/cr_hamiltonian_analysis.py | 3 +- .../characterization/analysis/t1_analysis.py | 4 +- .../analysis/t2hahn_analysis.py | 2 +- .../analysis/t2ramsey_analysis.py | 2 +- .../correlated_readout_error.py | 2 +- .../characterization/cr_hamiltonian.py | 10 +- .../library/characterization/drag.py | 2 +- .../characterization/fine_amplitude.py | 6 +- .../library/characterization/fine_drag.py | 10 +- .../characterization/fine_frequency.py | 2 +- .../library/characterization/half_angle.py | 2 +- .../characterization/local_readout_error.py | 2 +- .../multi_state_discrimination.py | 2 +- .../characterization/qubit_spectroscopy.py | 2 +- .../library/characterization/rabi.py | 2 +- .../library/characterization/ramsey_xy.py | 2 +- .../library/characterization/readout_angle.py | 2 +- .../resonator_spectroscopy.py | 4 +- .../library/characterization/t1.py | 8 +- .../library/characterization/t2hahn.py | 2 +- .../library/characterization/t2ramsey.py | 2 +- .../library/characterization/tphi.py | 4 +- .../library/characterization/zz_ramsey.py | 2 +- .../library/quantum_volume/qv_experiment.py | 2 +- .../interleaved_rb_experiment.py | 2 +- .../randomized_benchmarking/rb_experiment.py | 2 +- .../library/tomography/basis/base_basis.py | 4 +- .../library/tomography/qpt_analysis.py | 2 +- .../library/tomography/qpt_experiment.py | 4 +- .../library/tomography/qst_analysis.py | 2 +- .../library/tomography/qst_experiment.py | 4 +- .../tomography/tomography_experiment.py | 2 +- qiskit_experiments/test/mock_iq_backend.py | 2 +- qiskit_experiments/test/mock_iq_helpers.py | 28 +- qiskit_experiments/visualization/__init__.py | 2 +- .../visualization/plotters/curve_plotter.py | 4 +- .../visualization/plotters/iq_plotter.py | 8 +- ...stless_enable_option-3486b0b0d89c1cd7.yaml | 2 +- test/visualization/mock_plotter.py | 4 +- test/visualization/test_iq_plotter.py | 6 +- 73 files changed, 425 insertions(+), 251 deletions(-) diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index d32fa413f9..59d7e8e6ba 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -82,14 +82,12 @@ def format_note(self, lines: List[str]) -> List[str]: @_check_no_indent def format_see_also(self, lines: List[str]) -> List[str]: """Format see also section.""" - text = ".. seealso:: Module(s) " + format_lines = [".. rubric:: See Also", ""] - modules = [] - for line in lines: - modules.append(f":py:mod:`~{line.lstrip()}`") - text += ", ".join(modules) + format_lines.extend(lines) + format_lines.append("") - return [text, ""] + return format_lines @_check_no_indent def format_tutorial(self, lines: List[str]) -> List[str]: @@ -216,6 +214,7 @@ def format_fit_parameters(self, lines: List[str]) -> List[str]: return format_lines + class VisualizationSectionFormatter(DocstringSectionFormatter): """Formatter for visualization classes.""" diff --git a/docs/conf.py b/docs/conf.py index 52e63c8db9..84ff773aa7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -49,32 +49,6 @@ # The full version, including alpha/beta/rc tags release = "0.5.0" -rst_prolog = """ -.. raw:: html - -


- -.. |version| replace:: {0} -""".format( - release -) - -nbsphinx_prolog = """ -{% set docname = env.doc2path(env.docname, base=None) %} -.. only:: html - - .. role:: raw-html(raw) - :format: html - - .. raw:: html - -


- - .. note:: - Run interactively in jupyter notebook. -""" - - # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. @@ -207,6 +181,7 @@ if not os.getenv("EXPERIMENTS_DEV_DOCS", None): rst_prolog = """ .. raw:: html +


""".format( release @@ -214,6 +189,7 @@ else: rst_prolog = """ .. raw:: html +


.. note:: This is the documentation for the current state of the development branch diff --git a/docs/guides/quantum_volume.rst b/docs/guides/quantum_volume.rst index a180826992..176d31b5df 100644 --- a/docs/guides/quantum_volume.rst +++ b/docs/guides/quantum_volume.rst @@ -7,7 +7,7 @@ QV method quantifies the largest random circuit of equal width and depth that the computer successfully implements. Quantum computing systems with high-fidelity operations, high connectivity, large calibrated gate sets, and circuit rewriting toolchains are expected to have higher -quantum volumes. See `Qiskit +quantum volumes. See the `Qiskit Textbook `__ for an explanation on the QV method, which is described in Ref. [1, 2]. @@ -18,8 +18,8 @@ circuits 2/3 with confidence level > 0.977 (corresponding to diff --git a/docs/guides/restless_measurements.rst b/docs/guides/restless_measurements.rst index 598af434ab..f8cd090a1e 100644 --- a/docs/guides/restless_measurements.rst +++ b/docs/guides/restless_measurements.rst @@ -49,9 +49,9 @@ Enabling restless measurements ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In Qiskit Experiments the experiments that support restless measurements -have a special method :meth:`enable_restless` to set the restless run options +have a special method :meth:`~.RestlessMixin.enable_restless` to set the restless run options and define the data processor that will process the measured data. -If you are an experiment developer, you can add the :class:`RestlessMixin` +If you are an experiment developer, you can add the :class:`.RestlessMixin` to your experiment class to add support for restless measurements. Here, we will show how to activate restless measurements using a fake backend and a rough Drag experiment. Note however, that you will not @@ -87,7 +87,7 @@ data processor post-processes the restless measured shots according to the order they were acquired. Furthermore, the appropriate run options are also set. Note that these run options might be unique to IBM Quantum providers. Therefore, execute may fail on non-IBM Quantum providers if the required options are not supported. -After calling ``enable_restless`` the experiment is ready to be run in a restless +After calling :meth:`~.RestlessMixin.enable_restless` the experiment is ready to be run in a restless mode. With a hardware backend this would be done by calling the ``run`` method .. code:: python @@ -95,9 +95,9 @@ mode. With a hardware backend this would be done by calling the ``run`` method drag_data_restless = cal_drag.run() As shown by the example, the code is identical to running a normal experiment aside -from a call to the method ``enable_restless``. Note that you can also choose to keep +from a call to the method :meth:`~.RestlessMixin.enable_restless`. Note that you can also choose to keep the standard data processor by providing it to the analysis options and telling -``enable_restless`` not to override the data processor. +:meth:`~.RestlessMixin.enable_restless` not to override the data processor. .. jupyter-execute:: diff --git a/docs/release_notes.rst b/docs/release_notes.rst index 7e2951be41..ec77acc34c 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -1 +1 @@ -.. release-notes:: Release Notes \ No newline at end of file +.. release-notes:: Release Notes diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index f7274f37c3..84f542d2f3 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -339,8 +339,8 @@ negative amplitude. Fine amplitude calibration -------------------------- -The ``FineAmplitude`` calibration experiment repeats :math:`N` times -a gate with a pulse to amplify the under or over-rotations +The :class:`.FineAmplitude` experiment and its subclass experiments repeats +a gate :math:`N` times with a pulse to amplify the under or over-rotations in the gate to determine the optimal amplitude. .. jupyter-execute:: diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index f35899f177..9b20d3582e 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -18,7 +18,7 @@ analyses that inherits from the base class. Curve analysis overview ----------------------- -The base class :class:`CurveAnalysis` implements the multi-objective optimization on +The base class :class:`.CurveAnalysis` implements the multi-objective optimization on different sets of experiment results. A single experiment can define sub-experiments consisting of multiple circuits which are tagged with common metadata, and curve analysis sorts the experiment results based on the circuit metadata. @@ -96,7 +96,7 @@ More specifically, the curve analysis defines following data model. Once the group is assigned, a curve analysis instance builds a proper internal optimization routine. -Finally, the analysis outputs a set of :class:`AnalysisResultData` entries +Finally, the analysis outputs a set of :class:`.AnalysisResultData` entries for important fit outcomes along with a single Matplotlib figure of the fit curves with the measured data points. @@ -281,7 +281,7 @@ A developer can override this method to perform initialization of analysis-speci Curve analysis calls :meth:`_run_data_processing` method where the data processor in the analysis option is internally called. -This consumes input experiment results and creates :class:`CurveData` dataclass. +This consumes input experiment results and creates :class:`.CurveData` dataclass. Then :meth:`_format_data` method is called with the processed dataset to format it. By default, the formatter takes average of the outcomes in the processed dataset over the same x values, followed by the sorting in the ascending order of x values. @@ -306,13 +306,13 @@ custom fitting algorithms. This method must return :class:`.CurveFitResult` data ^^^^^^^^^^^^^^^^^^ Curve analysis runs several postprocessing against to the fit outcome. -It calls :meth:`_create_analysis_results` to create :class:`AnalysisResultData` class +It calls :meth:`._create_analysis_results` to create :class:`.AnalysisResultData` class for the fitting parameters of interest. A developer can inject a custom code to compute custom quantities based on the raw fit parameters. See :ref:`curve_analysis_results` for details. Afterwards, the analysis draws several curves in the Matplotlib figure. -Users can set a custom plotter in :class:`CurveAnalysis` classes, to customize -figures, by setting the :attr:`~CurveAnalysis.plotter` attribute. +Users can set a custom plotter in :class:`.CurveAnalysis` classes, to customize +figures, by setting the :attr:`~.CurveAnalysis.plotter` attribute. Finally, it returns the list of created analysis results and Matplotlib figure. @@ -339,14 +339,14 @@ by using this curve data object, which will be provided to the fitter. Note that there are several common initial guess estimators available in :mod:`qiskit_experiments.curve_analysis.guess`. -The :meth:`_generate_fit_guesses` also receives :class:`FitOptions` instance ``user_opt``, +The :meth:`_generate_fit_guesses` also receives :class:`.FitOptions` instance ``user_opt``, which contains user provided guesses and boundaries. This is dictionary-like object consisting of sub-dictionaries for initial guess ``.p0``, boundary ``.bounds``, and extra options for the fitter. -Note that :class:`CurveAnalysis` uses SciPy `curve_fit`_ as the least square solver. +Note that :class:`.CurveAnalysis` uses SciPy `curve_fit`_ as the least square solver. See the API documentation for available options. -The :class:`FitOptions` class implements convenient method :meth:`set_if_empty` to manage +The :class:`.FitOptions` class implements convenient method :meth:`set_if_empty` to manage conflict with user provided values, i.e. user provided values have higher priority, thus systematically generated values cannot override user values. diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 86441ae183..4a47ccbffb 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -4,19 +4,18 @@ Writing a custom experiment Qiskit Experiments is designed to be easily customizable. To create an experiment subclass based on either the :class:`.BaseExperiment` class or an existing experiment, you should: -- Implement the abstract :meth:`~.BaseExperiment.circuits` method. - This should return a list of ``QuantumCircuit`` objects defining +- Implement the abstract :meth:`.BaseExperiment.circuits` method. + This should return a list of :class:`~qiskit.QuantumCircuit` objects defining the experiment payload. -- Call the :meth:`BaseExperiment.__init__` method during the subclass +- Call the :meth:`.BaseExperiment.__init__` method during the subclass constructor with a list of physical qubits. The length of this list must be equal to the number of qubits in each circuit and is used to map these circuits to this layout during execution. Arguments in the constructor can be overridden so that a subclass can be initialized with some experiment configuration. -Optionally, the following methods can also be overridden in the subclass to -allow configuring various experiment and execution options: +Optionally, to allow configuring experiment and execution options, you can override: - :meth:`.BaseExperiment._default_experiment_options` to set default values for configurable option parameters for the experiment. @@ -39,7 +38,8 @@ allow configuring various experiment and execution options: Furthermore, some characterization and calibration experiments can be run with restless measurements, i.e. measurements where the qubits are not reset and circuits are executed -immediately after the previous measurement. Here, the :class:`.RestlessMixin` can help +immediately after the previous measurement. Here, the :class:`.RestlessMixin` class +can help to set the appropriate run options and data processing chain. Analysis Subclasses @@ -81,28 +81,29 @@ In order to do this, we need to create a subclass as shown below. .. code-block:: - class HigherOrderFineXAmplitude(FineXAmplitude): - def _pre_circuit(self) -> QuantumCircuit: - """Return a preparation circuit. - - This method can be overridden by subclasses e.g. to calibrate schedules on - transitions other than the 0 <-> 1 transition. - """ - circuit = QuantumCircuit(1) + class HigherOrderFineXAmplitude(FineXAmplitude): + def _pre_circuit(self) -> QuantumCircuit: + """Return a preparation circuit. + + This method can be overridden by subclasses e.g. to calibrate schedules on + transitions other than the 0 <-> 1 transition. + """ + circuit = QuantumCircuit(1) - circuit.x(0) + circuit.x(0) - if self.experiment_options.add_sx: - circuit.sx(0) + if self.experiment_options.add_sx: + circuit.sx(0) - if self.experiment_options.sx_schedule is not None: - sx_schedule = self.experiment_options.sx_schedule - circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) - circuit.barrier() + if self.experiment_options.sx_schedule is not None: + sx_schedule = self.experiment_options.sx_schedule + circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) + circuit.barrier() - return circuit + return circuit -In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate on higher energy transitions by using an initial X gate to populate the first excited state. +In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate +on higher energy transitions by using an initial X gate to populate the first excited state. Using the Subclass ------------------ @@ -124,32 +125,38 @@ You can try this for yourself and verify that your results are similar. - device_components: ['Q0'] - verified: False -Writing a custom experiment ---------------------------- +Writing a new experiment +------------------------ -Now we'll use what we've learned so far to make a full custom experiment from +Now we'll use what we've learned so far to make an entirely new experiment using the :class:`.BaseExperiment` template. A randomized measurement experiment ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -This experiment creates a list of copies of an input circuit -and randomly samples an N-qubit Pauli to apply to each one before -a final N-qubit Z-basis measurement to randomize the expected +Our goal is to write an experiment that symmetrizes the measurement readout error +of an input circuit, which is especially useful in systems where readout has an unknown +and potentially large bias. To do so, our experiment should create a list of copies of an input circuit +and randomly sample an :math:`N`-qubit Pauli to apply to each one before +a final :math:`N`-qubit :math:`Z`-basis measurement to randomize the expected ideal output bitstring in the measurement. The analysis uses the applied Pauli frame of a randomized measurement experiment to de-randomize the measured counts -and combine across samples to return a single counts dictionary +and combine across samples to return a single counts dictionary for the original circuit. This has the effect of Pauli-twirling and symmetrizing the -measurement readout error. +measurement readout error. -.. jupyter-execute:: +To start, we must write our own ``__init__()`` method to take as input the circuit that +we want to twirl on. We also want to give the user the option to specify which +physical qubits to run the circuit over, and which qubits to measure over. If the user +doesn't specify these options, we default to the list of qubits starting with 0 with +the length of the number of qubits in the circuit for both. + +.. code-block:: python - from numpy.random import default_rng, Generator from qiskit import QuantumCircuit from qiskit_experiments.framework import BaseExperiment - from qiskit.quantum_info import random_pauli_list class RandomizedMeasurement(BaseExperiment): """Randomized measurement experiment. @@ -163,11 +170,11 @@ measurement readout error. backend=None, **experiment_options ): - """Basic randomize Z-basis measurement via a Pauli frame transformation + """Basic randomized Z-basis measurement experiment via a Pauli frame transformation - Note this will just append a new set of measurment at the end of a circuit. + Note this will just append a new set of measurements at the end of a circuit. A more advanced version of this experiment would be use a transpiler pass to - replace all exisiting measurements in a circuit with randomized measurements. + replace all existing measurements in a circuit with randomized measurements. """ if physical_qubits is None: physical_qubits = tuple(range(circuit.num_qubits)) @@ -185,13 +192,31 @@ measurement readout error. # Set any init optinos self.set_experiment_options(**experiment_options) +Notice that when we called ``super().__init__``, we provided the list of physical qubits, +the name of our analysis class, and the backend, which is optionally specified by the +user at this stage. + +Now we consider default experiment options. Because randomness is involved, +it is good practice to allow the user to set a seed. We would also like the user to +be able to set how many repetitions of the circuit to run: + +.. code-block:: python + @classmethod def _default_experiment_options(cls): options = super()._default_experiment_options() options.num_samples = "default" options.seed = None return options - + +Now we write the ``circuits()`` method. We need to take the input circuit in ``self._circuit`` +and add our random Paulis as well as measurement at the end. + +.. code-block:: python + + from qiskit.quantum_info import random_pauli_list + from numpy.random import default_rng, Generator + def circuits(self): # Number of classical bits for original circuit and added measurements circ_nc = self._circuit.num_clbits @@ -213,6 +238,12 @@ measurement readout error. else: rng = default_rng(seed) +We use the :func:`~qiskit.quantum_info.random_pauli_list` function from the quantum +info module to generate random Paulis. This returns ``num_samples`` Paulis, each +across ``meas_nc`` qubits. + +.. code-block:: python + # Sample Paulis this might have duplicates, but we don't really # have any easy way of running different number of shots per circuit # so we just run repeat circuits multiple times @@ -237,8 +268,14 @@ measurement readout error. # Add final Measurement circ.measure(meas_qubits, meas_clbits) - - # Add metadata + +Let's look at what the :meth:`~.qiskit.circuit.QuantumCircuit.compose` does here. + +We need to tell our analysis class how to restore the results of the original circuit. +To do so, we add metadata to each of our circuits. + +.. code-block:: python + circ.metadata = orig_metadata.copy() circ.metadata["rm_bits"] = meas_clbits circ.metadata["rm_frame"] = str(pauli) @@ -250,7 +287,165 @@ measurement readout error. And the corresponding analysis class: +.. code-block:: python + + from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData + + class RandomizedMeasurementAnalysis(BaseAnalysis): + """Analysis for randomized measurement experiment.""" + + # Helper dict to swap a clbit value + _swap_bit = {"0": "1", "1": "0"} + + def _run_analysis(self, experiment_data): + + combined_counts = {} + for datum in experiment_data.data(): + # Get counts + counts = datum["counts"] + num_bits = len(next(iter(counts))) + + # Get metadata + metadata = datum["metadata"] + clbits = metadata["rm_bits"] + sig = metadata["rm_sig"] + + # Construct full signature + full_sig = num_bits * [0] + for bit, val in zip(clbits, sig): + full_sig[bit] = val + + # Combine dicts + for key, val in counts.items(): + bitstring = self._swap_bitstring(key, full_sig) + if bitstring in combined_counts: + combined_counts[bitstring] += val + else: + combined_counts[bitstring] = val + + + result = AnalysisResultData("counts", combined_counts) + return [result], [] + + @classmethod + def _swap_bitstring(cls, bitstring, sig): + """Swap a bitstring based signature to flip bits at.""" + # This is very inefficient but demonstrates the basic idea + # Really should do with bitwise operations of integer counts rep + return "".join(reversed( + [cls._swap_bit[b] if sig[- 1 - i] else b for i, b in enumerate(bitstring)] + )) + .. jupyter-execute:: + :hide-code: + :hide-output: + + # this is the actual code that defines the experiment so the code below can work + + from numpy.random import default_rng, Generator + from qiskit import QuantumCircuit + from qiskit_experiments.framework import BaseExperiment + from qiskit.quantum_info import random_pauli_list + + class RandomizedMeasurement(BaseExperiment): + """Randomized measurement experiment. + """ + + def __init__( + self, + circuit, + measured_qubits=None, + physical_qubits=None, + backend=None, + **experiment_options + ): + """Basic randomize Z-basis measurement via a Pauli frame transformation + + Note this will just append a new set of measurment at the end of a circuit. + A more advanced version of this experiment would be use a transpiler pass to + replace all exisiting measurements in a circuit with randomized measurements. + """ + if physical_qubits is None: + physical_qubits = tuple(range(circuit.num_qubits)) + if measured_qubits is None: + measured_qubits = tuple(range(circuit.num_qubits)) + + # Initialize BaseExperiment + analysis = RandomizedMeasurementAnalysis() + super().__init__(physical_qubits, analysis=analysis, backend=backend) + + # Add experiment properties + self._circuit = circuit + self._measured_qubits = measured_qubits + + # Set any init optinos + self.set_experiment_options(**experiment_options) + + @classmethod + def _default_experiment_options(cls): + options = super()._default_experiment_options() + options.num_samples = "default" + options.seed = None + return options + + def circuits(self): + # Number of classical bits for original circuit and added measurements + circ_nc = self._circuit.num_clbits + meas_nc = len(self._measured_qubits) + circ_qubits = list(range(self.num_qubits)) + circ_clbits = list(range(circ_nc)) + meas_qubits = self._measured_qubits + meas_clbits = list(range(circ_nc, circ_nc + meas_nc)) + + # Get number of samples from options + num_samples = self.experiment_options.num_samples + if num_samples == "default": + num_samples = 2 ** self.num_qubits + + # Get rng seed + seed = self.experiment_options.seed + if isinstance(seed, Generator): + rng = seed + else: + rng = default_rng(seed) + + # Sample Paulis this might have duplicates, but we don't really + # have any easy way of running different number of shots per circuit + # so we just run repeat circuits multiple times + paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) + + # Construct circuits + circuits = [] + orig_metadata = self._circuit.metadata or {} + for pauli in paulis: + name = f"{self._circuit.name}_{str(pauli)}" + circ = QuantumCircuit( + self.num_qubits, circ_nc + meas_nc, + name=name + ) + # Append original circuit + circ.compose( + self._circuit, circ_qubits, circ_clbits, inplace=True + ) + + # Add Pauli frame + circ.compose(pauli, meas_qubits, inplace=True) + + # Add final Measurement + circ.measure(meas_qubits, meas_clbits) + + # Add metadata + circ.metadata = orig_metadata.copy() + circ.metadata["rm_bits"] = meas_clbits + circ.metadata["rm_frame"] = str(pauli) + circ.metadata["rm_sig"] = pauli.x.astype(int).tolist() + + circuits.append(circ) + + return circuits + + + from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData @@ -299,6 +494,7 @@ And the corresponding analysis class: [cls._swap_bit[b] if sig[- 1 - i] else b for i, b in enumerate(bitstring)] )) + To test our code, we first simulate a noisy backend with asymmetric readout error in Aer: .. jupyter-execute:: diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 8ed4538fc2..1de87d655f 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -97,7 +97,7 @@ draw the first and last circuits for our T1 experiment: As expected, the delay block spans the full range of time values that we specified. -After instantiating the experiment, we run the experiment by calling :meth:`.run` with our specified backend. +After instantiating the experiment, we run the experiment by calling :meth:`~.BaseExperiment.run` with our specified backend. This returns the :class:`.ExperimentData` class containing the results of the experiment, so it's crucial that we assign the output to a data variable. We could have also provided the backend at the instantiation of the experiment, but specifying the backend at run time @@ -262,7 +262,7 @@ arbitrarily to make complex composite experiments. :align: center Viewing child experiment data -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +----------------------------- The experiment data returned from a composite experiment contains individual analysis results for each child experiment that can be accessed @@ -282,7 +282,7 @@ experiments into one level: .. jupyter-execute:: - parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)], + parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)], flatten_results=True) parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() diff --git a/qiskit_experiments/__init__.py b/qiskit_experiments/__init__.py index ed6c15223a..a64b1ed70b 100644 --- a/qiskit_experiments/__init__.py +++ b/qiskit_experiments/__init__.py @@ -21,8 +21,8 @@ This package is still under active development and it is very likely that there will be breaking API changes in future releases. - If you encounter any bugs please open an issue on - `Github `_ + If you encounter any bugs, please open an issue on + `GitHub `_. Qiskit Experiments provides both a general :mod:`~qiskit_experiments.framework` for creating and diff --git a/qiskit_experiments/calibration_management/__init__.py b/qiskit_experiments/calibration_management/__init__.py index 7933903ab6..7435e4a5c8 100644 --- a/qiskit_experiments/calibration_management/__init__.py +++ b/qiskit_experiments/calibration_management/__init__.py @@ -28,7 +28,7 @@ Furthermore, the resulting parameter values and schedules must be managed. The calibration management module in Qiskit experiments allows users to manage the resulting schedules and parameter values from obtained when running -calibration experiments from the :mod:`qiskit_experiments.library`. +calibration experiments from the :mod:`~qiskit_experiments.library`. Classes ======= @@ -47,7 +47,7 @@ Managing Calibration Data ========================= -Calibrations are managed by the :class:`Calibrations` class. This class stores schedules +Calibrations are managed by the :class:`.Calibrations` class. This class stores schedules which are intended to be fully parameterized, including the index of the channels. This class: @@ -89,25 +89,26 @@ Parametrized channel indices must be named according to a predefined pattern to properly identify the channels and control channels when assigning values to the parametric -channel indices. A channel must have a name that starts with `ch` followed by an integer. -For control channels this integer can be followed by a sequence `.integer`. -Optionally, the name can end with `$integer` to specify the index of a control channel +channel indices. A channel must have a name that starts with ``ch`` followed by an integer. +For control channels, this integer can be followed by a sequence ``.integer``. +Optionally, the name can end with ``$integer`` to specify the index of a control channel for the case when a set of qubits share multiple control channels. For example, -valid channel names include "ch0", "ch1", "ch0.1", "ch0$", "ch2$3", and "ch1.0.3$2". -The "." delimiter is used to specify the different qubits when looking for control +valid channel names include ``"ch0"``, ``"ch1"``, ``"ch0.1"``, ``"ch0$"``, ``"ch2$3"``, +and ``"ch1.0.3$2"``. +The ``.`` delimiter is used to specify the different qubits when looking for control channels. The optional $ delimiter is used to specify which control channel to use if several control channels work together on the same qubits. For example, if the -control channel configuration is {(3,2): [ControlChannel(3), ControlChannel(12)]} -then given qubits (2, 3) the name "ch1.0$1" will resolve to ControlChannel(12) while -"ch1.0$0" will resolve to ControlChannel(3). A channel can only have one parameter. +control channel configuration is ``{(3,2): [ControlChannel(3), ControlChannel(12)]}`` +then given qubits ``(2, 3)`` the name ``"ch1.0$1"`` will resolve to ``ControlChannel(12)`` while +``"ch1.0$0"`` will resolve to ``ControlChannel(3)``. A channel can only have one parameter. Parameter naming restriction **************************** Each parameter must have a unique name within each schedule. For example, it is -acceptable to have a parameter named 'amp' in the schedule 'xp' and a different -parameter instance named 'amp' in the schedule named 'xm'. It is not acceptable -to have two parameters named 'amp' in the same schedule. The naming restriction +acceptable to have a parameter named ``amp`` in the schedule ``xp`` and a different +parameter instance named ``amp`` in the schedule named ``xm``. It is not acceptable +to have two parameters named ``amp`` in the same schedule. The naming restriction only applies to parameters used in the immediate scope of the schedule. Schedules called by Call instructions have their own scope for Parameter names. diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py index 74910963c3..242a0067d1 100644 --- a/qiskit_experiments/calibration_management/base_calibration_experiment.py +++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py @@ -44,13 +44,13 @@ class BaseCalibrationExperiment(BaseExperiment, ABC): This abstract class extends a characterization experiment by turning it into a calibration experiment. Such experiments allow schedule management and updating of an - instance of :class:`Calibrations`. Furthermore, calibration experiments also specify + instance of :class:`.Calibrations`. Furthermore, calibration experiments also specify an auto_update variable which, by default, is set to True. If this variable, - is True then the run method of the experiment will call :meth:`block_for_results` + is True then the run method of the experiment will call :meth:`~.ExperimentData.block_for_results` and update the calibrations instance once the backend has returned the data. - This mixin class inherits from the :class:`BaseExperiment` class since calibration - experiments by default call :meth:`block_for_results`. This ensures that the next + This mixin class inherits from the :class:`.BaseExperiment` class since calibration + experiments by default call :meth:`~.ExperimentData.block_for_results`. This ensures that the next calibration experiment cannot proceed before the calibration parameters have been updated. Developers that wish to create a calibration experiment must subclass this base class and the characterization experiment. Therefore, developers that use this @@ -63,12 +63,12 @@ class should be this mixin and the second class should be the characterization RoughFrequency(BaseCalibrationExperiment, QubitSpectroscopy) - This ensures that the :meth:`run` method of :class:`RoughFrequency` will be the - run method of the :class:`BaseCalibrationExperiment` class. Furthermore, developers + This ensures that the :meth:`run` method of :class:`.RoughFrequency` will be the + run method of the :class:`.BaseCalibrationExperiment` class. Furthermore, developers must explicitly call the :meth:`__init__` methods of both parent classes. Developers should strive to follow the convention that the first two arguments of - a calibration experiment are the qubit(s) and the :class:`Calibration` instance. + a calibration experiment are the qubit(s) and the :class:`.Calibration` instance. If the experiment uses custom schedules, which is typically the case, then developers may chose to use the :meth:`get_schedules` method when creating the @@ -85,7 +85,7 @@ class should be this mixin and the second class should be the characterization These methods are called by :meth:`get_schedules`. The :meth:`update_calibrations` method is responsible for updating the values of the parameters - stored in the instance of :class:`Calibrations`. Here, :class:`BaseCalibrationExperiment` + stored in the instance of :class:`.Calibrations`. Here, :class:`BaseCalibrationExperiment` provides a default update methodology that subclasses can override if a more elaborate behaviour is needed. At the minimum the developer must set the variable :code:`_updater` which should have an :code:`update` method and can be chosen from the library @@ -213,13 +213,13 @@ def set_transpile_options(self, **fields): warnings.warn(f"Transpile options are not used in {self.__class__.__name__ }.") def update_calibrations(self, experiment_data: ExperimentData): - """Update parameter values in the :class:`Calibrations` instance. + """Update parameter values in the :class:`.Calibrations` instance. The default behaviour is to call the update method of the class variable :code:`__updater__` with simplistic options. Subclasses can override this - method to update the instance of :class:`Calibrations` if they require a - more sophisticated behaviour as is the case for the :class:`Rabi` and - :class:`FineAmplitude` calibration experiments. + method to update the instance of :class:`.Calibrations` if they require a + more sophisticated behaviour as is the case for the :class:`.Rabi` and + :class:`.FineAmplitude` calibration experiments. """ if self._updater is not None: self._updater.update( diff --git a/qiskit_experiments/calibration_management/basis_gate_library.py b/qiskit_experiments/calibration_management/basis_gate_library.py index d309d3d9e7..713066f5f2 100644 --- a/qiskit_experiments/calibration_management/basis_gate_library.py +++ b/qiskit_experiments/calibration_management/basis_gate_library.py @@ -121,7 +121,7 @@ def default_values(self) -> List[DefaultCalValue]: Returns A list of tuples is returned. These tuples are structured so that instances of - :class:`Calibrations` can call :meth:`add_parameter_value` on the tuples. + :class:`.Calibrations` can call :meth:`.add_parameter_value` on the tuples. """ @abstractmethod @@ -287,7 +287,7 @@ def default_values(self) -> List[DefaultCalValue]: Returns A list of tuples is returned. These tuples are structured so that instances of - :class:`Calibrations` can call :meth:`add_parameter_value` on the tuples. + :class:`.Calibrations` can call :meth:`.add_parameter_value` on the tuples. """ defaults = [] for name, schedule in self.items(): @@ -325,7 +325,7 @@ class EchoedCrossResonance(BasisGateLibrary): - rzx: RZXGate built from the ecr as ``cr45p - x - cr45m - x``. Required gates: - - x: the x gate is defined outside of this library, see :class:`FixedFrequencyTransmon`. + - x: the x gate is defined outside of this library, see :class:`.FixedFrequencyTransmon`. Pulse parameters: - tgt_amp: The amplitude of the pulse applied to the target qubit. Default value: 0. diff --git a/qiskit_experiments/calibration_management/calibrations.py b/qiskit_experiments/calibration_management/calibrations.py index 2f9405f12f..127729b73d 100644 --- a/qiskit_experiments/calibration_management/calibrations.py +++ b/qiskit_experiments/calibration_management/calibrations.py @@ -80,7 +80,7 @@ def __init__( """Initialize the calibrations. Calibrations can be initialized from a list of basis gate libraries, i.e. a subclass of - :class:`BasisGateLibrary`. As example consider the following code: + :class:`.BasisGateLibrary`. As example consider the following code: .. code-block:: python @@ -490,7 +490,7 @@ def inst_map_add( schedule_name: The name of the schedule. If None is given then we assume that the schedule and the instruction have the same name. assign_params: An optional dict of parameter mappings to apply. See for instance - :meth:`get_schedule` of :class:`Calibrations`. + :meth:`.get_schedule` of :class:`.Calibrations`. """ schedule_name = schedule_name or instruction_name diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py index 539831b9be..eac5e32d04 100644 --- a/qiskit_experiments/calibration_management/update_library.py +++ b/qiskit_experiments/calibration_management/update_library.py @@ -35,14 +35,14 @@ def __init__(self): """Updaters are not meant to be instantiated. Instead of instantiating updaters use them by calling the :meth:`update` class method. - For example, the :class:`Frequency` updater is called in the following way + For example, the :class:`.Frequency` updater is called in the following way .. code-block:: python Frequency.update(calibrations, spectroscopy_data) - Here, calibrations is an instance of :class:`Calibrations` and spectroscopy_data - is the result of a :class:`QubitSpectroscopy` experiment. + Here, calibrations is an instance of :class:`.Calibrations` and spectroscopy_data + is the result of a :class:`.QubitSpectroscopy` experiment. """ raise CalibrationError( "Calibration updaters are not meant to be instantiated. The intended usage" diff --git a/qiskit_experiments/curve_analysis/base_curve_analysis.py b/qiskit_experiments/curve_analysis/base_curve_analysis.py index 21389aed95..fcc90cfbd7 100644 --- a/qiskit_experiments/curve_analysis/base_curve_analysis.py +++ b/qiskit_experiments/curve_analysis/base_curve_analysis.py @@ -158,7 +158,7 @@ def _default_options(cls) -> Options: return_data_points (bool): Set ``True`` to include in the analysis result the formatted data points given to the fitter. Default to ``False``. data_processor (Callable): A callback function to format experiment data. - This can be a :class:`~qiskit_experiments.data_processing.DataProcessor` + This can be a :class:`.DataProcessor` instance that defines the `self.__call__` method. normalization (bool) : Set ``True`` to normalize y values within range [-1, 1]. Default to ``False``. diff --git a/qiskit_experiments/curve_analysis/data_processing.py b/qiskit_experiments/curve_analysis/data_processing.py index f07e7f059e..b0c4365e6f 100644 --- a/qiskit_experiments/curve_analysis/data_processing.py +++ b/qiskit_experiments/curve_analysis/data_processing.py @@ -185,7 +185,7 @@ def multi_mean_xy_data( Tuple of (series, xdata, ydata, sigma, shots) See also: - :py:func:`~qiskit_experiments.curve_analysis.data_processing.mean_xy_data` + :func:`~.data_processing.mean_xy_data` """ series_vals = np.unique(series) diff --git a/qiskit_experiments/curve_analysis/guess.py b/qiskit_experiments/curve_analysis/guess.py index fbfc7f981e..ddf2617bba 100644 --- a/qiskit_experiments/curve_analysis/guess.py +++ b/qiskit_experiments/curve_analysis/guess.py @@ -213,7 +213,7 @@ def oscillation_exp_decay( This function first applies a Savitzky-Golay filter to y value, then run scipy peak search to extract peak positions. If ``freq_guess`` is provided, the search function will be robust to fake peaks due to noise. - This function calls :py:func:`exp_decay` function for extracted x and y values at peaks. + This function calls :func:`exp_decay` function for extracted x and y values at peaks. .. note:: diff --git a/qiskit_experiments/curve_analysis/standard_analysis/bloch_trajectory.py b/qiskit_experiments/curve_analysis/standard_analysis/bloch_trajectory.py index b0425404e6..9e4885ba9b 100644 --- a/qiskit_experiments/curve_analysis/standard_analysis/bloch_trajectory.py +++ b/qiskit_experiments/curve_analysis/standard_analysis/bloch_trajectory.py @@ -74,8 +74,8 @@ class BlochTrajectoryAnalysis(curve.CurveAnalysis): a flat-topped Gaussian, two Gaussian edges may become an offset duration. init_guess: Computed as :math:`N \sqrt{2 \pi} \sigma` where the :math:`N` is number of pulses and :math:`\sigma` is Gaussian sigma of rising and falling edges. - Note that this implicitly assumes the :py:class:`~qiskit.pulse.library\ - .parametric_pulses.GaussianSquare` pulse envelope. + Note that this implicitly assumes the :class:`~qiskit.pulse.library\ + .GaussianSquare` pulse envelope. bounds: [0, None] defpar p_x: diff --git a/qiskit_experiments/curve_analysis/standard_analysis/decay.py b/qiskit_experiments/curve_analysis/standard_analysis/decay.py index 276ac26296..1ff1daaf58 100644 --- a/qiskit_experiments/curve_analysis/standard_analysis/decay.py +++ b/qiskit_experiments/curve_analysis/standard_analysis/decay.py @@ -33,7 +33,7 @@ class DecayAnalysis(curve.CurveAnalysis): defpar \rm amp: desc: Height of the decay curve. - init_guess: Determined by :py:func:`~qiskit_experiments.curve_analysis.guess.min_height`. + init_guess: Determined by :func:`~qiskit_experiments.curve_analysis.guess.min_height`. bounds: None defpar \rm base: @@ -43,7 +43,7 @@ class DecayAnalysis(curve.CurveAnalysis): defpar \tau: desc: This is the fit parameter of main interest. - init_guess: Determined by :py:func:`~qiskit_experiments.curve_analysis.guess.exp_decay`. + init_guess: Determined by :func:`~qiskit_experiments.curve_analysis.guess.exp_decay`. bounds: None """ diff --git a/qiskit_experiments/curve_analysis/standard_analysis/oscillation.py b/qiskit_experiments/curve_analysis/standard_analysis/oscillation.py index 393b16b6f8..68155340ea 100644 --- a/qiskit_experiments/curve_analysis/standard_analysis/oscillation.py +++ b/qiskit_experiments/curve_analysis/standard_analysis/oscillation.py @@ -152,19 +152,19 @@ class DampedOscillationAnalysis(curve.CurveAnalysis): defpar \rm base: desc: Offset. Base line of the decay curve. - init_guess: Determined by :py:func:`~qiskit_experiments.curve_analysis.\ + init_guess: Determined by :func:`~qiskit_experiments.curve_analysis.\ guess.constant_sinusoidal_offset` bounds: [0, 1.5] defpar \tau: desc: Represents the rate of decay. - init_guess: Determined by :py:func:`~qiskit_experiments.curve_analysis.\ + init_guess: Determined by :func:`~qiskit_experiments.curve_analysis.\ guess.oscillation_exp_decay` bounds: [0, None] defpar \rm freq: desc: Oscillation frequency. - init_guess: Determined by :py:func:`~qiskit_experiments.curve_analysis.guess.frequency` + init_guess: Determined by :func:`~qiskit_experiments.curve_analysis.guess.frequency` bounds: [0, 10 freq] defpar \phi: diff --git a/qiskit_experiments/framework/__init__.py b/qiskit_experiments/framework/__init__.py index 7c8b9cc9a5..98c9cf7367 100644 --- a/qiskit_experiments/framework/__init__.py +++ b/qiskit_experiments/framework/__init__.py @@ -78,6 +78,7 @@ Experiment Data Classes *********************** + .. autosummary:: :toctree: ../stubs/ @@ -96,6 +97,7 @@ Composite Experiment Classes **************************** + .. autosummary:: :toctree: ../stubs/ @@ -113,7 +115,7 @@ BaseAnalysis Experiment Configuration Helper Classes -******* +*************************************** .. autosummary:: :toctree: ../stubs/ diff --git a/qiskit_experiments/framework/restless_mixin.py b/qiskit_experiments/framework/restless_mixin.py index b3d23d43f2..6386d261e8 100644 --- a/qiskit_experiments/framework/restless_mixin.py +++ b/qiskit_experiments/framework/restless_mixin.py @@ -33,25 +33,26 @@ class RestlessMixin: This class defines the following methods - - :meth:`enable_restless` - - :meth:`_get_restless_processor` - - :meth:`_t1_check` + - :meth:`~.RestlessMixin.enable_restless` + - :meth:`~.RestlessMixin._get_restless_processor` + - :meth:`~.RestlessMixin._t1_check` A restless enabled experiment is an experiment that can be run in a restless measurement setting. In restless measurements, the qubit is not reset after each measurement. Instead, the outcome of the previous quantum non-demolition measurement is the initial state for the current circuit. Restless measurements therefore require special data processing which is provided by sub-classes of - the :code:`RestlessNode`. Restless experiments are a fast alternative for + the :class:`.RestlessNode`. Restless experiments are a fast alternative for several calibration and characterization tasks, for details see https://arxiv.org/pdf/2202.06981.pdf. - This class makes it possible for users to enter a restless run-mode without having + + This class makes it possible for users to enter a restless run mode without having to manually set all the required run options and the data processor. The required options are ``rep_delay``, ``init_qubits``, ``memory``, and ``meas_level``. Furthermore, subclasses can override the :meth:`_get_restless_processor` method if they require more complex restless data processing such as two-qubit calibrations. In addition, this class makes it easy to determine if restless measurements are supported for a given - experiments. + experiment. """ analysis: BaseAnalysis diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index cfadbd07ac..a82e014cf0 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -90,7 +90,7 @@ Experiments for pulse level calibration of quantum gates. These experiments are usually run with a -:py:class:`~qiskit_experiments.calibration_management.Calibrations` +:class:`~qiskit_experiments.calibration_management.Calibrations` class instance to manage parameters and pulse schedules. See :doc:`/tutorials/calibrations` for examples. diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py index 2d7d33ed0a..e281ae839f 100644 --- a/qiskit_experiments/library/calibration/__init__.py +++ b/qiskit_experiments/library/calibration/__init__.py @@ -30,9 +30,9 @@ experiments and manage the resulting schedules and parameter values. The following experiments are designed to calibrate parameter values. Some experiments such -as :class:`QubitSpectroscopy` can both be seen as characterization and calibrations +as :class:`.QubitSpectroscopy` can both be seen as characterization and calibrations experiments. Such experiments can be found in the -:mod:`qiskit_experiments.library.characterization` +:mod:`~qiskit_experiments.library.characterization` module. .. autosummary:: @@ -56,7 +56,7 @@ Calibrations management ======================= -See :mod:`qiskit_experiments.calibration_management`. +See :mod:`.calibration_management`. """ from .rough_frequency import RoughFrequencyCal diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py index 119787d1af..dc1dbb184d 100644 --- a/qiskit_experiments/library/calibration/fine_amplitude.py +++ b/qiskit_experiments/library/calibration/fine_amplitude.py @@ -29,16 +29,16 @@ class FineAmplitudeCal(BaseCalibrationExperiment, FineAmplitude): - r"""A calibration version of the :class:`FineAmplitude` experiment. + r"""A calibration version of the :class:`.FineAmplitude` experiment. # section: overview - :class:`FineAmplitudeCal` is a subclass of :class:`FineAmplitude`. In the calibration + :class:`FineAmplitudeCal` is a subclass of :class:`.FineAmplitude`. In the calibration experiment the circuits that are run have a custom gate with the pulse schedule attached to it through the calibrations. # section: see_also - qiskit_experiments.library.characterization.fine_amplitude.FineAmplitude + :class:`.FineAmplitude` """ @@ -54,7 +54,7 @@ def __init__( gate: Optional[Gate] = None, measurement_qubits: Sequence[int] = None, ): - """see class :class:`FineAmplitude` for details. + """See class :class:`FineAmplitude` for details. Args: physical_qubits: Sequence containing the qubit(s) for which to run @@ -164,7 +164,7 @@ class FineXAmplitudeCal(FineAmplitudeCal): """A calibration experiment to calibrate the amplitude of the X schedule. # section: see_also - qiskit_experiments.library.characterization.fine_amplitude.FineAmplitude + :class:`.FineAmplitude` """ @qubit_deprecate() @@ -217,7 +217,7 @@ class FineSXAmplitudeCal(FineAmplitudeCal): """A calibration experiment to calibrate the amplitude of the SX schedule. # section: see_also - qiskit_experiments.library.characterization.fine_amplitude.FineAmplitude + :class:`.FineAmplitude` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/calibration/fine_drag_cal.py b/qiskit_experiments/library/calibration/fine_drag_cal.py index 9009f3e4ec..83d0a352bb 100644 --- a/qiskit_experiments/library/calibration/fine_drag_cal.py +++ b/qiskit_experiments/library/calibration/fine_drag_cal.py @@ -34,7 +34,7 @@ class FineDragCal(BaseCalibrationExperiment, FineDrag): """A calibration version of the fine drag experiment. # section: see_also - qiskit_experiments.library.characterization.fine_drag.FineDrag + :class:`.FineDrag` """ @qubit_deprecate() @@ -47,7 +47,7 @@ def __init__( cal_parameter_name: Optional[str] = "β", auto_update: bool = True, ): - r"""see class :class:`FineDrag` for details. + r"""See class :class:`FineDrag` for details. Note that this class implicitly assumes that the target angle of the gate is :math:`\pi` as seen from the default experiment options. @@ -153,7 +153,7 @@ class FineXDragCal(FineDragCal): """Fine drag calibration of X gate. # section: see_also - qiskit_experiments.library.characterization.fine_drag.FineDrag + :class:`.FineDrag` """ @qubit_deprecate() @@ -190,7 +190,7 @@ class FineSXDragCal(FineDragCal): """Fine drag calibration of X gate. # section: see_also - qiskit_experiments.library.characterization.fine_drag.FineDrag + :class:`.FineDrag` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/calibration/fine_frequency_cal.py b/qiskit_experiments/library/calibration/fine_frequency_cal.py index 6012da5afb..f7cb8c8333 100644 --- a/qiskit_experiments/library/calibration/fine_frequency_cal.py +++ b/qiskit_experiments/library/calibration/fine_frequency_cal.py @@ -32,7 +32,7 @@ class FineFrequencyCal(BaseCalibrationExperiment, FineFrequency): """A calibration version of the fine frequency experiment. # section: see_also - :py:class:`FineFrequency` + :class:`.FineFrequency` """ @qubit_deprecate() @@ -46,7 +46,7 @@ def __init__( auto_update: bool = True, gate_name: str = "sx", ): - r"""see class :class:`FineFrequency` for details. + r"""See class :class:`.FineFrequency` for details. Note that this class implicitly assumes that the target angle of the gate is :math:`\pi/2` as seen from the default analysis options. This experiment diff --git a/qiskit_experiments/library/calibration/frequency_cal.py b/qiskit_experiments/library/calibration/frequency_cal.py index 29864b92be..f7ba2696b0 100644 --- a/qiskit_experiments/library/calibration/frequency_cal.py +++ b/qiskit_experiments/library/calibration/frequency_cal.py @@ -31,7 +31,7 @@ class FrequencyCal(BaseCalibrationExperiment, RamseyXY): """A qubit frequency calibration experiment based on the Ramsey XY experiment. # section: see_also - qiskit_experiments.library.characterization.ramsey_xy.RamseyXY + :class:`.RamseyXY` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/calibration/half_angle_cal.py b/qiskit_experiments/library/calibration/half_angle_cal.py index 6d2ad28cc2..f78a88a8d1 100644 --- a/qiskit_experiments/library/calibration/half_angle_cal.py +++ b/qiskit_experiments/library/calibration/half_angle_cal.py @@ -32,7 +32,7 @@ class HalfAngleCal(BaseCalibrationExperiment, HalfAngle): """Calibration version of the half-angle experiment. # section: see_also - qiskit_experiments.library.characterization.half_angle.HalfAngle + :class:`.HalfAngle` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/calibration/rough_amplitude_cal.py b/qiskit_experiments/library/calibration/rough_amplitude_cal.py index e7db08ae25..66355c31e1 100644 --- a/qiskit_experiments/library/calibration/rough_amplitude_cal.py +++ b/qiskit_experiments/library/calibration/rough_amplitude_cal.py @@ -35,7 +35,7 @@ class RoughAmplitudeCal(BaseCalibrationExperiment, Rabi): """A calibration version of the Rabi experiment. # section: see_also - qiskit_experiments.library.characterization.rabi.Rabi + :class:`.Rabi` """ @qubit_deprecate() @@ -197,7 +197,7 @@ class RoughXSXAmplitudeCal(RoughAmplitudeCal): """A rough amplitude calibration of x and sx gates. # section: see_also - qiskit_experiments.library.characterization.rabi.Rabi + :class:`.Rabi` """ @qubit_deprecate() @@ -231,7 +231,7 @@ class EFRoughXSXAmplitudeCal(RoughAmplitudeCal): """A rough amplitude calibration of x and sx gates on the 1<->2 transition. # section: see_also - qiskit_experiments.library.characterization.rabi.Rabi + :class:`.Rabi` """ __outcome__ = "rabi_rate_12" diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py index 3d3f277412..9abcc2e078 100644 --- a/qiskit_experiments/library/calibration/rough_drag_cal.py +++ b/qiskit_experiments/library/calibration/rough_drag_cal.py @@ -31,7 +31,7 @@ class RoughDragCal(BaseCalibrationExperiment, RoughDrag): """A calibration version of the Drag experiment. # section: see_also - qiskit_experiments.library.characterization.rough_drag.RoughDrag + :class:`.RoughDrag` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 1719f10854..6d61b75368 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -31,7 +31,7 @@ class RoughFrequencyCal(BaseCalibrationExperiment, QubitSpectroscopy): """A calibration experiment that runs QubitSpectroscopy. # section: see_also - qiskit_experiments.library.characterization.qubit_spectroscopy.QubitSpectroscopy + :class:`.QubitSpectroscopy` """ @qubit_deprecate() @@ -80,7 +80,7 @@ class RoughEFFrequencyCal(BaseCalibrationExperiment, EFSpectroscopy): """A calibration experiment that runs QubitSpectroscopy. # section: see_also - qiskit_experiments.library.characterization.ef_spectroscopy.EFSpectroscopy + :class:`.EFSpectroscopy` """ __updater__ = Frequency diff --git a/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py index 62831f3a8e..1fd248fcf5 100644 --- a/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py @@ -43,8 +43,7 @@ class CrossResonanceHamiltonianAnalysis(curve.CompositeCurveAnalysis): for the projection axis :math:`\beta` with the control qubit state :math:`|j\rangle`. # section: see_also - - qiskit_experiments.curve_analysis.standard_analysis.BlochTrajectoryAnalysis + :class:`.BlochTrajectoryAnalysis` """ diff --git a/qiskit_experiments/library/characterization/analysis/t1_analysis.py b/qiskit_experiments/library/characterization/analysis/t1_analysis.py index 91a69cc2a5..c1584bfe6f 100644 --- a/qiskit_experiments/library/characterization/analysis/t1_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/t1_analysis.py @@ -26,7 +26,7 @@ class T1Analysis(curve.DecayAnalysis): r"""A class to analyze T1 experiments. # section: see_also - qiskit_experiments.curve_analysis.standard_analysis.decay.DecayAnalysis + :class:`.DecayAnalysis` """ @@ -77,7 +77,7 @@ class T1KerneledAnalysis(curve.DecayAnalysis): r"""A class to analyze T1 experiments with kerneled data. # section: see_also - qiskit_experiments.curve_analysis.standard_analysis.decay.DecayAnalysis + :class:`.DecayAnalysis` """ diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py index fa1f3c958e..b9f13b251b 100644 --- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py @@ -26,7 +26,7 @@ class T2HahnAnalysis(curve.DecayAnalysis): r"""A class to analyze T2Hahn experiments. # section: see_also - qiskit_experiments.curve_analysis.standard_analysis.decay.DecayAnalysis + :class:`.DecayAnalysis` """ diff --git a/qiskit_experiments/library/characterization/analysis/t2ramsey_analysis.py b/qiskit_experiments/library/characterization/analysis/t2ramsey_analysis.py index d33d60a478..5375c3a59c 100644 --- a/qiskit_experiments/library/characterization/analysis/t2ramsey_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/t2ramsey_analysis.py @@ -21,7 +21,7 @@ class T2RamseyAnalysis(curve.DampedOscillationAnalysis): """T2 Ramsey result analysis class. # section: see_also - qiskit_experiments.curve_analysis.standard_analysis.oscillation.DampedOscillationAnalysis + :class:`.DampedOscillationAnalysis` """ diff --git a/qiskit_experiments/library/characterization/correlated_readout_error.py b/qiskit_experiments/library/characterization/correlated_readout_error.py index 35c344e761..466277bfda 100644 --- a/qiskit_experiments/library/characterization/correlated_readout_error.py +++ b/qiskit_experiments/library/characterization/correlated_readout_error.py @@ -72,7 +72,7 @@ class CorrelatedReadoutError(BaseExperiment): documentation for additional information on correlated readout error experiment analysis. # section: analysis_ref - :py:class:`CorrelatedReadoutErrorAnalysis` + :class:`CorrelatedReadoutErrorAnalysis` # section: reference .. ref_arxiv:: 1 2006.14044 diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py index 29864a766f..e641f609a6 100644 --- a/qiskit_experiments/library/characterization/cr_hamiltonian.py +++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py @@ -95,11 +95,11 @@ class CrossResonanceHamiltonian(BaseExperiment): Here ``cr_tone`` is implemented by a single cross resonance tone driving the control qubit at the frequency of the target qubit. The pulse envelope might be a flat-topped Gaussian implemented by the parametric pulse - :py:class:`~qiskit.pulse.library.parametric_pulses.GaussianSquare`. + :class:`~qiskit.pulse.library.parametric_pulses.GaussianSquare`. This experiment scans the total duration of the cross resonance pulse including the pulse ramps at both edges. The pulse shape is defined by the - :py:class:`~qiskit.pulse.library.parametric_pulses.GaussianSquare`, and + :class:`~qiskit.pulse.library.parametric_pulses.GaussianSquare`, and an effective length of these Gaussian ramps with :math:`\sigma` can be computed by .. math:: @@ -117,7 +117,7 @@ class CrossResonanceHamiltonian(BaseExperiment): interaction rates. # section: analysis_ref - :py:class:`CrossResonanceHamiltonianAnalysis` + :class:`CrossResonanceHamiltonianAnalysis` # section: reference .. ref_arxiv:: 1 1603.04821 @@ -505,7 +505,7 @@ class EchoedCrossResonanceHamiltonian(CrossResonanceHamiltonian): # section: overview - This is a variant of :py:class:`CrossResonanceHamiltonian` + This is a variant of :class:`CrossResonanceHamiltonian` for which the experiment framework is identical but the cross resonance operation is realized as an echoed sequence to remove unwanted single qubit rotations. The cross resonance @@ -519,7 +519,7 @@ class EchoedCrossResonanceHamiltonian(CrossResonanceHamiltonian): q_1: ┤1 ├┤ Rz(π) ├┤1 ├┤ Rz(-π) ├ └────────────────────┘└───────┘└────────────────────┘└────────┘ - Here two ``cr_tone``s are applied where the latter one is with the + Here two ``cr_tone`` are applied, where the latter one is with the control qubit state flipped and with a phase flip of the target qubit frame. This operation is equivalent to applying the ``cr_tone`` with a negative amplitude. The Hamiltonian for this decomposition has no IX and ZI interactions, diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index 55ea762bbd..654651c5ef 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -63,7 +63,7 @@ class RoughDrag(BaseExperiment, RestlessMixin): Note that the analysis class requires this experiment to run with three repetition numbers. # section: analysis_ref - :py:class:`DragCalAnalysis` + :class:`DragCalAnalysis` # section: reference .. ref_arxiv:: 1 1011.1949 diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index 72e9074d83..e515e3d8f0 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -27,11 +27,11 @@ class FineAmplitude(BaseExperiment, RestlessMixin): - r"""Error amplifying fine amplitude calibration experiment. + r"""Error amplifying fine amplitude experiment. # section: overview - The :class:`FineAmplitude` calibration experiment repeats N times a gate with a pulse + The :class:`FineAmplitude` experiment repeats N times a gate with a pulse to amplify the under-/over-rotations in the gate to determine the optimal amplitude. The circuits are therefore of the form: @@ -81,7 +81,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): that set the appropriate options by default. # section: analysis_ref - :py:class:`FineAmplitudeAnalysis` + :class:`FineAmplitudeAnalysis` # section: reference .. ref_arxiv:: 1 1504.06597 diff --git a/qiskit_experiments/library/characterization/fine_drag.py b/qiskit_experiments/library/characterization/fine_drag.py index 295a0d74db..eecfb658fc 100644 --- a/qiskit_experiments/library/characterization/fine_drag.py +++ b/qiskit_experiments/library/characterization/fine_drag.py @@ -30,7 +30,7 @@ class FineDrag(BaseExperiment, RestlessMixin): # section: overview - :class:`FineDrag` runs fine DRAG characterization experiments (see :class:`DragCal` + :class:`FineDrag` runs fine DRAG characterization experiments (see :class:`.DragCal` for the definition of DRAG pulses). Fine DRAG proceeds by iterating the gate sequence Rp - Rm where Rp is a rotation around an axis and Rm is the same rotation but in the opposite direction and is implemented by the gates Rz - Rp - Rz where the Rz gates @@ -126,10 +126,10 @@ class FineDrag(BaseExperiment, RestlessMixin): This is the correction formula in the FineDRAG Updater. # section: analysis_ref - :py:class:`~qiskit_experiments.curve_analysis.ErrorAmplificationAnalysis` + :class:`.ErrorAmplificationAnalysis` # section: see_also - qiskit_experiments.library.calibration.drag.DragCal + :class:`.DragCal` # section: reference .. ref_arxiv:: 1 1612.00858 @@ -248,7 +248,7 @@ class FineXDrag(FineDrag): """Class to fine characterize the DRAG parameter of an X gate. # section: see_also - qiskit_experiments.library.characterization.fine_drag.FineDrag + :class:`.FineDrag` """ @qubit_deprecate() @@ -278,7 +278,7 @@ class FineSXDrag(FineDrag): """Class to fine characterize the DRAG parameter of an SX gate. # section: see_also - qiskit_experiments.library.characterization.fine_drag.FineDrag + :class:`.FineDrag` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/characterization/fine_frequency.py b/qiskit_experiments/library/characterization/fine_frequency.py index fd022d0846..b970d7c49e 100644 --- a/qiskit_experiments/library/characterization/fine_frequency.py +++ b/qiskit_experiments/library/characterization/fine_frequency.py @@ -48,7 +48,7 @@ class FineFrequency(BaseExperiment): meas: 1/══════════════════════════════════════════════╩═ 0 # section: analysis_ref - :py:class:`~qiskit_experiments.curve_analysis.ErrorAmplificationAnalysis` + :class:`~qiskit_experiments.curve_analysis.ErrorAmplificationAnalysis` """ @qubit_deprecate() diff --git a/qiskit_experiments/library/characterization/half_angle.py b/qiskit_experiments/library/characterization/half_angle.py index 98f82f393a..7a3b84bf8b 100644 --- a/qiskit_experiments/library/characterization/half_angle.py +++ b/qiskit_experiments/library/characterization/half_angle.py @@ -49,7 +49,7 @@ class HalfAngle(BaseExperiment): be different from the :math:`\pi` pulse. # section: analysis_ref - :py:class:`.ErrorAmplificationAnalysis` + :class:`.ErrorAmplificationAnalysis` # section: reference .. ref_arxiv:: 1 1504.06597 diff --git a/qiskit_experiments/library/characterization/local_readout_error.py b/qiskit_experiments/library/characterization/local_readout_error.py index ce109b8e7a..f53f122074 100644 --- a/qiskit_experiments/library/characterization/local_readout_error.py +++ b/qiskit_experiments/library/characterization/local_readout_error.py @@ -60,7 +60,7 @@ class LocalReadoutError(BaseExperiment): documentation for additional information on local readout error experiment analysis. # section: analysis_ref - :py:class:`LocalReadoutErrorAnalysis` + :class:`LocalReadoutErrorAnalysis` # section: reference .. ref_arxiv:: 1 2006.14044 diff --git a/qiskit_experiments/library/characterization/multi_state_discrimination.py b/qiskit_experiments/library/characterization/multi_state_discrimination.py index 90634f75a2..9950e92914 100644 --- a/qiskit_experiments/library/characterization/multi_state_discrimination.py +++ b/qiskit_experiments/library/characterization/multi_state_discrimination.py @@ -52,7 +52,7 @@ class MultiStateDiscrimination(BaseExperiment): meas: ═══════════════════════╩═ # section: analysis_ref - :py:class:`MultiStateDiscriminationAnalysis` + :class:`MultiStateDiscriminationAnalysis` # section: reference `Qiskit Textbook Tuple[int, ...]: - """Return the shape of subsystem dimensions of the state :py:attr:`~matrix`. + """Return the shape of subsystem dimensions of the state attr:`~matrix`. Args: qubits: the physical qubit subsystems. @@ -170,7 +170,7 @@ def outcome_shape(self, qubits: Sequence[int]) -> Tuple[int, ...]: @abstractmethod def matrix_shape(self, qubits: Sequence[int]) -> Tuple[int, ...]: - """Return the shape of subsystem dimensions of a POVM :py:attr:`~matrix`. + """Return the shape of subsystem dimensions of a POVM attr:`~matrix`. Args: qubits: the physical qubit subsystems. diff --git a/qiskit_experiments/library/tomography/qpt_analysis.py b/qiskit_experiments/library/tomography/qpt_analysis.py index 2d1d792616..1a364c82e9 100644 --- a/qiskit_experiments/library/tomography/qpt_analysis.py +++ b/qiskit_experiments/library/tomography/qpt_analysis.py @@ -60,7 +60,7 @@ class ProcessTomographyAnalysis(TomographyAnalysis): .. ref_arxiv:: 1 1106.5458 # section: see_also - qiskit_experiments.library.tomography.tomography_analysis.TomographyAnalysis + :class:`.TomographyAnalysis` """ diff --git a/qiskit_experiments/library/tomography/qpt_experiment.py b/qiskit_experiments/library/tomography/qpt_experiment.py index 4d00cf47e4..c83380dba2 100644 --- a/qiskit_experiments/library/tomography/qpt_experiment.py +++ b/qiskit_experiments/library/tomography/qpt_experiment.py @@ -45,10 +45,10 @@ class ProcessTomography(TomographyExperiment): preparation and measurement bases. # section: analysis_ref - :py:class:`ProcessTomographyAnalysis` + :class:`ProcessTomographyAnalysis` # section: see_also - qiskit_experiments.library.tomography.tomography_experiment.TomographyExperiment + :class:`.TomographyExperiment` """ diff --git a/qiskit_experiments/library/tomography/qst_analysis.py b/qiskit_experiments/library/tomography/qst_analysis.py index 43754a3f56..d64982886a 100644 --- a/qiskit_experiments/library/tomography/qst_analysis.py +++ b/qiskit_experiments/library/tomography/qst_analysis.py @@ -59,7 +59,7 @@ class StateTomographyAnalysis(TomographyAnalysis): .. ref_arxiv:: 1 1106.5458 # section: see_also - qiskit_experiments.library.tomography.tomography_analysis.TomographyAnalysis + :class:`.TomographyAnalysis` """ diff --git a/qiskit_experiments/library/tomography/qst_experiment.py b/qiskit_experiments/library/tomography/qst_experiment.py index 005b0d19f3..bc68a42ff6 100644 --- a/qiskit_experiments/library/tomography/qst_experiment.py +++ b/qiskit_experiments/library/tomography/qst_experiment.py @@ -43,10 +43,10 @@ class StateTomography(TomographyExperiment): measurement basis. # section: analysis_ref - :py:class:`StateTomographyAnalysis` + :class:`StateTomographyAnalysis` # section: see_also - qiskit_experiments.library.tomography.tomography_experiment.TomographyExperiment + :class:`.TomographyExperiment` """ diff --git a/qiskit_experiments/library/tomography/tomography_experiment.py b/qiskit_experiments/library/tomography/tomography_experiment.py index 015f7fa1df..277c4e6eb0 100644 --- a/qiskit_experiments/library/tomography/tomography_experiment.py +++ b/qiskit_experiments/library/tomography/tomography_experiment.py @@ -31,7 +31,7 @@ class TomographyExperiment(BaseExperiment): """Base experiment for quantum state and process tomography. # section: analysis_ref - :py:class:`TomographyAnalysis` + :class:`TomographyAnalysis` """ @classmethod diff --git a/qiskit_experiments/test/mock_iq_backend.py b/qiskit_experiments/test/mock_iq_backend.py index 3ffdc03968..aa46a0049b 100644 --- a/qiskit_experiments/test/mock_iq_backend.py +++ b/qiskit_experiments/test/mock_iq_backend.py @@ -244,7 +244,7 @@ def _get_normal_samples_for_shot( """ Produce a list in the size of num_qubits. Each entry value is produced from normal distribution with expected value of '0' and standard deviation of 1. The intention is that these samples are - scaled by :py:func:`_scale_samples_for_widths` for various circuits, experiments, and their IQ + scaled by :func:`_scale_samples_for_widths` for various circuits, experiments, and their IQ widths; removing the need to query a RNG for each new width list. Example: diff --git a/qiskit_experiments/test/mock_iq_helpers.py b/qiskit_experiments/test/mock_iq_helpers.py index c44b7df054..9ff0593493 100644 --- a/qiskit_experiments/test/mock_iq_helpers.py +++ b/qiskit_experiments/test/mock_iq_helpers.py @@ -40,9 +40,9 @@ def __init__( """Create a MockIQBackend helper object to define how the backend functions. `iq_cluster_centers` and `iq_cluster_width` define the base IQ cluster centers and - standard-deviations for each qubit in a :py:class:`MockIQBackend` instance. These are used by - :py:meth:`iq_clusters` by default. Subclasses can override :py:meth:`iq_clusters` to return a - modified version of :py:attr:`iq_cluster_centers` and :py:attr:`iq_cluster_width`. + standard-deviations for each qubit in a :class:`MockIQBackend` instance. These are used by + :meth:`iq_clusters` by default. Subclasses can override :meth:`iq_clusters` to return a + modified version of attr:`iq_cluster_centers` and attr:`iq_cluster_width`. `iq_cluster_centers` is a list of tuples. For a given qubit `i_qbt` and computational state `i_state` (either `0` or `1`), the centers of the IQ clusters are found by indexing `iq_cluster_centers` as follows: @@ -182,17 +182,17 @@ def iq_clusters( """Returns circuit-specific IQ cluster centers and widths in the IQ plane. Subclasses can override this function to modify the centers and widths of IQ clusters based on - the circuits being simulated by a :py:class:`MockIQBackend`. The base centers and widths are - stored internally within the helper object, and can be set in :py:meth:`__init__` or by modifying - :py:attr:`iq_cluster_centers` and :py:attr:`iq_cluster_width`. The default behaviour for - :py:meth:`iq_clusters` is to return the centers and widths unmodified for each circuit in + the circuits being simulated by a :class:`MockIQBackend`. The base centers and widths are + stored internally within the helper object, and can be set in :meth:`__init__` or by modifying + attr:`iq_cluster_centers` and attr:`iq_cluster_width`. The default behaviour for + :meth:`iq_clusters` is to return the centers and widths unmodified for each circuit in `circuits`. Subclasses may return different centers and widths based on the circuits provided. The returned list contains a tuple per circuit. Each tuple contains the IQ centers and widths in - the same format as :py:attr:`iq_cluster_centers` and :py:attr:`iq_cluster_width`, passed as - arguments to :py:meth:`__init__`. The format of the centers and widths lists, in the argument + the same format as attr:`iq_cluster_centers` and attr:`iq_cluster_width`, passed as + arguments to :meth:`__init__`. The format of the centers and widths lists, in the argument list and in the returned tuples, must match the format of `iq_cluster_centers` and - `iq_cluster_width` in :py:func:`qiskit_experiments.test.MockIQExperimentHelper.__init__`. + `iq_cluster_width` in :func:`qiskit_experiments.test.MockIQExperimentHelper.__init__`. Args: circuits: The quantum circuits for which the clusters should be modified. @@ -216,14 +216,14 @@ def __init__( Parallel Experiment Helper initializer. The class assumes `exp_helper_list` is ordered to match the corresponding experiment in `exp_list`. - Note that :py:meth:`__init__` does not have `iq_cluster_centers` and `iq_cluster_width` as in - :py:func:`MockIQExperimentHelper.__init__`. This is because the centers and widths for - :py:class:`MockIQParallelBackend` are stored in multiple experiment helpers in the list + Note that :meth:`__init__` does not have `iq_cluster_centers` and `iq_cluster_width` as in + :func:`MockIQExperimentHelper.__init__`. This is because the centers and widths for + :class:`MockIQParallelBackend` are stored in multiple experiment helpers in the list `exp_helper_list`. Args: exp_list(List): List of experiments. - exp_helper_list(List): Ordered list of `MockIQExperimentHelper` corresponding to the + exp_helper_list(List): Ordered list of :class:`.MockIQExperimentHelper` corresponding to the experiments in `exp_list`. Nested parallel experiment aren't supported currently. Raises: diff --git a/qiskit_experiments/visualization/__init__.py b/qiskit_experiments/visualization/__init__.py index 7c7eb4cda4..5e49aa1e85 100644 --- a/qiskit_experiments/visualization/__init__.py +++ b/qiskit_experiments/visualization/__init__.py @@ -18,7 +18,7 @@ Visualization provides plotting functionality for creating figures from experiment and analysis results. This includes plotter and drawer classes to plot data in -:py:class:`CurveAnalysis` and its subclasses. Plotters inherit from :class:`BasePlotter` +class:`.CurveAnalysis` and its subclasses. Plotters inherit from :class:`BasePlotter` and define a type of figure that may be generated from experiment or analysis data. For example, the results from :class:`CurveAnalysis` --- or any other experiment where results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted diff --git a/qiskit_experiments/visualization/plotters/curve_plotter.py b/qiskit_experiments/visualization/plotters/curve_plotter.py index cf793972ca..033ba81a54 100644 --- a/qiskit_experiments/visualization/plotters/curve_plotter.py +++ b/qiskit_experiments/visualization/plotters/curve_plotter.py @@ -9,7 +9,7 @@ # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. -"""Plotter for curve fits, specifically from :class:`CurveAnalysis`.""" +"""Plotter for curve fits, specifically from :class:`.CurveAnalysis`.""" from typing import List from uncertainties import UFloat @@ -21,7 +21,7 @@ class CurvePlotter(BasePlotter): - """A plotter class to plot results from :class:`CurveAnalysis`. + """A plotter class to plot results from :class:`.CurveAnalysis`. ``CurvePlotter`` plots results from curve fits, which includes diff --git a/qiskit_experiments/visualization/plotters/iq_plotter.py b/qiskit_experiments/visualization/plotters/iq_plotter.py index 9058f18809..4917dc5e51 100644 --- a/qiskit_experiments/visualization/plotters/iq_plotter.py +++ b/qiskit_experiments/visualization/plotters/iq_plotter.py @@ -26,13 +26,13 @@ class IQPlotter(BasePlotter): """A plotter class to plot IQ data. - :class:`IQPlotter` plots results from experiments which used measurement-level 1, + :class:`.IQPlotter` plots results from experiments which used measurement-level 1, i.e. IQ data. This class also supports plotting predictions from a discriminator - (subclass of :class:`BaseDiscriminator`), which is used to classify IQ results into + (subclass of :class:`.BaseDiscriminator`), which is used to classify IQ results into labels. The discriminator labels are matched with the series names to generate an image of the predictions. Points that are misclassified by the discriminator are flagged in the figure (see ``flag_misclassified`` :attr:`option`). A canonical - application of :class:`IQPlotter` is for classification of single-qubit readout for + application of :class:`.IQPlotter` is for classification of single-qubit readout for different prepared states. Example: @@ -97,7 +97,7 @@ def expected_supplementary_data_keys(cls) -> List[str]: outcome. The predictions are assumed to be series names (``Union[str, int, float]``). The generated image allows viewers to see how well the discriminator classifies the provided series data. Must be a subclass of - :class:`BaseDiscriminator`. See :attr:`options` for ways to control the + :class:`.BaseDiscriminator`. See :attr:`options` for ways to control the generation of the discriminator prediction image. fidelity: A float representing the fidelity of the discrimination. """ diff --git a/releasenotes/notes/0.4/restless_enable_option-3486b0b0d89c1cd7.yaml b/releasenotes/notes/0.4/restless_enable_option-3486b0b0d89c1cd7.yaml index a982bdc8ff..966285cff0 100644 --- a/releasenotes/notes/0.4/restless_enable_option-3486b0b0d89c1cd7.yaml +++ b/releasenotes/notes/0.4/restless_enable_option-3486b0b0d89c1cd7.yaml @@ -1,7 +1,7 @@ --- fixes: - | - The :meth:`.enable_restless` method of the :class:`.RestlessMixin` class now has + The :meth:`~.RestlessMixin.enable_restless` method of the :class:`.RestlessMixin` class now has the non-default option to supress errors when T1 values are lower than the repetition dely. This allows users to accomodate cases when backends report erronous T1 values. diff --git a/test/visualization/mock_plotter.py b/test/visualization/mock_plotter.py index b4d03cf295..3834eac1ba 100644 --- a/test/visualization/mock_plotter.py +++ b/test/visualization/mock_plotter.py @@ -22,8 +22,8 @@ class MockPlotter(BasePlotter): """Mock plotter for visualization tests. - If :attr:`plotting_enabled` is true, :class:`MockPlotter` will plot formatted data. - :attr:`plotting_enabled` defaults to false as most test usage of the class uses :class:`MockDrawer`, + If :attr:`plotting_enabled` is true, :class:`.MockPlotter` will plot formatted data. + :attr:`plotting_enabled` defaults to false as most test usage of the class uses :class:`.MockDrawer`, which doesn't generate a useful figure. """ diff --git a/test/visualization/test_iq_plotter.py b/test/visualization/test_iq_plotter.py index 5fa5f8362d..e39b6f6a09 100644 --- a/test/visualization/test_iq_plotter.py +++ b/test/visualization/test_iq_plotter.py @@ -26,7 +26,7 @@ class MockDiscriminatorNotTrainedException(Exception): """Mock exception to be raised when :meth:`MockDiscriminator.predict` is called on an untrained - :class:`MockDiscriminator`.""" + :class:`.MockDiscriminator`.""" pass @@ -89,14 +89,14 @@ def _dummy_data( Args: is_trained: Whether the discriminator should be trained or not. Defaults to True. n_series: The number of series to generate dummy data for. Defaults to 3. - raise_predict_not_trained: Passed to the discriminator :class:`MockDiscriminator` class. + raise_predict_not_trained: Passed to the discriminator :class:`.MockDiscriminator` class. factor: A scaler factor by which to multipl all data. Returns: tuple: the tuple ``(points, names, discrim)`` where ``points`` is a list of NumPy arrays of IQ points, ``names`` is a list of series names (one for each NumPy array), and - ``discrim`` is a :class:`MockDiscriminator` instance. + ``discrim`` is a :class:`.MockDiscriminator` instance. """ points = [] labels = [] From 1d8a2674dd1b31239a361fa82a432cefeb72f943 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 20 Feb 2023 21:23:58 -0500 Subject: [PATCH 26/64] formatting fix --- docs/tutorials/getting_started.rst | 2 +- qiskit_experiments/calibration_management/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 1de87d655f..854435095c 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -10,7 +10,7 @@ Qiskit following its :external+qiskit:doc:`installation guide ` Qiskit Experiments supports the same platforms and Python versions (currently 3.7+) as Qiskit itself. -Qiskit Experiments releases can be installed via the python package manager +Qiskit Experiments releases can be installed via the Python package manager ``pip``: .. code-block:: console diff --git a/qiskit_experiments/calibration_management/__init__.py b/qiskit_experiments/calibration_management/__init__.py index 7435e4a5c8..82be9ae50d 100644 --- a/qiskit_experiments/calibration_management/__init__.py +++ b/qiskit_experiments/calibration_management/__init__.py @@ -93,7 +93,7 @@ For control channels, this integer can be followed by a sequence ``.integer``. Optionally, the name can end with ``$integer`` to specify the index of a control channel for the case when a set of qubits share multiple control channels. For example, -valid channel names include ``"ch0"``, ``"ch1"``, ``"ch0.1"``, ``"ch0$"``, ``"ch2$3"``, +valid channel names include ``"ch0"``, ``"ch1"``, ``"ch0.1"``, ``"ch0$"``, ``"ch2$3"``, and ``"ch1.0.3$2"``. The ``.`` delimiter is used to specify the different qubits when looking for control channels. The optional $ delimiter is used to specify which control channel to use From a021d8e29bac9181b16156a6737a025d2ad349fe Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sat, 25 Feb 2023 16:44:06 -0500 Subject: [PATCH 27/64] added job splitting howto --- docs/conf.py | 5 +- docs/howtos/job_splitting.rst | 18 +++++ docs/howtos/new_experimentdata.rst | 52 +++++++++++++ docs/howtos/newexperimentdata.rst | 36 --------- docs/tutorials/custom_experiment.rst | 75 ++++++++++++++++--- docs/tutorials/getting_started.rst | 6 ++ .../characterization/fine_amplitude.py | 5 +- 7 files changed, 145 insertions(+), 52 deletions(-) create mode 100644 docs/howtos/job_splitting.rst create mode 100644 docs/howtos/new_experimentdata.rst delete mode 100644 docs/howtos/newexperimentdata.rst diff --git a/docs/conf.py b/docs/conf.py index 84ff773aa7..185dea29c2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -101,9 +101,7 @@ # Autodoc # ----------------------------------------------------------------------------- -autodoc_default_options = { - "inherited-members": None, -} +autodoc_default_options = {"inherited-members": None, "private-members": True} # If true, figures, tables and code-blocks are automatically numbered if they @@ -182,7 +180,6 @@ rst_prolog = """ .. raw:: html -


""".format( release ) diff --git a/docs/howtos/job_splitting.rst b/docs/howtos/job_splitting.rst new file mode 100644 index 0000000000..cc7b53b342 --- /dev/null +++ b/docs/howtos/job_splitting.rst @@ -0,0 +1,18 @@ +How to control the splitting of experiment circuits into jobs +============================================================= + +Problem +------- + +You want to control how many jobs an experiment is split into when running on a backend. + +Solution +-------- + +Discussion +---------- + +Qiskit Experiments will automatically split circuits across jobs for you for backends +that have a maximum circuit number per circuit, which is given by `max_circuits` property in :meth:`qiskit.providers.BackendV1.configuration` for V1 backends and :meth:`qiskit.providers.BackendV2.max_circuits` for V2. This should +work automatically in most cases, but there may be some backends where other limits +exist. \ No newline at end of file diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst new file mode 100644 index 0000000000..30c166952c --- /dev/null +++ b/docs/howtos/new_experimentdata.rst @@ -0,0 +1,52 @@ +How to instantiate a new data object for an existing experiment +=============================================================== + +Problem +------- + +You want to instantiate a new :class:`.ExperimentData` object from an existing +experiment whose jobs have finished execution successfully. + +Solution +-------- + +Use the code template below. You need to know the exact experiment you +ran and its options, as well as the IDs of the jobs that were executed. + +.. code-block:: python + + from qiskit_experiments.framework import ExperimentData + + # The experiment you ran + experiment = Experiment(**opts) + + # List of job IDs for the experiment + job_ids= [job1, job2, ...] + + data = ExperimentData(job_ids=job_ids) + experiment.analysis.run(data) + # Blocks execution of subsequent code until analysis is complete + data.block_for_results() + +``data`` will be the new experiment data object. + +Discussion +---------- + +This recipe is helpful for cases such as a lost connection during experiment execution, +where the jobs may have finished running on the remote backends but the +:class:`.ExperimentData` class returned upon completion of an experiment does not +contain correct results. + +There are also times when you may want to rerun the analysis of a previously-run +experiment. You can instantiate this new :class:`.ExperimentData` object +with different options. Here's an example where we take an existing T1 experiment +and rerun it with a new analysis: + +exp = T1(qubit=0, delays=t1_delays) +... + +See Also +-------- + +* `Saving and loading experiment data with the cloud service `_ diff --git a/docs/howtos/newexperimentdata.rst b/docs/howtos/newexperimentdata.rst deleted file mode 100644 index 4b2ebc07cb..0000000000 --- a/docs/howtos/newexperimentdata.rst +++ /dev/null @@ -1,36 +0,0 @@ -How to instantiate a new experiment data object -=============================================== - -Problem -------- - -Sometimes due to events like a lost connection, the :class:`.ExperimentData` class that is returned -upon completion of an experiment may not contain correct results. There -are also times when you would like to rerun the analysis for existing experiments with -previously run jobs. - -Solution --------- - -If you want to instantiate a new experiment data object from an existing experiment and -jobs that finished execution successfully, you need to know the exact experiment you -ran and its options, as well as the IDs of the jobs that were executed. - -.. code-block:: python - - from qiskit_experiments.framework import ExperimentData - - # The same experiment that you ran - experiment = Experiment(**opts) - - # List of job IDs for the experiment - data = ExperimentData(job_ids=job_ids) - experiment.analysis.run(data) - data.block_for_results() - -``data`` will be the new experiment data class. - -See Also --------- - -* `Saving and loading experiment data with the cloud service `_ diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 4a47ccbffb..ebfd09f991 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -1,8 +1,13 @@ Writing a custom experiment =========================== -Qiskit Experiments is designed to be easily customizable. To create an experiment subclass -based on either the :class:`.BaseExperiment` class or an existing experiment, you should: +Qiskit Experiments is designed to be easily customizable. If you would like to +run an experiment that's similar to an existing experiment in the +:doc:`library `, you can subclass the existing experiment and analysis +classes. You can also write your own experiment class from the ground up by subclassing +the :class:`.BaseExperiment` class. We will discuss both cases in this tutorial. + +In general, to subclass :class:`.BaseExperiment` class, you should: - Implement the abstract :meth:`.BaseExperiment.circuits` method. This should return a list of :class:`~qiskit.QuantumCircuit` objects defining @@ -39,8 +44,7 @@ Optionally, to allow configuring experiment and execution options, you can overr Furthermore, some characterization and calibration experiments can be run with restless measurements, i.e. measurements where the qubits are not reset and circuits are executed immediately after the previous measurement. Here, the :class:`.RestlessMixin` class -can help -to set the appropriate run options and data processing chain. +can help to set the appropriate run options and data processing chain. Analysis Subclasses ------------------- @@ -69,15 +73,21 @@ Experiments library. The FineAmplitude Experiment ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The ``FineAmplitude`` calibration experiment optimizes gate amplitude by repeating the +The :class:`.FineAmplitude` experiment optimizes gate amplitude by repeating the gate pulse N times, hence amplifying the under- or over-rotations. This experiment can be performed for a variety of rotations, and subclasses are -provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as ``FineXAmplitude`` and ``FineSXAmplitude`` respectively. -These provided subclasses focus on the 0 <-> 1 transition, however, this experiment can also be performed for higher order transitions. +provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as +:class:`.FineXAmplitude`` and :class:`.FineSXAmplitude`` respectively. These provided subclasses +focus on the 0 <-> 1 transition, but this experiment can also be performed +for higher order transitions. Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates -schedules on transitions other than the 0 <-> 1 transition for the :math:`\pi` rotation. -In order to do this, we need to create a subclass as shown below. +schedules on the 1 <-> 2 transition for the :math:`\pi` rotation. To do this, we need to +excite the qubit to the :math:`|1\rangle` state and then execute the Usually we would need +to override the :meth:`~.BaseExperiment.circuits` method, but :class:`.FineAmplitude` +generates the preparation circuit generation in a separate method, +:meth:`~.FineAmplitude._pre_circuit`, which is combined with the main circuit in +:meth:`~.BaseExperiment.circuits`. Therefore, we only need to .. code-block:: @@ -128,6 +138,53 @@ You can try this for yourself and verify that your results are similar. Writing a new experiment ------------------------ +Based on the information so far, here is a barebones template to help you get started +with customization: + +.. code-block:: python + + class CustomExperiment(BaseExperiment): + """Custom experiment class template.""" + + def __init__(self, qubits=None): + """Initialize the experiment.""" + if qubits is None: + qubits = [0] + super().__init__(qubits, analysis=FakeAnalysis()) + + def circuits(self): + """Generate the list of circuits to be run.""" + return [] + + @classmethod + def _default_experiment_options(cls) -> Options: + """Update default experiment options here.""" + options = super()._default_experiment_options() + options.update_options( + dummy_option=None, + ) + return options + +And the corresponding analysis class template: + +.. code-block:: python + + class CustomAnalysis(BaseAnalysis): + """Custom analysis class template.""" + + def __init__(self, **kwargs): + super().__init__() + self._kwargs = kwargs + + def _run_analysis(self, experiment_data, **options): + seed = options.get("seed", None) + rng = np.random.default_rng(seed=seed) + analysis_results = [ + AnalysisResultData(f"result_{i}", value) for i, value in enumerate(rng.random(3)) + ] + return analysis_results, None + + Now we'll use what we've learned so far to make an entirely new experiment using the :class:`.BaseExperiment` template. diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 854435095c..3e13487d83 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -97,6 +97,9 @@ draw the first and last circuits for our T1 experiment: As expected, the delay block spans the full range of time values that we specified. +The ExperimentData class +======================== + After instantiating the experiment, we run the experiment by calling :meth:`~.BaseExperiment.run` with our specified backend. This returns the :class:`.ExperimentData` class containing the results of the experiment, so it's crucial that we assign the output to a data variable. We could have also provided the backend @@ -157,6 +160,9 @@ and the :meth:`~.ExperimentData.metadata` property: print(exp_data.data(0)['counts']) print(exp_data.metadata) +The actual backend jobs that were executed for the experiment can be accessed with the +:meth:`~.ExperimentData.jobs` method. + Setting experiment options ========================== diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index e515e3d8f0..43f2edea9e 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -72,13 +72,12 @@ class FineAmplitude(BaseExperiment, RestlessMixin): amp_cal = FineAmplitude([qubit], SXGate()) amp_cal.set_experiment_options( angle_per_gate=np.pi/2, - add_xp_circuit=False, - add_sx=False + phase_offset=np.pi ) amp_cal.run(backend) Note that there are subclasses of :class:`FineAmplitude` such as :class:`FineSXAmplitude` - that set the appropriate options by default. + that set the appropriate options for specific gates by default. # section: analysis_ref :class:`FineAmplitudeAnalysis` From e8b375556500b6675bb3ae49087364264616ec8e Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sun, 26 Feb 2023 01:32:44 -0500 Subject: [PATCH 28/64] trying fix for dataclass duplicate object description warning --- docs/conf.py | 9 +------ docs/guides/quantum_volume.rst | 8 +++--- docs/guides/randomized_benchmarking.rst | 1 - docs/guides/readout_mitigation.rst | 15 +++++------ docs/howtos/cloud_service.rst | 25 ++++++++++--------- docs/howtos/index.rst | 2 ++ docs/howtos/new_experimentdata.rst | 7 +++--- qiskit_experiments/curve_analysis/__init__.py | 2 ++ .../framework/experiment_data.py | 14 +++++------ .../characterization/local_readout_error.py | 5 ++-- qiskit_experiments/test/__init__.py | 6 ++--- 11 files changed, 47 insertions(+), 47 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 185dea29c2..ca10712cfc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -176,14 +176,7 @@ # Prepend warning for development docs: -if not os.getenv("EXPERIMENTS_DEV_DOCS", None): - rst_prolog = """ -.. raw:: html - -""".format( - release - ) -else: +if os.getenv("EXPERIMENTS_DEV_DOCS", None): rst_prolog = """ .. raw:: html diff --git a/docs/guides/quantum_volume.rst b/docs/guides/quantum_volume.rst index 176d31b5df..af20924c7d 100644 --- a/docs/guides/quantum_volume.rst +++ b/docs/guides/quantum_volume.rst @@ -183,7 +183,9 @@ https://arxiv.org/pdf/1811.12926 a superconducting quantum computing system, https://arxiv.org/pdf/2008.08571 -.. jupyter-execute:: +See also +-------- + +* API documentation: :mod:`~qiskit_experiments.library.quantum_volume` +* Qiskit Textbook: `Measuring Quantum Volume `__ - import qiskit.tools.jupyter - %qiskit_copyright diff --git a/docs/guides/randomized_benchmarking.rst b/docs/guides/randomized_benchmarking.rst index a97355ce2e..1610cfcaf2 100644 --- a/docs/guides/randomized_benchmarking.rst +++ b/docs/guides/randomized_benchmarking.rst @@ -346,4 +346,3 @@ See also * API documentation: :mod:`~qiskit_experiments.library.randomized_benchmarking` * Qiskit Textbook: `Randomized Benchmarking `__ - diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/readout_mitigation.rst index 785d156867..9c8e9a9847 100644 --- a/docs/guides/readout_mitigation.rst +++ b/docs/guides/readout_mitigation.rst @@ -51,7 +51,7 @@ experiments to generate the corresponding mitigators. .. jupyter-execute:: - SHOTS = 1024 + shots = 1024 qubits = [0,1,2,3] num_qubits = len(qubits) @@ -108,8 +108,8 @@ Mitigation Example .. jupyter-execute:: - counts = backend.run(qc, shots=SHOTS, seed_simulator=42, method="density_matrix").result().get_counts() - unmitigated_probs = {label: count / SHOTS for label, count in counts.items()} + counts = backend.run(qc, shots=shots, seed_simulator=42, method="density_matrix").result().get_counts() + unmitigated_probs = {label: count / shots for label, count in counts.items()} .. jupyter-execute:: @@ -136,7 +136,7 @@ Expectation value diagonals = [str2diag(d) for d in diagonal_labels] qubit_index = {i: i for i in range(num_qubits)} unmitigated_probs_vector, _ = counts_probability_vector(unmitigated_probs, qubit_index=qubit_index) - unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, SHOTS) for d in diagonals] + unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, shots) for d in diagonals] mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals] .. jupyter-execute:: @@ -171,7 +171,8 @@ a few qubits. print(c) -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :mod:`~qiskit_experiments.library.randomized_benchmarking` +* Qiskit Textbook: `Randomized Benchmarking `__ diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index 00355f9bbe..1478a8325d 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -1,5 +1,5 @@ -How to save and load experiment data with the cloud service -=========================================================== +Save and load experiment data with the cloud service +==================================================== .. note:: This recipe is only for those who have access to the cloud service. You can @@ -53,9 +53,9 @@ backend to be able to save it. You can save the experiment data by calling ``sav Loading ~~~~~~~ -Let’s load a `previous T1 -experiment `__, -which we’ve made public by editing the ``Share level`` field: +Let's load a `previous T1 +experiment `__ +(requires login to view), which we've made public by editing the ``Share level`` field: .. jupyter-execute:: :hide-output: @@ -129,9 +129,9 @@ The analysis results have been retrieved as well: Discussion ---------- -Calling ``save()`` before the experiment is complete will +Calling :meth:`.ExperimentData.save` before the experiment is complete will instantiate an experiment entry in the database, but it will not have -complete data. To fix this, you can call ``save()`` again once the +complete data. To fix this, you can call :meth:`~.ExperimentData.save` again once the experiment is done running. .. jupyter-execute:: @@ -175,7 +175,7 @@ analysis results, which for the :math:`T_1` experiment is the fitted .. image:: ./experiment_cloud_service/t1_experiment.png -The metadata field shows experiment metadata included in the ``ExperimentData`` object. +The metadata field shows experiment metadata included in the :class:`.ExperimentData` object. .. image:: ./experiment_cloud_service/metadata.png @@ -190,8 +190,8 @@ human to determine whether the result is acceptable. Auto-saving an experiment ~~~~~~~~~~~~~~~~~~~~~~~~~ -The ``auto_save`` feature automatically saves changes to the `ExperimentData` object to -the cloud service whenever it's updated. +The :meth:`.ExperimentData.auto_save` feature automatically saves changes to the +:class:`.ExperimentData` object to the cloud service whenever it's updated. .. jupyter-execute:: :hide-output: @@ -213,8 +213,9 @@ Deleting an experiment Both figures and analysis results can be deleted. Note that unless you have auto save on, the update has to be manually saved to the remote -database by calling ``save()``. Because there are two analysis results, one for -the T1 parameter and one for the curve fitting results, we delete twice. +database by calling :meth:`.ExperimentData.save`. Because there are two analysis +results, one for the T1 parameter and one for the curve fitting results, we must +delete twice to fully remove the analysis results. .. jupyter-execute:: :hide-output: diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index 97463e36d7..8cab0876f6 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -5,6 +5,8 @@ How-Tos This section of the documentation provides concrete recipes for how to do specific useful actions in Qiskit Experiments. +How to... + .. toctree:: :maxdepth: 1 :glob: diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst index 30c166952c..f0977496c6 100644 --- a/docs/howtos/new_experimentdata.rst +++ b/docs/howtos/new_experimentdata.rst @@ -1,5 +1,5 @@ -How to instantiate a new data object for an existing experiment -=============================================================== +Instantiate a new data object for an existing experiment +======================================================== Problem ------- @@ -25,7 +25,8 @@ ran and its options, as well as the IDs of the jobs that were executed. data = ExperimentData(job_ids=job_ids) experiment.analysis.run(data) - # Blocks execution of subsequent code until analysis is complete + + # Block execution of subsequent code until analysis is complete data.block_for_results() ``data`` will be the new experiment data object. diff --git a/qiskit_experiments/curve_analysis/__init__.py b/qiskit_experiments/curve_analysis/__init__.py index e0e3451c19..31a9ae6ad4 100644 --- a/qiskit_experiments/curve_analysis/__init__.py +++ b/qiskit_experiments/curve_analysis/__init__.py @@ -38,6 +38,8 @@ .. autosummary:: :toctree: ../stubs/ + :exclude-members: CurveData.data_allocation, CurveData.labels, CurveData.shots, + CurveData.x, CurveData.y, CurveData.y_err, CurveData.y_err SeriesDef CurveData diff --git a/qiskit_experiments/framework/experiment_data.py b/qiskit_experiments/framework/experiment_data.py index 8da1056140..4377c237cc 100644 --- a/qiskit_experiments/framework/experiment_data.py +++ b/qiskit_experiments/framework/experiment_data.py @@ -143,18 +143,16 @@ def _repr_svg_(self): class ExperimentData: - """Qiskit Experiments Data container class. + """Experiment data container class. This class handles the following: - 1. Storing the data related to an experiment - the experiment's metadata, - the analysis results and the figures + 1. Storing the data related to an experiment - the experiment's metadata, + the analysis results and the figures + 2. Managing jobs and adding data from jobs automatically + 3. Saving/Loading data from the result database - 2. Managing jobs and adding data from jobs automatically - - 3. Saving/Loading data from the result database - - The field `db_data` is a dataclass (`ExperimentDataclass`) containing + The field ``db_data`` is a dataclass (``ExperimentDataclass``) containing all the data that can be stored in the database and loaded from it, and as such is subject to strict conventions. diff --git a/qiskit_experiments/library/characterization/local_readout_error.py b/qiskit_experiments/library/characterization/local_readout_error.py index f53f122074..e73b74f88d 100644 --- a/qiskit_experiments/library/characterization/local_readout_error.py +++ b/qiskit_experiments/library/characterization/local_readout_error.py @@ -25,10 +25,11 @@ class LocalReadoutError(BaseExperiment): - r"""Class for local readout error characterization experiment + r"""Class for local readout error characterization experiment. + # section: overview - This class constructs the a :class:`~qiskit.result.LocalReadoutMitigator` containing sequence + This class constructs a :class:`~qiskit.result.LocalReadoutMitigator` containing a sequence of assignment matrices :math:`A` characterizing the readout error for the given qubits from the experiment results. The full assignment matrix is accessible via the :meth:`~qiskit.result.LocalReadoutMitigator.assignment_matrix` method. diff --git a/qiskit_experiments/test/__init__.py b/qiskit_experiments/test/__init__.py index 4ed8c150c1..651bd96e45 100644 --- a/qiskit_experiments/test/__init__.py +++ b/qiskit_experiments/test/__init__.py @@ -11,9 +11,9 @@ # that they have been altered from the originals. """ -================================================================== -Qiskit Experiments Test Utilities (:mod:`qiskit_experiments.test`) -================================================================== +=============================================== +Test Utilities (:mod:`qiskit_experiments.test`) +=============================================== .. currentmodule:: qiskit_experiments.test From 1a8405a3cd602e130deaa5cb304afbec0e1f0856 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sun, 26 Feb 2023 05:26:43 -0500 Subject: [PATCH 29/64] revert test change and updated howtos --- docs/guides/restless_measurements.rst | 6 +- docs/guides/t1_experiment.rst | 7 +- docs/guides/t2ramsey_characterization.rst | 4 +- docs/howtos/job_splitting.rst | 31 +++++++-- docs/howtos/new_experimentdata.rst | 34 +++++++--- docs/tutorials/calibrations.rst | 2 + qiskit_experiments/curve_analysis/__init__.py | 2 - qiskit_experiments/library/__init__.py | 64 +++++++++++++++---- .../library/calibration/rough_drag_cal.py | 4 ++ .../library/characterization/drag.py | 2 +- .../characterization/fine_amplitude.py | 6 +- .../library/characterization/rabi.py | 2 +- .../library/characterization/t2hahn.py | 2 +- .../library/characterization/t2ramsey.py | 2 +- .../library/characterization/tphi.py | 2 +- 15 files changed, 125 insertions(+), 45 deletions(-) diff --git a/docs/guides/restless_measurements.rst b/docs/guides/restless_measurements.rst index f8cd090a1e..4b342050d4 100644 --- a/docs/guides/restless_measurements.rst +++ b/docs/guides/restless_measurements.rst @@ -1,7 +1,7 @@ Restless Measurements ===================== -When running circuits the qubits are typically reset to the ground state after +When running circuits, the qubits are typically reset to the ground state after each measurement to ensure that the next circuit has a well-defined initial state. This can be done passively by waiting several :math:`T_1`-times so that qubits in the excited state decay to :math:`\left\vert0\right\rangle`. Since :math:`T_1`-times @@ -48,13 +48,13 @@ respectively. This is why restless measurements need special post-processing. Enabling restless measurements ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In Qiskit Experiments the experiments that support restless measurements +In Qiskit Experiments, the experiments that support restless measurements have a special method :meth:`~.RestlessMixin.enable_restless` to set the restless run options and define the data processor that will process the measured data. If you are an experiment developer, you can add the :class:`.RestlessMixin` to your experiment class to add support for restless measurements. Here, we will show how to activate restless measurements using -a fake backend and a rough Drag experiment. Note however, that you will not +a fake backend and a rough DRAG experiment. Note however, that you will not observe any meaningful outcomes with fake backends since the circuit simulator they use always starts with the qubits in the ground state. diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_experiment.rst index e3c6e1337c..e828e84394 100644 --- a/docs/guides/t1_experiment.rst +++ b/docs/guides/t1_experiment.rst @@ -136,7 +136,8 @@ that is close to a logical value '0'. for result in expdataT1_kerneled.analysis_results(): print(result) -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :mod:`~qiskit_experiments.library.t1` +* Qiskit Textbook: `Randomized Benchmarking `__ diff --git a/docs/guides/t2ramsey_characterization.rst b/docs/guides/t2ramsey_characterization.rst index 4e24c0933d..96911369ba 100644 --- a/docs/guides/t2ramsey_characterization.rst +++ b/docs/guides/t2ramsey_characterization.rst @@ -2,12 +2,12 @@ T2 Ramsey Characterization ========================== The purpose of the :math:`T_2`\ Ramsey experiment is to determine two of -the qubit’s properties: *Ramsey* or *detuning frequency* and +the qubit's properties: *Ramsey* or *detuning frequency* and :math:`T_2^\ast`. The rough frequency of the qubit was already determined previously. The control pulses are based on this frequency. In this experiment, we would like to get a more precise estimate of the -qubit’s frequency. The difference between the frequency used for the +qubit's frequency. The difference between the frequency used for the control rotation pulses, and the precise frequency is called the *detuning frequency*. This part of the experiment is called a *Ramsey Experiment*. :math:`T_2^\ast` represents the rate of decay toward a diff --git a/docs/howtos/job_splitting.rst b/docs/howtos/job_splitting.rst index cc7b53b342..47b35802d8 100644 --- a/docs/howtos/job_splitting.rst +++ b/docs/howtos/job_splitting.rst @@ -1,18 +1,39 @@ -How to control the splitting of experiment circuits into jobs -============================================================= +Control the splitting of experiment circuits into jobs +====================================================== Problem ------- -You want to control how many jobs an experiment is split into when running on a backend. +You want to manually control how an experiment is split into jobs when running on +a backend. Solution -------- +There are two experiment options relevant to custom job splitting. +You can set the ``max_circuits`` option manually when running an experiment: + +.. code-block:: python + + exp = Experiment([0]) + exp.set_experiment_options(max_circuits=100) + +The experiment class will split its circuits into jobs such that no job has more than +``max_circuits`` number of jobs. + +Furthermore, the :class:`.BatchExperiment` class has the experiment option ``separate_jobs`` +which will run circuits of different sub-experiments in different jobs. Note that the purpose of +:class:`.ParallelExperiment` is to run circuits on disjoint sets of qubits simultaneously, +therefore this option is not of much use for parallel experiments. + Discussion ---------- Qiskit Experiments will automatically split circuits across jobs for you for backends -that have a maximum circuit number per circuit, which is given by `max_circuits` property in :meth:`qiskit.providers.BackendV1.configuration` for V1 backends and :meth:`qiskit.providers.BackendV2.max_circuits` for V2. This should +that have a maximum circuit number per circuit, which is given by the ``max_experiments`` +property of :meth:`qiskit.providers.BackendV1.configuration` for V1 backends and +:attr:`qiskit.providers.BackendV2.max_circuits` for V2. This should work automatically in most cases, but there may be some backends where other limits -exist. \ No newline at end of file +exist. When the ``max_circuits`` experiment option is provided, the experiment class +will split the experiment circuits as dictated by the smaller of the backend property +and the experiment option. \ No newline at end of file diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst index f0977496c6..9e59dda544 100644 --- a/docs/howtos/new_experimentdata.rst +++ b/docs/howtos/new_experimentdata.rst @@ -11,7 +11,8 @@ Solution -------- Use the code template below. You need to know the exact experiment you -ran and its options, as well as the IDs of the jobs that were executed. +ran and its options, the IDs of the jobs that were executed. You also need the +``backend`` object corresponding to the backend that the experiment was run on. .. code-block:: python @@ -23,7 +24,8 @@ ran and its options, as well as the IDs of the jobs that were executed. # List of job IDs for the experiment job_ids= [job1, job2, ...] - data = ExperimentData(job_ids=job_ids) + data = ExperimentData(experiment = experiment) + data.add_jobs([backend.retrieve_job(job_id) for job_id in job_ids]) experiment.analysis.run(data) # Block execution of subsequent code until analysis is complete @@ -39,13 +41,29 @@ where the jobs may have finished running on the remote backends but the :class:`.ExperimentData` class returned upon completion of an experiment does not contain correct results. -There are also times when you may want to rerun the analysis of a previously-run -experiment. You can instantiate this new :class:`.ExperimentData` object -with different options. Here's an example where we take an existing T1 experiment -and rerun it with a new analysis: +You may also want to rerun the analysis of a previously-run experiment with different +options when you instantiate this new :class:`.ExperimentData` object. +Here's a code snippet where we reconstruct a parallel experiment +consisting of randomized benchmarking experiments, then change the gate error ratio +as well as the line plot color of the first component experiment. -exp = T1(qubit=0, delays=t1_delays) -... +.. code-block:: python + + pexp = ParallelExperiment([ + StandardRB((i,), np.arange(1, 800, 200), num_samples=10) for i in range(2)]) + + pexp.analysis.options.gate_error_ratio = {"x": 10, "sx": 1, "rz": 0} + + pexp.analysis.component_analysis(0).options.gate_error_ratio = {"x": 10, "sx": 1, "rz": 0} + pexp.analysis.component_analysis(0).plotter.figure_options.series_params.update( + { + "rb_decay": {"color": "r"} + } + ) + + data = ExperimentData(experiment=pexp) + data.add_jobs([backend_real.retrieve_job(job_id) for job_id in job_ids]) + pexp.analysis.run(data) See Also -------- diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index 84f542d2f3..29f0196e12 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -208,6 +208,7 @@ whether a values is valid or not and the experiment id that produce a value. pd.DataFrame(**cals.parameters_table(qubit_list=[qubit]))[columns_to_show] +.. _Rabi Calibration: Calibrating the pulse amplitudes with a Rabi experiment ------------------------------------------------------- @@ -293,6 +294,7 @@ By contrast, the value of the parameters will be recovered from the file. pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] +.. _DRAG Calibration: Calibrating the value of the DRAG coefficient --------------------------------------------- diff --git a/qiskit_experiments/curve_analysis/__init__.py b/qiskit_experiments/curve_analysis/__init__.py index 31a9ae6ad4..e0e3451c19 100644 --- a/qiskit_experiments/curve_analysis/__init__.py +++ b/qiskit_experiments/curve_analysis/__init__.py @@ -38,8 +38,6 @@ .. autosummary:: :toctree: ../stubs/ - :exclude-members: CurveData.data_allocation, CurveData.labels, CurveData.shots, - CurveData.x, CurveData.y, CurveData.y_err, CurveData.y_err SeriesDef CurveData diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index a82e014cf0..4f9ae34fb1 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -42,13 +42,13 @@ ~tomography.MitigatedProcessTomography ~quantum_volume.QuantumVolume -.. _characterization: +.. _characterization single qubit: -Characterization Experiments -============================ +Characterization Experiments: Single Qubit +========================================== -Experiments for characterization of qubits and quantum device properties. -Some experiments may be also used for gate calibration. +Experiments for characterization of properties of individual qubits. +Some experiments also have a calibration experiment version. .. autosummary:: :toctree: ../stubs/ @@ -60,12 +60,6 @@ ~characterization.Tphi ~characterization.QubitSpectroscopy ~characterization.EFSpectroscopy - ~characterization.CrossResonanceHamiltonian - ~characterization.EchoedCrossResonanceHamiltonian - ~characterization.RoughDrag - ~characterization.FineDrag - ~characterization.FineXDrag - ~characterization.FineSXDrag ~characterization.HalfAngle ~characterization.FineAmplitude ~characterization.FineXAmplitude @@ -76,12 +70,54 @@ ~characterization.RamseyXY ~characterization.FineFrequency ~characterization.ReadoutAngle - ~characterization.LocalReadoutError - ~characterization.CorrelatedReadoutError ~characterization.ResonatorSpectroscopy - ~characterization.ZZRamsey ~characterization.MultiStateDiscrimination +.. _characterization two qubits: + +Characterization Experiments: Two Qubits +======================================== + +Experiments for characterization of properties of two qubit interactions. + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ~characterization.CrossResonanceHamiltonian + ~characterization.EchoedCrossResonanceHamiltonian + ~characterization.ZZRamsey + +.. _characterization pulse gates: + +Characterization Experiments: Pulse Gates +========================================= + +Experiments for characterization of pulse gates. +Some experiments also have a calibration experiment version. + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ~characterization.RoughDrag + ~characterization.FineDrag + ~characterization.FineXDrag + ~characterization.FineSXDrag + +.. _characterization-mitigation: + +Characterization Experiments: Mitigation +======================================== + +Experiments for characterizing and mitigating readout error. + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ~characterization.LocalReadoutError + ~characterization.CorrelatedReadoutError .. _calibration: diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py index 9abcc2e078..cbebf46e4e 100644 --- a/qiskit_experiments/library/calibration/rough_drag_cal.py +++ b/qiskit_experiments/library/calibration/rough_drag_cal.py @@ -32,6 +32,10 @@ class RoughDragCal(BaseCalibrationExperiment, RoughDrag): # section: see_also :class:`.RoughDrag` + + # section: tutorial + :ref:`DRAG Calibration` + """ @qubit_deprecate() diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index 654651c5ef..fa28f402b0 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -71,7 +71,7 @@ class RoughDrag(BaseExperiment, RestlessMixin): .. ref_arxiv:: 3 1509.05470 # section: tutorial - :doc:`/tutorials/calibrations` + :ref:`DRAG Calibration` """ diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index 43f2edea9e..0a72399885 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -86,7 +86,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): .. ref_arxiv:: 1 1504.06597 # section: tutorial - :doc:`/tutorials/calibrations` + :doc:`/guides/calibrations` """ @@ -345,10 +345,10 @@ class FineZXAmplitude(FineAmplitude): # section: example - To run this experiment the user will have to provide the instruction schedule + To run this experiment, the user will have to provide the instruction schedule map in the transpile options that contains the schedule for the experiment. - ..code-block:: python + .. code-block:: python qubits = (1, 2) inst_map = InstructionScheduleMap() diff --git a/qiskit_experiments/library/characterization/rabi.py b/qiskit_experiments/library/characterization/rabi.py index de393a9ac6..c8dc2196f7 100644 --- a/qiskit_experiments/library/characterization/rabi.py +++ b/qiskit_experiments/library/characterization/rabi.py @@ -48,7 +48,7 @@ class Rabi(BaseExperiment, RestlessMixin): free parameter, i.e. the amplitude to scan and a drive channel which matches the qubit. # section: tutorial - :doc:`/tutorials/calibrating_real_device` + :ref:`Rabi Calibration` See also `Qiskit Textbook `_ diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py index 3c56df3357..017b99e798 100644 --- a/qiskit_experiments/library/characterization/t2hahn.py +++ b/qiskit_experiments/library/characterization/t2hahn.py @@ -54,7 +54,7 @@ class T2Hahn(BaseExperiment): The circuits are run on the device or on a simulator backend. # section: tutorial - :doc:`/tutorials/t2hahn_characterization` + :doc:`/guides/t2hahn_characterization` # section: analysis_ref :class:`T2HahnAnalysis` diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py index 7a1ececa0e..4d4494542b 100644 --- a/qiskit_experiments/library/characterization/t2ramsey.py +++ b/qiskit_experiments/library/characterization/t2ramsey.py @@ -54,7 +54,7 @@ class T2Ramsey(BaseExperiment): The circuits are run on the device or on a simulator backend. # section: tutorial - :doc:`/tutorials/t2ramsey_characterization` + :doc:`/guides/t2ramsey_characterization` # section: analysis_ref :class:`T2RamseyAnalysis` diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index 6d04931baf..fbd922d0c1 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -49,7 +49,7 @@ class Tphi(BatchExperiment): .. ref_arxiv:: 1 1904.06560v5 # section: tutorial - :doc:`/tutorials/tphi_characterization` + :doc:`/guides/tphi_characterization` """ def set_experiment_options(self, **fields): From e4ca9c405e4a16165fb4a6350b2cc0c68987608e Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 27 Feb 2023 03:19:53 -0500 Subject: [PATCH 30/64] addressing review comments --- docs/conf.py | 5 +- docs/guides/readout_mitigation.rst | 5 +- docs/guides/restless_measurements.rst | 6 +- docs/guides/state_tomography.rst | 13 ++- docs/guides/t1_experiment.rst | 3 +- docs/guides/t2hahn_characterization.rst | 2 +- docs/guides/t2ramsey_characterization.rst | 22 ++--- docs/guides/tphi_characterization.rst | 6 +- docs/howtos/cloud_service.rst | 75 +++++++----------- docs/howtos/index.rst | 8 +- docs/howtos/job_splitting.rst | 13 ++- docs/howtos/new_experimentdata.rst | 10 +-- docs/index.rst | 35 +++----- docs/tutorials/custom_experiment.rst | 39 ++++----- docs/tutorials/getting_started.rst | 59 +++++++++----- .../tutorials/images/compositeexperiments.png | Bin 673448 -> 677880 bytes docs/tutorials/index.rst | 2 + docs/tutorials/intro.rst | 2 + docs/tutorials/visualization.rst | 36 +++++---- qiskit_experiments/__init__.py | 3 - qiskit_experiments/library/__init__.py | 21 +---- 21 files changed, 173 insertions(+), 192 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index ca10712cfc..bc02b97c0d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -149,7 +149,10 @@ # html_theme = "qiskit_sphinx_theme" # use the theme in subdir 'theme' -html_context = {"analytics_enabled": True} # enable built-in analytics +html_context = { + "analytics_enabled": True, + "expandable_sidebar": True, +} # enable built-in analytics # html_sidebars = {'**': ['globaltoc.html']} html_last_updated_fmt = "%Y/%m/%d" diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/readout_mitigation.rst index 9c8e9a9847..cfe5e1b613 100644 --- a/docs/guides/readout_mitigation.rst +++ b/docs/guides/readout_mitigation.rst @@ -174,5 +174,6 @@ a few qubits. See also -------- -* API documentation: :mod:`~qiskit_experiments.library.randomized_benchmarking` -* Qiskit Textbook: `Randomized Benchmarking `__ +* API documentation: :mod:`~qiskit_experiments.library.characterization.LocalReadoutError`, + :mod:`~qiskit_experiments.library.characterization.CorrelatedReadoutError` +* Qiskit Textbook: `Measurement Error Mitigation `__ diff --git a/docs/guides/restless_measurements.rst b/docs/guides/restless_measurements.rst index 4b342050d4..77cec89412 100644 --- a/docs/guides/restless_measurements.rst +++ b/docs/guides/restless_measurements.rst @@ -207,7 +207,7 @@ Jay M. Gambetta, Blake R. Johnson, Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers, https://arxiv.org/abs/2110.14108 -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright \ No newline at end of file +* API documentation: :mod:`~qiskit_experiments.framework.RestlessMixin` diff --git a/docs/guides/state_tomography.rst b/docs/guides/state_tomography.rst index 0d947d7248..c0f17d7c4c 100644 --- a/docs/guides/state_tomography.rst +++ b/docs/guides/state_tomography.rst @@ -17,7 +17,7 @@ Quantum State Tomography State Tomography Experiment --------------------------- -To run a state tomography experiment we initialize the experiment with a +To run a state tomography experiment, we initialize the experiment with a circuit to prepare the state to be measured. We can also pass in an ``Operator``, or a ``Statevector`` to describe the preparation circuit. @@ -146,7 +146,7 @@ PSD without requiring rescaling. Parallel Tomography Experiment ------------------------------ -We can also use the ``qiskit_experiments.ParallelExperiment`` class to +We can also use the :class:`.ParallelExperiment` class to run subsystem tomography on multiple qubits in parallel. For example if we want to perform 1-qubit QST on several qubits at once: @@ -168,7 +168,7 @@ For example if we want to perform 1-qubit QST on several qubits at once: for result in pardata.analysis_results(): print(result) -View component experiment analysis results +View component experiment analysis results: .. jupyter-execute:: @@ -180,8 +180,7 @@ View component experiment analysis results print("State Fidelity: {:.5f}".format(fid_result_i.value)) print("State: {}".format(state_result_i.value)) +See also +-------- -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :mod:`~qiskit_experiments.library.tomography.StateTomography` diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_experiment.rst index e828e84394..396257645f 100644 --- a/docs/guides/t1_experiment.rst +++ b/docs/guides/t1_experiment.rst @@ -139,5 +139,4 @@ that is close to a logical value '0'. See also -------- -* API documentation: :mod:`~qiskit_experiments.library.t1` -* Qiskit Textbook: `Randomized Benchmarking `__ +* API documentation: :mod:`~qiskit_experiments.library.characterization.T1` diff --git a/docs/guides/t2hahn_characterization.rst b/docs/guides/t2hahn_characterization.rst index a7f340e765..3a5449d209 100644 --- a/docs/guides/t2hahn_characterization.rst +++ b/docs/guides/t2hahn_characterization.rst @@ -239,4 +239,4 @@ is close to the estimate of the one echo experiment. See also -------- -* API documentation: :class:`.T2Hahn` \ No newline at end of file +* API documentation: :mod:`~qiskit_experiments.library.characterization.T2Hahn` diff --git a/docs/guides/t2ramsey_characterization.rst b/docs/guides/t2ramsey_characterization.rst index 96911369ba..bf54fdd69c 100644 --- a/docs/guides/t2ramsey_characterization.rst +++ b/docs/guides/t2ramsey_characterization.rst @@ -1,7 +1,7 @@ T2 Ramsey Characterization ========================== -The purpose of the :math:`T_2`\ Ramsey experiment is to determine two of +The purpose of the :math:`T_2` Ramsey experiment is to determine two of the qubit's properties: *Ramsey* or *detuning frequency* and :math:`T_2^\ast`. The rough frequency of the qubit was already determined previously. The control pulses are based on this frequency. @@ -25,15 +25,15 @@ frequency* (``osc_freq`` parameter). import qiskit from qiskit_experiments.library import T2Ramsey -The circuit used for the experiment comprises the following: +The circuits used for the experiment comprise the following steps: -:: +#. Hadamard gate +#. Delay +#. RZ gate that rotates the qubit in the x-y plane +#. Hadamard gate +#. Measurement - 1. Hadamard gate - 2. delay - 3. RZ gate that rotates the qubit in the x-y plane - 4. Hadamard gate - 5. measurement +| The user provides as input a series of delays (in seconds) and the oscillation frequency (in Hz). During the delay, we expect the qubit to @@ -140,7 +140,7 @@ computed for other qubits. print(result) -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :mod:`~qiskit_experiments.library.characterization.T2Ramsey` diff --git a/docs/guides/tphi_characterization.rst b/docs/guides/tphi_characterization.rst index 3c84dcfeab..ce90045ed3 100644 --- a/docs/guides/tphi_characterization.rst +++ b/docs/guides/tphi_characterization.rst @@ -79,8 +79,8 @@ we compute the results for :math:`T_\varphi.` display(expdata.figure(1)) -.. jupyter-execute:: +See also +-------- - import qiskit.tools.jupyter - %qiskit_copyright +* API documentation: :mod:`~qiskit_experiments.library.characterization.Tphi` diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index 1478a8325d..e8185f50c7 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -21,14 +21,7 @@ You will need to have :mod:`qiskit-ibmq-provider` installed locally and an account in the Qiskit cloud service. You must run the experiment on a real IBM backend to be able to save it. You can save the experiment data by calling ``save()``: -.. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - -.. jupyter-execute:: +.. jupyter-input:: from qiskit import IBMQ IBMQ.load_account() @@ -45,10 +38,10 @@ backend to be able to save it. You can save the experiment data by calling ``sav t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() t1_expdata.save() -.. jupyter-execute:: - :hide-code: +.. jupyter-output:: - print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") + You can view the experiment online at + https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704 Loading ~~~~~~~ @@ -79,17 +72,14 @@ To display the figure, which is serialized into a string, we need the The analysis results have been retrieved as well: -.. jupyter-execute:: - :hide-output: - :raises: +.. jupyter-input:: for result in load_expdata.analysis_results(): print(result) -.. jupyter-execute:: - :hide-code: +.. jupyter-output:: - print("""AnalysisResult + AnalysisResult - name: T1 - value: 0.0001040+/-0.0000028 - χ²: 0.8523786276663019 @@ -124,7 +114,7 @@ The analysis results have been retrieved as well: * (amp, tau) = 0.09302612202500576 - quality: good - device_components: ['Q0'] - - verified: False""") + - verified: False Discussion ---------- @@ -134,15 +124,7 @@ instantiate an experiment entry in the database, but it will not have complete data. To fix this, you can call :meth:`~.ExperimentData.save` again once the experiment is done running. -.. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - - -.. jupyter-execute:: +.. jupyter-input:: from qiskit import IBMQ IBMQ.load_account() @@ -152,10 +134,9 @@ experiment is done running. t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() t1_expdata.save() -.. jupyter-execute:: - :hide-code: +.. jupyter-output:: - print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") + You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") You can also view the results at the `IBM Quantum Experiments @@ -169,16 +150,22 @@ by device, date, provider, and result by clicking on the filter icon. .. image:: ./experiment_cloud_service/filter.png +| + Individual experiment pages show the plot, and one or more important analysis results, which for the :math:`T_1` experiment is the fitted :math:`T_1` value. .. image:: ./experiment_cloud_service/t1_experiment.png +| + The metadata field shows experiment metadata included in the :class:`.ExperimentData` object. .. image:: ./experiment_cloud_service/metadata.png +| + You can change the quality and verify/unverify the results upon selection of an analysis result. Quality is an automatic parameter generated by the experiment analysis based on pre-set criteria. The verification field is for a @@ -193,8 +180,7 @@ Auto-saving an experiment The :meth:`.ExperimentData.auto_save` feature automatically saves changes to the :class:`.ExperimentData` object to the cloud service whenever it's updated. -.. jupyter-execute:: - :hide-output: +.. jupyter-input:: exp = T1(qubit=0, delays=t1_delays) @@ -202,11 +188,10 @@ The :meth:`.ExperimentData.auto_save` feature automatically saves changes to the t1_expdata.auto_save = True t1_expdata.block_for_results() -.. jupyter-execute:: - :hide-code: +.. jupyter-output:: - print("You can view the experiment online at https://quantum-computing.ibm.com/experiments/cdaff3fa-f621-4915-a4d8-812d05d9a9ca") - print("") + You can view the experiment online at https://quantum-computing.ibm.com/experiments/cdaff3fa-f621-4915-a4d8-812d05d9a9ca + Deleting an experiment ~~~~~~~~~~~~~~~~~~~~~~ @@ -217,24 +202,22 @@ database by calling :meth:`.ExperimentData.save`. Because there are two analysis results, one for the T1 parameter and one for the curve fitting results, we must delete twice to fully remove the analysis results. -.. jupyter-execute:: - :hide-output: +.. jupyter-input:: t1_expdata.delete_figure(0) t1_expdata.delete_analysis_result(0) t1_expdata.delete_analysis_result(0) -.. jupyter-execute:: - :hide-code: +.. jupyter-output:: - print("Are you sure you want to delete the experiment plot? [y/N]: y") - print("Are you sure you want to delete the analysis result? [y/N]: y") - print("Are you sure you want to delete the analysis result? [y/N]: y") + Are you sure you want to delete the experiment plot? [y/N]: y + Are you sure you want to delete the analysis result? [y/N]: y + Are you sure you want to delete the analysis result? [y/N]: y The web interface shows that both the figure and analysis result have been -deleted: |t1_deleted.png| +deleted: -.. |t1_deleted.png| image:: ./experiment_cloud_service/t1_deleted.png +.. image:: ./experiment_cloud_service/t1_deleted.png Tagging and sharing experiments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -242,7 +225,7 @@ Tagging and sharing experiments Tags and notes can be added to experiments to help identify specific experiments in the interface. For example, an experiment can be tagged and made public with the following code. -.. jupyter-execute:: +.. jupyter-input:: t1_expdata.tags = ['tag1', 'tag2'] t1_expdata.share_level = "public" diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index 8cab0876f6..cb506f87a9 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -3,17 +3,17 @@ How-Tos ####### This section of the documentation provides concrete recipes for how to do specific -useful actions in Qiskit Experiments. - -How to... +useful actions in Qiskit Experiments. It is recommended that you first familiarize +with :ref:`the basics ` of the package before using these recipes. .. toctree:: + :caption: How to... :maxdepth: 1 :glob: * - +| .. Hiding - Indices and tables diff --git a/docs/howtos/job_splitting.rst b/docs/howtos/job_splitting.rst index 47b35802d8..36d460b0b2 100644 --- a/docs/howtos/job_splitting.rst +++ b/docs/howtos/job_splitting.rst @@ -13,7 +13,7 @@ Solution There are two experiment options relevant to custom job splitting. You can set the ``max_circuits`` option manually when running an experiment: -.. code-block:: python +.. jupyter-input:: exp = Experiment([0]) exp.set_experiment_options(max_circuits=100) @@ -22,8 +22,15 @@ The experiment class will split its circuits into jobs such that no job has more ``max_circuits`` number of jobs. Furthermore, the :class:`.BatchExperiment` class has the experiment option ``separate_jobs`` -which will run circuits of different sub-experiments in different jobs. Note that the purpose of -:class:`.ParallelExperiment` is to run circuits on disjoint sets of qubits simultaneously, +which will run circuits of different sub-experiments in different jobs: + +.. jupyter-input:: + + batch_exp = BatchExperiment([exp, exp]) + batch_exp.set_experiment_options(separate_jobs=True) + +Note that the purpose of :class:`.ParallelExperiment` is to run +circuits on disjoint sets of qubits simultaneously, therefore this option is not of much use for parallel experiments. Discussion diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst index 9e59dda544..a4908bece4 100644 --- a/docs/howtos/new_experimentdata.rst +++ b/docs/howtos/new_experimentdata.rst @@ -14,7 +14,7 @@ Use the code template below. You need to know the exact experiment you ran and its options, the IDs of the jobs that were executed. You also need the ``backend`` object corresponding to the backend that the experiment was run on. -.. code-block:: python +.. jupyter-input:: from qiskit_experiments.framework import ExperimentData @@ -47,14 +47,14 @@ Here's a code snippet where we reconstruct a parallel experiment consisting of randomized benchmarking experiments, then change the gate error ratio as well as the line plot color of the first component experiment. -.. code-block:: python +.. jupyter-input:: pexp = ParallelExperiment([ StandardRB((i,), np.arange(1, 800, 200), num_samples=10) for i in range(2)]) - pexp.analysis.options.gate_error_ratio = {"x": 10, "sx": 1, "rz": 0} - - pexp.analysis.component_analysis(0).options.gate_error_ratio = {"x": 10, "sx": 1, "rz": 0} + pexp.analysis.component_analysis(0).options.gate_error_ratio = { + "x": 10, "sx": 1, "rz": 0 + } pexp.analysis.component_analysis(0).plotter.figure_options.series_params.update( { "rb_decay": {"color": "r"} diff --git a/docs/index.rst b/docs/index.rst index bb72684978..bf5acb95e0 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,20 +26,17 @@ We've divided up the documentation into four sections with different purposes: ^^^^^^^^^ These step-by-step tutorials teach the fundamentals of the package and - are suitable for getting started. + are suitable for getting started. You'll find in these tutorials: - You'll find in these tutorials: - - * An overview of the package structure - * What constitutes an experiment - * How to write your own experiment + * An overview of the :ref:`package structure ` + * How to :doc:`install the package and run your first experiment ` + * How to :doc:`write your own experiment ` +++ .. button-ref:: tutorials/index :expand: :color: secondary - :click-parent: To the learning tutorials @@ -50,19 +47,16 @@ We've divided up the documentation into four sections with different purposes: ^^^^^^^ These individual how-to recipes provide short and direct solutions to some commonly - asked questions for Qiskit Experiments users. - - You'll find in these recipes: + asked questions for Qiskit Experiments users. You'll find in these recipes: - * How to save and retrieve experiment data - * How to customize the appearance of your figures + * How to :doc:`instantiate experiment data for an existing experiment ` + * How to :doc:`customize the splitting of circuits into jobs ` +++ .. button-ref:: howtos/index :expand: :color: secondary - :click-parent: To the how-to recipes @@ -73,18 +67,15 @@ We've divided up the documentation into four sections with different purposes: ^^^^^^^^^^^^^^^^^ These are in-depth guides to key experiments in the package, describing - their background, principle, and how to run them in Qiskit Experiments. - - You'll find in these guides: + their background, principle, and how to run them in Qiskit Experiments. You'll find in these guides: - * What is randomized benchmarking and when is it useful + * What is :doc:`randomized benchmarking ` and when is it useful +++ .. button-ref:: guides/index :expand: :color: secondary - :click-parent: To the experiment guides @@ -97,18 +88,15 @@ We've divided up the documentation into four sections with different purposes: This is a detailed description of every module, method, and function in Qiskit Experiments and how to use them, suitable for those working closely - with specific parts of the package or writing your custom code. - - You'll find in these references: + with specific parts of the package or writing your custom code. You'll find in these references: - * What are all possible input parameters to CurveAnalysis + * What are all possible input parameters to the :class:`.CurveAnalysis` class +++ .. button-ref:: apidocs/index :expand: :color: secondary - :click-parent: To the API reference @@ -120,6 +108,7 @@ We've divided up the documentation into four sections with different purposes: guides/index apidocs/index release_notes + GitHub .. Hiding - Indices and tables :ref:`genindex` diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index ebfd09f991..6658b0c1a1 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -77,7 +77,7 @@ The :class:`.FineAmplitude` experiment optimizes gate amplitude by repeating the gate pulse N times, hence amplifying the under- or over-rotations. This experiment can be performed for a variety of rotations, and subclasses are provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as -:class:`.FineXAmplitude`` and :class:`.FineSXAmplitude`` respectively. These provided subclasses +:class:`.FineXAmplitude` and :class:`.FineSXAmplitude` respectively. These provided subclasses focus on the 0 <-> 1 transition, but this experiment can also be performed for higher order transitions. @@ -89,7 +89,7 @@ generates the preparation circuit generation in a separate method, :meth:`~.FineAmplitude._pre_circuit`, which is combined with the main circuit in :meth:`~.BaseExperiment.circuits`. Therefore, we only need to -.. code-block:: +.. jupyter-input:: class HigherOrderFineXAmplitude(FineXAmplitude): def _pre_circuit(self) -> QuantumCircuit: @@ -135,13 +135,12 @@ You can try this for yourself and verify that your results are similar. - device_components: ['Q0'] - verified: False -Writing a new experiment ------------------------- +Custom experiment template +-------------------------- -Based on the information so far, here is a barebones template to help you get started -with customization: +Here is a barebones template to help you get started with customization: -.. code-block:: python +.. jupyter-input:: class CustomExperiment(BaseExperiment): """Custom experiment class template.""" @@ -167,7 +166,7 @@ with customization: And the corresponding analysis class template: -.. code-block:: python +.. jupyter-input:: class CustomAnalysis(BaseAnalysis): """Custom analysis class template.""" @@ -188,8 +187,8 @@ And the corresponding analysis class template: Now we'll use what we've learned so far to make an entirely new experiment using the :class:`.BaseExperiment` template. -A randomized measurement experiment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Writing a new experiment +------------------------ Our goal is to write an experiment that symmetrizes the measurement readout error of an input circuit, which is especially useful in systems where readout has an unknown @@ -210,7 +209,7 @@ physical qubits to run the circuit over, and which qubits to measure over. If th doesn't specify these options, we default to the list of qubits starting with 0 with the length of the number of qubits in the circuit for both. -.. code-block:: python +.. jupyter-input:: from qiskit import QuantumCircuit from qiskit_experiments.framework import BaseExperiment @@ -257,7 +256,7 @@ Now we consider default experiment options. Because randomness is involved, it is good practice to allow the user to set a seed. We would also like the user to be able to set how many repetitions of the circuit to run: -.. code-block:: python +.. jupyter-input:: @classmethod def _default_experiment_options(cls): @@ -269,7 +268,7 @@ be able to set how many repetitions of the circuit to run: Now we write the ``circuits()`` method. We need to take the input circuit in ``self._circuit`` and add our random Paulis as well as measurement at the end. -.. code-block:: python +.. jupyter-input:: from qiskit.quantum_info import random_pauli_list from numpy.random import default_rng, Generator @@ -299,7 +298,7 @@ We use the :func:`~qiskit.quantum_info.random_pauli_list` function from the quan info module to generate random Paulis. This returns ``num_samples`` Paulis, each across ``meas_nc`` qubits. -.. code-block:: python +.. jupyter-input:: # Sample Paulis this might have duplicates, but we don't really # have any easy way of running different number of shots per circuit @@ -326,13 +325,6 @@ across ``meas_nc`` qubits. # Add final Measurement circ.measure(meas_qubits, meas_clbits) -Let's look at what the :meth:`~.qiskit.circuit.QuantumCircuit.compose` does here. - -We need to tell our analysis class how to restore the results of the original circuit. -To do so, we add metadata to each of our circuits. - -.. code-block:: python - circ.metadata = orig_metadata.copy() circ.metadata["rm_bits"] = meas_clbits circ.metadata["rm_frame"] = str(pauli) @@ -342,9 +334,10 @@ To do so, we add metadata to each of our circuits. return circuits -And the corresponding analysis class: +Metadata is added to each of our circuits to tell the analysis class how to restore +results of the original circuit. Now we write the analysis class: -.. code-block:: python +.. jupyter-input:: from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 3e13487d83..9dfef5a229 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -13,21 +13,21 @@ Qiskit itself. Qiskit Experiments releases can be installed via the Python package manager ``pip``: -.. code-block:: console +.. jupyter-input:: python -m pip install qiskit-experiments If you want to run the most up-to-date version instead (may not be stable), you can install the latest main branch: -.. code-block:: console +.. jupyter-input:: python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git If you want to develop the package, you can install Qiskit Experiments from source by cloning the repository: -.. code-block:: console +.. jupyter-input:: git clone https://github.com/Qiskit/qiskit-experiments.git python -m pip install -e qiskit-experiments @@ -48,7 +48,7 @@ Qiskit Experiments library: from qiskit_experiments.library import T1 Experiments must be run on a backend. We're going to use a simulator, -:class:`qiskit.providers.fake_provider.FakeVigo`, for +:class:`~qiskit.providers.fake_provider.FakeVigo`, for this example, but you can use any IBM backend, real or simulated, that you can access through Qiskit. @@ -82,9 +82,9 @@ estimate for the sweep range of the delays. exp = T1(physical_qubits=(0,), delays=delays) The circuits encapsulated by the experiment can be accessed using the experiment's -:meth:`.BaseExperiment.circuits` method, which returns a list of circuits that can +:meth:`~.BaseExperiment.circuits` method, which returns a list of circuits that can be run on a backend. Let's print the range of delay times we're sweeping over and -draw the first and last circuits for our T1 experiment: +draw the first and last circuits for our :math:`T_1` experiment: .. jupyter-execute:: @@ -100,8 +100,15 @@ As expected, the delay block spans the full range of time values that we specifi The ExperimentData class ======================== -After instantiating the experiment, we run the experiment by calling :meth:`~.BaseExperiment.run` with our specified backend. -This returns the :class:`.ExperimentData` class containing the results of the experiment, +After instantiating the experiment, we run the experiment by calling +:meth:`~.BaseExperiment.run` with our backend of choice. This transpiles our experiment +circuits then packages them into jobs that are run on the backend. + +.. note:: + See the how-tos for :doc:`customizing job splitting ` when + running an experiment. + +This statement returns the :class:`.ExperimentData` class containing the results of the experiment, so it's crucial that we assign the output to a data variable. We could have also provided the backend at the instantiation of the experiment, but specifying the backend at run time allows us to run the same exact experiment on different backends should we choose to do so. @@ -112,8 +119,13 @@ allows us to run the same exact experiment on different backends should we choos The :meth:`~.ExperimentData.block_for_results` method is optional and is used to block execution of subsequent code until the experiment has fully completed execution and analysis. If -``exp_data = exp.run(backend=backend)`` is executed instead, the statement will finish -running as soon as the jobs are submitted, but the analysis callback won't populate + +.. jupyter-input:: + + exp_data = exp.run(backend=backend) + +is executed instead, the statement will finish running as soon as the jobs are submitted, +but the analysis callback won't populate ``exp_data`` with results until the entire process has finished. In this case, there are two useful methods in the :class:`.ExperimentData`, :meth:`~.ExperimentData.job_status` and :meth:`~.ExperimentData.analysis_status`, that return the current status of the job @@ -163,6 +175,10 @@ and the :meth:`~.ExperimentData.metadata` property: The actual backend jobs that were executed for the experiment can be accessed with the :meth:`~.ExperimentData.jobs` method. +.. note:: + See the how-tos for :doc:`instantiating a new ExperimentData object ` + from an existing experiment that finished execution. + Setting experiment options ========================== @@ -175,7 +191,7 @@ Run options These options are passed to the experiment's ``run()`` method and then to the ``run()`` method of your specified backend. Any run option that your backend supports can be set: -.. code-block:: +.. jupyter-input:: exp.set_run_options(shots=1000, meas_level=MeasLevel.CLASSIFIED, @@ -189,7 +205,7 @@ Transpile options These options are passed to the Terra transpiler to transpile the experiment circuits before execution: -.. code-block:: +.. jupyter-input:: exp.set_transpile_options(scheduling_method='asap', optimization_level=3, @@ -217,7 +233,7 @@ These options are unique to each analysis class. Unlike the other options, analy are not directly set via the experiment object but use instead a method of the associated ``analysis``: -.. code-block:: +.. jupyter-input:: exp = rb.StandardRB(physical_qubits=(0,), lengths=list(range(1, 300, 30)), @@ -235,10 +251,10 @@ To run experiments across many qubits of the same device, we use **composite exp A composite experiment is a parent object that contains one or more child experiments, which may themselves be composite. There are two core types of composite experiments: -* **Parallel experiments** run across qubits simultaneously as set by the user. New circuits - are constructed such that parallelize circuits of the child experiments are executed - simultaneously. Therefore, the circuits in child experiments *cannot* overlap in qubits - used. The marginalization of measurement data for analysis of each child experiment is handled automatically. +* **Parallel experiments** run across qubits simultaneously as set by the user. The + circuits of child experiments are combined into new circuits that map circuit gates onto + qubits in parallel. Therefore, the circuits in child experiments *cannot* overlap in the ``physical_qubits`` + parameter. The marginalization of measurement data for analysis of each child experiment is handled automatically. * **Batch experiments** run consecutively in time. These child circuits *can* overlap in qubits used. Here's an example of measuring the :math:`T_1` of two qubits on the same device simultaneously @@ -251,15 +267,15 @@ in a parallel experiment: parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)]) parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() -Note that when options are set for a composite experiment, the child -experiments's options are also set recursively. Let's examine how the parallel +Note that when the transpile and run options are set for a composite experiment, the child +experiments's options are also set to the same options recursively. Let's examine how the parallel experiment is constructed by visualizing the circuits: .. jupyter-execute:: parallel_exp.circuits()[0].draw(output='mpl') -We see that the T1 circuits on qubits 0 and 1 have been parallelized to run simultaneously. +We see that the :math:`T_1` circuits on qubits 0 and 1 have been parallelized to run simultaneously. :class:`.ParallelExperiment` and :class:`.BatchExperiment` classes can be nested arbitrarily to make complex composite experiments. @@ -272,7 +288,8 @@ Viewing child experiment data The experiment data returned from a composite experiment contains individual analysis results for each child experiment that can be accessed -using :meth:`~.ExperimentData.child_data`. By default, the parent data object does not contain analysis results. +using :meth:`~.ExperimentData.child_data`. 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zeRT&Id#h41x*MB$x4`{NAJ3l^yt^6H<$F&3|5use_oL`mHdwvNmi^a8t|d3pe7u2E zZdT*sJKA|5DEfOs%FzSxa*su2z&9rSh9K}{qlpR8?G`})QXu_>NBR4q?fv=QZ0xP5 zy(sv9GzzvoBF47Y4XU5*+k3G$341GHFADaeU@r>xqF^ry z_M%`f3ihI4FADaeU@r>xqF^ry_M%`f3ihI4FADae;NL`n6z1c3bxPy*)s6 l4^`en)c=2o0)hSejDx+)AD=2p+c`G5Eq6yY>xRMe{{!o}lDPl? diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 53d30950eb..117d8d5047 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -5,6 +5,8 @@ Tutorials These tutorials assume some familiarity with Qiskit, but no knowledge of Qiskit Experiments. They're suitable for beginners who want to get started with the package. +.. _basics: + The Basics ========== diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst index d5ec8048f4..0eee1d41bd 100644 --- a/docs/tutorials/intro.rst +++ b/docs/tutorials/intro.rst @@ -27,6 +27,8 @@ What Qiskit Experiments can do * Visualize data with support for custom drawing backends * Save and retrieve timestamped calibration parameters for physical backends +.. _primer: + A quick primer ============== diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index 98b5c2b024..d8fd0241cd 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -2,7 +2,7 @@ Visualization: Creating figures =============================== The Visualization module provides plotting functionality for creating figures from experiment and analysis results. -This includes `plotter` and `drawer` classes to plot data in :py:class:`CurveAnalysis` and its subclasses. +This includes ``plotter`` and ``drawer`` classes to plot data in :class:`.CurveAnalysis` and its subclasses. Plotters define the kind of figures to plot, while drawers are backends that enable them to be visualized. How much you will interact directly with the visualization module depends on your use case: @@ -10,28 +10,30 @@ How much you will interact directly with the visualization module depends on you - **Running library experiments as-is:** You won't need to interact with the visualization module. - **Running library experiments with custom styling for figures**: You will be setting figure options through the plotter. - **Making plots using a plotting library other than Matplotlib**: You will need to define a custom drawer class. -- **Writing your own analysis class**: You don't need to interact with the visualization module. - if you want to use the the default plotter and drawer settings, or you can customize them +- **Writing your own analysis class**: If you want to use the the default plotter and drawer settings, + you don't need to interact with the visualization module. Optionally, you can customize your plotter and drawer. -Plotters inherit from :class:`BasePlotter` and define a type of figure that may be generated from -experiment or analysis data. For example, the results from :class:`CurveAnalysis` --- or any other +Plotters inherit from :class:`.BasePlotter` and define a type of figure that may be generated from +experiment or analysis data. For example, the results from :class:`.CurveAnalysis` --- or any other experiment where results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted -using the :class:`CurvePlotter` class, which plots X-Y-like values. +using the :class:`.CurvePlotter` class, which plots X-Y-like values. These plotter classes act as a bridge (from the common bridge pattern in software development) between analysis classes (or even users) and plotting backends such as Matplotlib. Drawers are the backends, with -a common interface defined in :class:`BaseDrawer`. Though Matplotlib is the only officially supported -plotting backend in Qiskit Experiments through :class:`MplDrawer`, custom drawers can be +a common interface defined in :class:`.BaseDrawer`. Though Matplotlib is the only officially supported +plotting backend in Qiskit Experiments through :class:`.MplDrawer`, custom drawers can be implemented by users to use alternative backends. As long as the backend is a subclass of -:class:`BaseDrawer`, and implements all the necessary functionality, all plotters should be able to +:class:`.BaseDrawer`, and implements all the necessary functionality, all plotters should be able to generate figures with the alternative backend. + + Generating and customizing a figure using a plotter --------------------------------------------------- -First, we display the default figure from a :class:`Rabi` experiment as a starting point: +First, we display the default figure from a :class:`.Rabi` experiment as a starting point: .. jupyter-execute:: @@ -184,20 +186,20 @@ Customizing plotting in your experiment Plotters are easily integrated into custom analysis classes. To add a plotter instance to such a class, we define a new ``plotter`` property, pass it relevant data in the analysis class's ``_run_analysis`` method, and return the generated figure alongside our -analysis results. We use the ``IQPlotter`` class to illustrate how this is done for an +analysis results. We use the :class:`.IQPlotter` class to illustrate how this is done for an arbitrary analysis class. -To ensure that we have an interface simlar to existing analysis classes, we make our plotter +To ensure that we have an interface similar to existing analysis classes, we make our plotter accessible as an ``analysis.plotter`` property and analysis.options.plotter option. The code below accomplishes this for our example ``MyIQAnalysis`` analysis class. We -set the drawer to MplDrawer to use Matplotlib by default. The plotter property of our +set the drawer to :class:`.MplDrawer` to use :mod:`matplotlib` by default. The plotter property of our analysis class makes it easier to access the plotter instance; i.e., using ``self.plotter`` and ``analysis.plotter``. We set default options and figure options in ``_default_options``, but you can still override them as we did above. The ``MyIQAnalysis`` class accepts single-shot level 1 IQ data, which consists of an in-phase and quadrature measurement for each shot and circuit. ``_run_analysis`` is -passed an ``ExperimentData`` instance which contains IQ data as a list of dictionaries +passed an :class:`.ExperimentData` instance which contains IQ data as a list of dictionaries (one per circuit) where their "memory" entries are lists of IQ values (one per shot). Each dictionary has a "metadata" entry, with the name of a prepared state: "0", "1", or "2". These are our series names. @@ -206,7 +208,7 @@ Our goal is to create a figure that displays the single-shot IQ values of each prepared-state (one per circuit). We process the "memory" data passed to the analysis class and set the points and centroid series data in the plotter. This is accomplished in the code below, where we also train a discriminator -to label the IQ points as one of the three prepared states. IQPlotter supports +to label the IQ points as one of the three prepared states. :class:`.IQPlotter` supports plotting a discriminator as optional supplementary data, which will show predicted series over the axis area. @@ -287,11 +289,11 @@ You can create a custom figure plotter by subclassing :class:`.BasePlotter` and :meth:`~.BasePlotter.expected_supplementary_data_keys`, and :meth:`~.BasePlotter._plot_figure`. -The first two methods allow you to define a list of supported data-keys, +The first two methods allow you to define a list of supported data-keys as strings, which identify the different data to plot. The third method, :meth:`~.BasePlotter._plot_figure`, must contain your code to generate a figure by calling methods on the plotter's drawer instance (self.drawer). When ``plotter.figure()`` is called -by an analysis class, the plotter calls _plot_figure and then returns your figure +by an analysis class, the plotter calls ``_plot_figure()`` and then returns your figure object which is added to the experiment data instance. It is also good practice to set default values for figure options, such as axis labels. You can do this by overriding the :meth:`~.BasePlotter._default_figure_options` method in your plotter subclass. diff --git a/qiskit_experiments/__init__.py b/qiskit_experiments/__init__.py index a64b1ed70b..d0a56c54d1 100644 --- a/qiskit_experiments/__init__.py +++ b/qiskit_experiments/__init__.py @@ -48,9 +48,6 @@ - Utility functions for curve fitting and analysis. * - :mod:`~qiskit_experiments.calibration_management` - Classes for managing calibration experiment result data. - * - :mod:`~qiskit_experiments.database_service` - - Classes for saving and retrieving experiment and analysis results - from a database. * - :mod:`~qiskit_experiments.visualization` - Classes for creating figures from experiment results. diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 4f9ae34fb1..9ba3f37f39 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -71,6 +71,10 @@ ~characterization.FineFrequency ~characterization.ReadoutAngle ~characterization.ResonatorSpectroscopy + ~characterization.RoughDrag + ~characterization.FineDrag + ~characterization.FineXDrag + ~characterization.FineSXDrag ~characterization.MultiStateDiscrimination .. _characterization two qubits: @@ -88,23 +92,6 @@ ~characterization.EchoedCrossResonanceHamiltonian ~characterization.ZZRamsey -.. _characterization pulse gates: - -Characterization Experiments: Pulse Gates -========================================= - -Experiments for characterization of pulse gates. -Some experiments also have a calibration experiment version. - -.. autosummary:: - :toctree: ../stubs/ - :template: autosummary/experiment.rst - - ~characterization.RoughDrag - ~characterization.FineDrag - ~characterization.FineXDrag - ~characterization.FineSXDrag - .. _characterization-mitigation: Characterization Experiments: Mitigation From 24baa82b75efaaf87284ca0711fbe35c7a0c1af8 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 28 Feb 2023 00:22:46 -0500 Subject: [PATCH 31/64] changed jupyter-sphinx import and addressed comments --- docs/_ext/custom_styles/formatter.py | 4 +- docs/apidocs/index.rst | 12 + docs/guides/restless_measurements.rst | 2 +- docs/howtos/index.rst | 2 + docs/tutorials/custom_experiment.rst | 496 ++++++++---------- docs/tutorials/getting_started.rst | 16 +- docs/tutorials/index.rst | 3 +- .../calibration/rough_amplitude_cal.py | 4 +- .../library/characterization/t1.py | 8 +- requirements-dev.txt | 1 + 10 files changed, 263 insertions(+), 285 deletions(-) diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index 59d7e8e6ba..111a75e80f 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -117,7 +117,7 @@ def format_experiment_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Experiment Options", "", - "These options can be set by :py:meth:`set_experiment_options` method.", + "These options can be set by the :py:meth:`set_experiment_options` method.", "", ] format_lines.extend(lines) @@ -131,7 +131,7 @@ def format_analysis_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Analysis Options", "", - "These options can be set by :py:meth:`analysis.set_options` method.", + "These options can be set by the :py:meth:`analysis.set_options` method.", "", ] format_lines.extend(lines) diff --git a/docs/apidocs/index.rst b/docs/apidocs/index.rst index fcb4b1ef00..ea9b00c5be 100644 --- a/docs/apidocs/index.rst +++ b/docs/apidocs/index.rst @@ -4,6 +4,18 @@ API Reference ============= +.. warning:: + + This package is still under active development and it is very likely + that there will be breaking API changes in future releases. + If you encounter any bugs, please open an issue on + `Github `_. + +The API documentation is organized into two sections below. The package modules include the framework, the +experiment library, experiment modules, and test utilities. Experiment modules are +the main categories of the experiment library itself, such as qubit characterization +and experimental suites like tomography. + Package Modules =============== diff --git a/docs/guides/restless_measurements.rst b/docs/guides/restless_measurements.rst index 77cec89412..e4e1a04316 100644 --- a/docs/guides/restless_measurements.rst +++ b/docs/guides/restless_measurements.rst @@ -90,7 +90,7 @@ on non-IBM Quantum providers if the required options are not supported. After calling :meth:`~.RestlessMixin.enable_restless` the experiment is ready to be run in a restless mode. With a hardware backend this would be done by calling the ``run`` method -.. code:: python +.. jupyter-input:: drag_data_restless = cal_drag.run() diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index cb506f87a9..dae6225d43 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -14,6 +14,8 @@ with :ref:`the basics ` of the package before using these recipes. * | +| +| .. Hiding - Indices and tables diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 6658b0c1a1..3dd4b4294e 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -1,4 +1,4 @@ -Writing a custom experiment +Writing your own experiment =========================== Qiskit Experiments is designed to be easily customizable. If you would like to @@ -53,87 +53,23 @@ To create an analysis subclass, one only needs to implement the abstract :meth:`.BaseAnalysis._run_analysis` method. This method takes an :class:`.ExperimentData` container and kwarg analysis options. If any kwargs are used, the :meth:`.BaseAnalysis._default_options` method should be -overriden to define default values for these options. +overriden to define default values for these options. You can also write a custom +analysis class for an existing experiment class and then run ``exp.analysis = NewAnalysis()`` +after instantiating the experiment object ``exp`` to override its default analysis class. The :meth:`.BaseAnalysis._run_analysis` method should return a pair -``(results, figures)``, where ``results`` is a list of -:class:`.AnalysisResultData` and ``figures`` is a list of -:class:`matplotlib.figure.Figure`. +:code:`(results, figures)`, where ``results`` is a list of +:class:`.AnalysisResultData` objects and ``figures`` is a list of +:class:`matplotlib.figure.Figure` objects. The :mod:`~qiskit_experiments.data_processing` module contains classes for building data processor workflows to help with advanced analysis of experiment data. -Subclassing an Existing Experiment ----------------------------------- +If you want to customize the figures of the experiment, consult the +:doc:`visualization module ` tutorial. -Let's walk through the process of subclassing an existing experiment in the Qiskit -Experiments library. -The FineAmplitude Experiment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The :class:`.FineAmplitude` experiment optimizes gate amplitude by repeating the -gate pulse N times, hence amplifying the under- or over-rotations. -This experiment can be performed for a variety of rotations, and subclasses are -provided for the :math:`\pi` and :math:`\frac{\pi}{2}` rotations as -:class:`.FineXAmplitude` and :class:`.FineSXAmplitude` respectively. These provided subclasses -focus on the 0 <-> 1 transition, but this experiment can also be performed -for higher order transitions. - -Our objective is to create a new class, ``HigherOrderFineXAmplitude``, which calibrates -schedules on the 1 <-> 2 transition for the :math:`\pi` rotation. To do this, we need to -excite the qubit to the :math:`|1\rangle` state and then execute the Usually we would need -to override the :meth:`~.BaseExperiment.circuits` method, but :class:`.FineAmplitude` -generates the preparation circuit generation in a separate method, -:meth:`~.FineAmplitude._pre_circuit`, which is combined with the main circuit in -:meth:`~.BaseExperiment.circuits`. Therefore, we only need to - -.. jupyter-input:: - - class HigherOrderFineXAmplitude(FineXAmplitude): - def _pre_circuit(self) -> QuantumCircuit: - """Return a preparation circuit. - - This method can be overridden by subclasses e.g. to calibrate schedules on - transitions other than the 0 <-> 1 transition. - """ - circuit = QuantumCircuit(1) - - circuit.x(0) - - if self.experiment_options.add_sx: - circuit.sx(0) - - if self.experiment_options.sx_schedule is not None: - sx_schedule = self.experiment_options.sx_schedule - circuit.add_calibration("sx", (self.physical_qubits[0],), sx_schedule, params=[]) - circuit.barrier() - - return circuit - -In this subclass we have overridden the ``_pre_circuit`` method in order to calibrate -on higher energy transitions by using an initial X gate to populate the first excited state. - -Using the Subclass ------------------- - -Now, we can use our new subclass as we would the original parent class. -Shown below are results from following the :ref:`fine-amplitude-cal` tutorial -for detecting an over-rotated pulse using our new -``HigherOrderFineXAmplitude`` class in place of the original -:class:`.FineXAmplitude` class. -You can try this for yourself and verify that your results are similar. - -.. code-block:: - - DbAnalysisResultV1 - - name: d_theta - - value: -0.020710672666906425 ± 0.0012903658449026907 - - χ²: 0.7819653845899581 - - quality: good - - device_components: ['Q0'] - - verified: False Custom experiment template -------------------------- @@ -187,112 +123,132 @@ And the corresponding analysis class template: Now we'll use what we've learned so far to make an entirely new experiment using the :class:`.BaseExperiment` template. -Writing a new experiment ------------------------- +Example custom experiment: randomized measurement +------------------------------------------------- -Our goal is to write an experiment that symmetrizes the measurement readout error -of an input circuit, which is especially useful in systems where readout has an unknown -and potentially large bias. To do so, our experiment should create a list of copies of an input circuit -and randomly sample an :math:`N`-qubit Pauli to apply to each one before -a final :math:`N`-qubit :math:`Z`-basis measurement to randomize the expected -ideal output bitstring in the measurement. +Symmetrizing the measurement readout error of a circuit is especially useful in systems +where readout has an unknown and potentially large bias. We can create an experiment +using the Qiskit Experiments framework to take a circuit as an input and symmetrize +its readout. -The analysis uses the applied Pauli frame of a randomized -measurement experiment to de-randomize the measured counts -and combine across samples to return a single counts dictionary for -the original circuit. This has the effect of Pauli-twirling and symmetrizing the +To do so, our experiment should create a list of copies of the input circuit +and randomly sample an :math:`N`-qubit Pauli to apply to each one, then add +a final :math:`N`-qubit :math:`Z`-basis measurement to randomize the expected +ideal output bitstring in the measurement. The analysis uses the applied Pauli frame of +a randomized measurement experiment to de-randomize the measured counts. The results +are then combined across samples to return a single counts dictionary for +the original circuit. This has the effect of Pauli twirling and symmetrizing the measurement readout error. -To start, we must write our own ``__init__()`` method to take as input the circuit that +To start, we write our own ``__init__()`` method to take as input the circuit that we want to twirl on. We also want to give the user the option to specify which physical qubits to run the circuit over, and which qubits to measure over. If the user -doesn't specify these options, we default to the list of qubits starting with 0 with +doesn't specify these options, we default both to the list of qubits starting with 0 with the length of the number of qubits in the circuit for both. .. jupyter-input:: - from qiskit import QuantumCircuit - from qiskit_experiments.framework import BaseExperiment - - class RandomizedMeasurement(BaseExperiment): - """Randomized measurement experiment. - """ - - def __init__( - self, - circuit, - measured_qubits=None, - physical_qubits=None, - backend=None, - **experiment_options - ): - """Basic randomized Z-basis measurement experiment via a Pauli frame transformation - - Note this will just append a new set of measurements at the end of a circuit. - A more advanced version of this experiment would be use a transpiler pass to - replace all existing measurements in a circuit with randomized measurements. - """ - if physical_qubits is None: - physical_qubits = tuple(range(circuit.num_qubits)) - if measured_qubits is None: - measured_qubits = tuple(range(circuit.num_qubits)) - - # Initialize BaseExperiment - analysis = RandomizedMeasurementAnalysis() - super().__init__(physical_qubits, analysis=analysis, backend=backend) - - # Add experiment properties - self._circuit = circuit - self._measured_qubits = measured_qubits - - # Set any init optinos - self.set_experiment_options(**experiment_options) + from numpy.random import default_rng, Generator + from qiskit import QuantumCircuit + from qiskit.quantum_info import random_pauli_list + from qiskit_experiments.framework import BaseExperiment + + class RandomizedMeasurement(BaseExperiment): + """Randomized measurement experiment.""" + def __init__( + self, + circuit, + measured_qubits=None, + physical_qubits=None, + backend=None, + **experiment_options + ): + """Basic randomized Z-basis measurement experiment via a Pauli frame transformation + + Note this will just append a new set of measurements at the end of a circuit. + A more advanced version of this experiment would be to use a transpiler pass to + replace all existing measurements in a circuit with randomized measurements. + """ + if physical_qubits is None: + physical_qubits = tuple(range(circuit.num_qubits)) + if measured_qubits is None: + measured_qubits = tuple(range(circuit.num_qubits)) + + # Initialize BaseExperiment + analysis = RandomizedMeasurementAnalysis() + super().__init__(physical_qubits, analysis=analysis, backend=backend) + + # Add experiment properties + self._circuit = circuit + self._measured_qubits = measured_qubits + + # Set any init optinos + self.set_experiment_options(**experiment_options) Notice that when we called ``super().__init__``, we provided the list of physical qubits, the name of our analysis class, and the backend, which is optionally specified by the user at this stage. + Now we consider default experiment options. Because randomness is involved, it is good practice to allow the user to set a seed. We would also like the user to be able to set how many repetitions of the circuit to run: .. jupyter-input:: - @classmethod - def _default_experiment_options(cls): - options = super()._default_experiment_options() - options.num_samples = "default" - options.seed = None - return options + ... + + @classmethod + def _default_experiment_options(cls): + options = super()._default_experiment_options() + options.num_samples = None + options.seed = None + return options + Now we write the ``circuits()`` method. We need to take the input circuit in ``self._circuit`` -and add our random Paulis as well as measurement at the end. +and add our random Paulis as well as measurement at the end. We use the built-in property +:attr:`~.BaseExperiment.num_qubits` of :class:`~.BaseExperiment` to get the number of qubits +in the experiment. We keep track of the list of qubits and classical +Note that the circuits themselves are always built on qubits `0` to `length of the circuit - 1`, +and not the actual physical qubit indices given in ``physical_qubits``. During experiment +execution, a mapping is performed to place these circuits on the physical layout. .. jupyter-input:: - from qiskit.quantum_info import random_pauli_list - from numpy.random import default_rng, Generator + ... - def circuits(self): - # Number of classical bits for original circuit and added measurements - circ_nc = self._circuit.num_clbits - meas_nc = len(self._measured_qubits) - circ_qubits = list(range(self.num_qubits)) - circ_clbits = list(range(circ_nc)) - meas_qubits = self._measured_qubits - meas_clbits = list(range(circ_nc, circ_nc + meas_nc)) - # Get number of samples from options - num_samples = self.experiment_options.num_samples - if num_samples == "default": - num_samples = 2 ** self.num_qubits - - # Get rng seed - seed = self.experiment_options.seed - if isinstance(seed, Generator): - rng = seed - else: - rng = default_rng(seed) + def circuits(self): + # Number of classical bits of the original circuit + circ_nc = self._circuit.num_clbits + + # Number of added measurements + meas_nc = len(self._measured_qubits) + + # Classical bits of the circuit + circ_clbits = list(range(circ_nc)) + + # Classical bits of the added measurements + meas_clbits = list(range(circ_nc, circ_nc + meas_nc)) + + # Qubits of the circuit + circ_qubits = list(range(self.num_qubits)) + + # Qubits of the added measurements + meas_qubits = self._measured_qubits + + # Get number of samples from options + num_samples = self.experiment_options.num_samples + if num_samples is None: + num_samples = 2 ** self.num_qubits + + # Get rng seed + seed = self.experiment_options.seed + if isinstance(seed, Generator): + rng = seed + else: + rng = default_rng(seed) We use the :func:`~qiskit.quantum_info.random_pauli_list` function from the quantum info module to generate random Paulis. This returns ``num_samples`` Paulis, each @@ -300,11 +256,23 @@ across ``meas_nc`` qubits. .. jupyter-input:: - # Sample Paulis this might have duplicates, but we don't really - # have any easy way of running different number of shots per circuit - # so we just run repeat circuits multiple times + ... + paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) +Now we construct the circuits by composing the original circuit with a Pauli frame then +adding a measurement at the end only to the measurement qubits. Metadata containing +the classical measurement register and the applied Pauli, and the +each of the circuits to tell the analysis class how to restore the original results. +To make restoration easier, we store Paulis in the +:class:`x symplectic form ` in ``metadata["rm_frame"]`` +so we know whether to apply a bit flip to each bit of the result +(the phase is not important for our purposes). + +.. jupyter-input:: + + ... + # Construct circuits circuits = [] orig_metadata = self._circuit.metadata or {} @@ -322,75 +290,82 @@ across ``meas_nc`` qubits. # Add Pauli frame circ.compose(pauli, meas_qubits, inplace=True) - # Add final Measurement + # Add final measurement circ.measure(meas_qubits, meas_clbits) circ.metadata = orig_metadata.copy() circ.metadata["rm_bits"] = meas_clbits circ.metadata["rm_frame"] = str(pauli) circ.metadata["rm_sig"] = pauli.x.astype(int).tolist() - circuits.append(circ) - return circuits -Metadata is added to each of our circuits to tell the analysis class how to restore -results of the original circuit. Now we write the analysis class: +Now we write the analysis class, overriding ``_run_analysis`` as described above. We +loop over each circuit to process the output bitstring. Since we're using default level +2 data, we access it with the ``counts`` key. We use the circuit metadata to calculate the bitwise XOR mask from the Pauli +signature to restore the output to what it should be without the random Pauli frame +at the end. We make a new :class:`.AnalysisResultData` object since we're rewriting the +counts from the original experiment. .. jupyter-input:: - from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData + from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData + + class RandomizedMeasurementAnalysis(BaseAnalysis): + """Analysis for randomized measurement experiment.""" + + # Helper dict to swap a clbit value + _swap_bit = {"0": "1", "1": "0"} + + def _run_analysis(self, experiment_data): + + combined_counts = {} + for datum in experiment_data.data(): + # Get counts + counts = datum["counts"] + num_bits = len(next(iter(counts))) + + # Get metadata + metadata = datum["metadata"] + clbits = metadata["rm_bits"] + sig = metadata["rm_sig"] + + # Construct full signature + full_sig = num_bits * [0] + for bit, val in zip(clbits, sig): + full_sig[bit] = val + + # Combine dicts + for key, val in counts.items(): + bitstring = self._swap_bitstring(key, full_sig) + if bitstring in combined_counts: + combined_counts[bitstring] += val + else: + combined_counts[bitstring] = val + + result = AnalysisResultData("counts", combined_counts) + return [result], [] + +This is the helper function we're using to apply the XOR mask and flip the bitstring +output if the Pauli corresponding to that bit has a nonzero signature. - class RandomizedMeasurementAnalysis(BaseAnalysis): - """Analysis for randomized measurement experiment.""" +.. jupyter-input:: - # Helper dict to swap a clbit value - _swap_bit = {"0": "1", "1": "0"} + ... - def _run_analysis(self, experiment_data): - - combined_counts = {} - for datum in experiment_data.data(): - # Get counts - counts = datum["counts"] - num_bits = len(next(iter(counts))) - - # Get metadata - metadata = datum["metadata"] - clbits = metadata["rm_bits"] - sig = metadata["rm_sig"] - - # Construct full signature - full_sig = num_bits * [0] - for bit, val in zip(clbits, sig): - full_sig[bit] = val - - # Combine dicts - for key, val in counts.items(): - bitstring = self._swap_bitstring(key, full_sig) - if bitstring in combined_counts: - combined_counts[bitstring] += val - else: - combined_counts[bitstring] = val - - - result = AnalysisResultData("counts", combined_counts) - return [result], [] - - @classmethod - def _swap_bitstring(cls, bitstring, sig): - """Swap a bitstring based signature to flip bits at.""" - # This is very inefficient but demonstrates the basic idea - # Really should do with bitwise operations of integer counts rep - return "".join(reversed( - [cls._swap_bit[b] if sig[- 1 - i] else b for i, b in enumerate(bitstring)] - )) + @classmethod + def _swap_bitstring(cls, bitstring, sig): + """Swap a bitstring based signature to flip bits at.""" + # This is very inefficient but demonstrates the basic idea + return "".join(reversed( + [cls._swap_bit[b] if sig[- 1 - i] else b for i, b in enumerate(bitstring)] + )) .. jupyter-execute:: :hide-code: :hide-output: - # this is the actual code that defines the experiment so the code below can work + # this is the actual code that defines the experiment so the experiment execution code below can work from numpy.random import default_rng, Generator from qiskit import QuantumCircuit @@ -398,9 +373,6 @@ results of the original circuit. Now we write the analysis class: from qiskit.quantum_info import random_pauli_list class RandomizedMeasurement(BaseExperiment): - """Randomized measurement experiment. - """ - def __init__( self, circuit, @@ -409,37 +381,28 @@ results of the original circuit. Now we write the analysis class: backend=None, **experiment_options ): - """Basic randomize Z-basis measurement via a Pauli frame transformation - Note this will just append a new set of measurment at the end of a circuit. - A more advanced version of this experiment would be use a transpiler pass to - replace all exisiting measurements in a circuit with randomized measurements. - """ if physical_qubits is None: physical_qubits = tuple(range(circuit.num_qubits)) if measured_qubits is None: measured_qubits = tuple(range(circuit.num_qubits)) - # Initialize BaseExperiment analysis = RandomizedMeasurementAnalysis() super().__init__(physical_qubits, analysis=analysis, backend=backend) - # Add experiment properties self._circuit = circuit self._measured_qubits = measured_qubits - # Set any init optinos self.set_experiment_options(**experiment_options) @classmethod def _default_experiment_options(cls): options = super()._default_experiment_options() - options.num_samples = "default" + options.num_samples = None options.seed = None return options def circuits(self): - # Number of classical bits for original circuit and added measurements circ_nc = self._circuit.num_clbits meas_nc = len(self._measured_qubits) circ_qubits = list(range(self.num_qubits)) @@ -447,24 +410,18 @@ results of the original circuit. Now we write the analysis class: meas_qubits = self._measured_qubits meas_clbits = list(range(circ_nc, circ_nc + meas_nc)) - # Get number of samples from options num_samples = self.experiment_options.num_samples - if num_samples == "default": + if num_samples is None: num_samples = 2 ** self.num_qubits - # Get rng seed seed = self.experiment_options.seed if isinstance(seed, Generator): rng = seed else: rng = default_rng(seed) - # Sample Paulis this might have duplicates, but we don't really - # have any easy way of running different number of shots per circuit - # so we just run repeat circuits multiple times paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) - # Construct circuits circuits = [] orig_metadata = self._circuit.metadata or {} for pauli in paulis: @@ -473,30 +430,19 @@ results of the original circuit. Now we write the analysis class: self.num_qubits, circ_nc + meas_nc, name=name ) - # Append original circuit circ.compose( self._circuit, circ_qubits, circ_clbits, inplace=True ) - - # Add Pauli frame circ.compose(pauli, meas_qubits, inplace=True) - - # Add final Measurement circ.measure(meas_qubits, meas_clbits) - - # Add metadata circ.metadata = orig_metadata.copy() circ.metadata["rm_bits"] = meas_clbits - circ.metadata["rm_frame"] = str(pauli) circ.metadata["rm_sig"] = pauli.x.astype(int).tolist() circuits.append(circ) return circuits - - - from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData class RandomizedMeasurementAnalysis(BaseAnalysis): @@ -509,21 +455,14 @@ results of the original circuit. Now we write the analysis class: combined_counts = {} for datum in experiment_data.data(): - # Get counts counts = datum["counts"] num_bits = len(next(iter(counts))) - - # Get metadata metadata = datum["metadata"] clbits = metadata["rm_bits"] sig = metadata["rm_sig"] - - # Construct full signature full_sig = num_bits * [0] for bit, val in zip(clbits, sig): full_sig[bit] = val - - # Combine dicts for key, val in counts.items(): bitstring = self._swap_bitstring(key, full_sig) if bitstring in combined_counts: @@ -560,49 +499,64 @@ To test our code, we first simulate a noisy backend with asymmetric readout erro noise_model.add_all_qubit_readout_error([[1 - p1g0, p1g0], [p0g1, 1 - p0g1]]) noise_backend = AerSimulator(noise_model=noise_model) -We now run the experiment with a GHZ circuit on an ideal backend: +Let's use a GHZ circuit as the input. .. jupyter-execute:: - # GHZ Circuit - nq = 4 - qc = QuantumCircuit(nq) - qc.h(0) - for i in range(1, nq): - qc.cx(i-1, i) + # GHZ Circuit + nq = 4 + qc = QuantumCircuit(nq) + qc.h(0) + for i in range(1, nq): + qc.cx(i-1, i) + + qc.draw("mpl") - # Experiment parameters - total_shots = 100000 - num_samples = 50 - shots = total_shots // num_samples +Check that the experiment is appending a random Pauli and measurements as expected: + +.. jupyter-execute:: + + # Experiment parameters + total_shots = 100000 + num_samples = 50 + shots = total_shots // num_samples + + # Run ideal randomized meas experiment + exp = RandomizedMeasurement(qc, num_samples=num_samples) + exp.circuits()[0].draw("mpl") + +We now run the experiment with a GHZ circuit on an ideal backend: + +.. jupyter-execute:: - # Run ideal randomized meas experiment - exp = RandomizedMeasurement(qc, num_samples=num_samples) - expdata_ideal = exp.run(AerSimulator(), shots=shots) - counts_ideal = expdata_ideal.analysis_results("counts").value - print(counts_ideal) + expdata_ideal = exp.run(AerSimulator(), shots=shots) + counts_ideal = expdata_ideal.analysis_results("counts").value + print(counts_ideal) -Now we repeat the experiment on the backend with readout error: +Repeat the experiment on the backend with readout error and compare with results +from running GHZ circuit itself: .. jupyter-execute:: - # Run noisy randomized meas experiment with readout error - expdata_noise = exp.run(noise_backend, shots=shots) - counts_noise = expdata_noise.analysis_results("counts").value + # Run noisy randomized meas experiment with readout error + expdata_noise = exp.run(noise_backend, shots=shots) + counts_noise = expdata_noise.analysis_results("counts").value - # Run noisy direct simulation of original circuit without randomization - meas_circ = qc.copy() - meas_circ.measure_all() - result = noise_backend.run(meas_circ, shots=total_shots).result() - counts_direct = result.get_counts(0) + # Run noisy simulation of the original circuit without randomization + meas_circ = qc.copy() + meas_circ.measure_all() + result = noise_backend.run(meas_circ, shots=total_shots).result() + counts_direct = result.get_counts(0) - from qiskit.visualization import plot_histogram + from qiskit.visualization import plot_histogram - # Plot counts, ideally randomized one should be more symmetric in noise - # than direct one with asymmetric readout error - plot_histogram([counts_ideal, counts_direct, counts_noise], + # Plot counts, ideally randomized one should be more symmetric in noise + # than direct one with asymmetric readout error + plot_histogram([counts_ideal, counts_direct, counts_noise], legend=["Ideal", "Asymmetric meas error (Direct)", "Asymmetric meas error (Randomized)"]) -We see that the direct asymmetric measurement is symmetrized by the application of randomized measurement. \ No newline at end of file +The asymmetric measurement of the original circuit on this backend (Direct on the plot legend) +has been successfully symmetrized by the application of randomized measurement +(Randomized on the plot legend). diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 9dfef5a229..e590a0e45f 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -164,12 +164,22 @@ The nominal value and standard deviation of each value can be accessed as follow For further documentation on how to work with UFloats, consult the ``uncertainties`` :external+uncertainties:doc:`user_guide`. -The raw data and metadata can be accessed with the :meth:`~.ExperimentData.data` method -and the :meth:`~.ExperimentData.metadata` property: +Raw circuit output data and its associated metadata can be accessed with the +:meth:`~.ExperimentData.data` property. Data is indexed by the circuit +it corresponds to. Depending on the measurement level set in the experiment, the raw data +will either be in the key ``counts`` (level 2) or ``memory`` (level 1 IQ data). Circuit metadata +contains information set by the experiment on a circuit-by-circuit basis; ``xval`` +is used by the analysis to extract the x value for each circuit when fitting the data. + +.. jupyter-execute:: + + print(exp_data.data(0)) + +Experiments also have global associated metadata accessed by the +:meth:`~.ExperimentData.metadata` property. .. jupyter-execute:: - print(exp_data.data(0)['counts']) print(exp_data.metadata) The actual backend jobs that were executed for the experiment can be accessed with the diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 117d8d5047..dc111a3a4a 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -2,7 +2,8 @@ Tutorials ######### -These tutorials assume some familiarity with Qiskit, but no knowledge of Qiskit Experiments. +These tutorials assume some familiarity with Qiskit (on the level of the +:docs:`introductory tutorials `) but no knowledge of Qiskit Experiments. They're suitable for beginners who want to get started with the package. .. _basics: diff --git a/qiskit_experiments/library/calibration/rough_amplitude_cal.py b/qiskit_experiments/library/calibration/rough_amplitude_cal.py index 66355c31e1..518b335d8d 100644 --- a/qiskit_experiments/library/calibration/rough_amplitude_cal.py +++ b/qiskit_experiments/library/calibration/rough_amplitude_cal.py @@ -197,7 +197,7 @@ class RoughXSXAmplitudeCal(RoughAmplitudeCal): """A rough amplitude calibration of x and sx gates. # section: see_also - :class:`.Rabi` + :class:`.Rabi`, :class:`.RoughAmplitudeCal` """ @qubit_deprecate() @@ -231,7 +231,7 @@ class EFRoughXSXAmplitudeCal(RoughAmplitudeCal): """A rough amplitude calibration of x and sx gates on the 1<->2 transition. # section: see_also - :class:`.Rabi` + :class:`.Rabi`, :class:`.RoughAmplitudeCal` """ __outcome__ = "rabi_rate_12" diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index 77cfea690d..da47f38be0 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -32,13 +32,11 @@ class T1(BaseExperiment): Each experiment consists of the following steps: - 1. Circuits generation: the circuits set the qubit in the excited state, + #. Circuits generation: the circuits set the qubit in the excited state, wait different time intervals, then measure the qubit. - - 2. Backend execution: actually running the circuits on the device + #. Backend execution: actually running the circuits on the device (or simulator). - - 3. Analysis of results: deduction of T\ :sub:`1`\ , based on the outcomes, + #. Analysis of results: deduction of T\ :sub:`1`\ , based on the outcomes, by fitting to an exponential curve. # section: analysis_ref diff --git a/requirements-dev.txt b/requirements-dev.txt index 6927da391c..7dcc5b06ef 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -4,6 +4,7 @@ astroid==2.5 pylint==2.7.1 jinja2==3.0.3 sphinx==5.2.3 +jupyter-sphinx>=0.4.0 qiskit-sphinx-theme>=1.10.2 sphinx-autodoc-typehints<=1.20.2 sphinx-design From c03d088b7fc38218050eb23a85e8843e61c04ecd Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 28 Feb 2023 00:34:11 -0500 Subject: [PATCH 32/64] fixed link --- docs/tutorials/custom_experiment.rst | 13 +++++-------- docs/tutorials/index.rst | 2 +- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 3dd4b4294e..40ec08c4bb 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -249,17 +249,14 @@ execution, a mapping is performed to place these circuits on the physical layout rng = seed else: rng = default_rng(seed) + + paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) -We use the :func:`~qiskit.quantum_info.random_pauli_list` function from the quantum -info module to generate random Paulis. This returns ``num_samples`` Paulis, each +In the last line of the above code block, we used the +:func:`~qiskit.quantum_info.random_pauli_list` function from the :mod:`qiskit.quantum_info` +module to generate random Paulis. This returns ``num_samples`` Paulis, each across ``meas_nc`` qubits. -.. jupyter-input:: - - ... - - paulis = random_pauli_list(meas_nc, size=num_samples, phase=False, seed=rng) - Now we construct the circuits by composing the original circuit with a Pauli frame then adding a measurement at the end only to the measurement qubits. Metadata containing the classical measurement register and the applied Pauli, and the diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index dc111a3a4a..421da6592d 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -3,7 +3,7 @@ Tutorials ######### These tutorials assume some familiarity with Qiskit (on the level of the -:docs:`introductory tutorials `) but no knowledge of Qiskit Experiments. +:external+qiskit:doc:`introductory tutorials `) but no knowledge of Qiskit Experiments. They're suitable for beginners who want to get started with the package. .. _basics: From 17ca5a59658f77cd1059048e7ae63aef3f4c8d48 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 28 Feb 2023 19:27:12 -0500 Subject: [PATCH 33/64] fixed lists and testing doc version build --- docs/conf.py | 2 +- docs/guides/t2ramsey_characterization.rst | 7 ++----- qiskit_experiments/framework/experiment_data.py | 10 ++++++---- .../library/characterization/readout_angle.py | 14 +++++++------- .../library/characterization/t1.py | 16 +++++++++------- .../library/characterization/t2ramsey.py | 2 +- .../library/characterization/tphi.py | 2 +- .../visualization/drawers/base_drawer.py | 16 +++++++++------- .../visualization/plotters/base_plotter.py | 7 ++++--- qiskit_experiments/visualization/utils.py | 17 +++++++++-------- 10 files changed, 49 insertions(+), 44 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index bc02b97c0d..356d282003 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -193,7 +193,7 @@ def _get_versions(app, config): context = config.html_context - start_version = (0, 5, 0) + start_version = (0, 4, 0) proc = subprocess.run(["git", "describe", "--abbrev=0"], capture_output=True) proc.check_returncode() current_version = proc.stdout.decode("utf8") diff --git a/docs/guides/t2ramsey_characterization.rst b/docs/guides/t2ramsey_characterization.rst index bf54fdd69c..6d4aa18206 100644 --- a/docs/guides/t2ramsey_characterization.rst +++ b/docs/guides/t2ramsey_characterization.rst @@ -3,11 +3,8 @@ T2 Ramsey Characterization The purpose of the :math:`T_2` Ramsey experiment is to determine two of the qubit's properties: *Ramsey* or *detuning frequency* and -:math:`T_2^\ast`. The rough frequency of the qubit was already -determined previously. The control pulses are based on this frequency. - -In this experiment, we would like to get a more precise estimate of the -qubit's frequency. The difference between the frequency used for the +:math:`T_2^\ast`. In this experiment, we would like to get a more precise estimate of the +qubit's frequency given a rough estimate. The difference between the frequency used for the control rotation pulses, and the precise frequency is called the *detuning frequency*. This part of the experiment is called a *Ramsey Experiment*. :math:`T_2^\ast` represents the rate of decay toward a diff --git a/qiskit_experiments/framework/experiment_data.py b/qiskit_experiments/framework/experiment_data.py index 4377c237cc..a423bbe1cd 100644 --- a/qiskit_experiments/framework/experiment_data.py +++ b/qiskit_experiments/framework/experiment_data.py @@ -147,10 +147,12 @@ class ExperimentData: This class handles the following: - 1. Storing the data related to an experiment - the experiment's metadata, - the analysis results and the figures - 2. Managing jobs and adding data from jobs automatically - 3. Saving/Loading data from the result database + 1. Storing the data related to an experiment - the experiment's metadata, + the analysis results and the figures + 2. Managing jobs and adding data from jobs automatically + 3. Saving/Loading data from the result database + + | The field ``db_data`` is a dataclass (``ExperimentDataclass``) containing all the data that can be stored in the database and loaded from it, and diff --git a/qiskit_experiments/library/characterization/readout_angle.py b/qiskit_experiments/library/characterization/readout_angle.py index 88391031ef..860a7bbd43 100644 --- a/qiskit_experiments/library/characterization/readout_angle.py +++ b/qiskit_experiments/library/characterization/readout_angle.py @@ -28,7 +28,7 @@ class ReadoutAngle(BaseExperiment): r""" - Readout angle experiment class + An experiment to measure the angle between ground and excited state IQ clusters. # section: overview @@ -40,15 +40,15 @@ class ReadoutAngle(BaseExperiment): Each experiment consists of the following steps: 1. Circuits generation: two circuits, the first circuit measures the qubit - in the ground state, the second circuit sets the qubit in the excited state - and measures it. Measurements are in level 1 (kerneled). - + in the ground state, the second circuit sets the qubit in the excited state + and measures it. Measurements are in level 1 (kerneled). 2. Backend execution: actually running the circuits on the device - (or a simulator that supports level 1 measurements). The backend returns - the cluster centers of the ground and excited states. - + (or a simulator that supports level 1 measurements). The backend returns + the cluster centers of the ground and excited states. 3. Analysis of results: return the average of the angles of the two centers. + | + # section: analysis_ref :class:`ReadoutAngleAnalysis` """ diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index da47f38be0..9ae0dddf1d 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -24,7 +24,7 @@ class T1(BaseExperiment): - r"""T1 experiment. + r"""An experiment to measure the qubit relaxation time. # section: overview @@ -32,12 +32,14 @@ class T1(BaseExperiment): Each experiment consists of the following steps: - #. Circuits generation: the circuits set the qubit in the excited state, - wait different time intervals, then measure the qubit. - #. Backend execution: actually running the circuits on the device - (or simulator). - #. Analysis of results: deduction of T\ :sub:`1`\ , based on the outcomes, - by fitting to an exponential curve. + 1. Circuits generation: the circuits set the qubit in the excited state, + wait different time intervals, then measure the qubit. + 2. Backend execution: actually running the circuits on the device + (or simulator). + 3. Analysis of results: deduction of T\ :sub:`1`\ , based on the outcomes, + by fitting to an exponential curve. + + | # section: analysis_ref :class:`.T1Analysis` diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py index 4d4494542b..2882e8f40d 100644 --- a/qiskit_experiments/library/characterization/t2ramsey.py +++ b/qiskit_experiments/library/characterization/t2ramsey.py @@ -27,7 +27,7 @@ class T2Ramsey(BaseExperiment): - r"""T2 Ramsey Experiment. + r"""An experiment to measure the effective qubit dephasing time and Ramsey frequency. # section: overview diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index fbd922d0c1..1c8ba0456e 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -28,7 +28,7 @@ class Tphi(BatchExperiment): - r"""Tphi Experiment Class + r"""An experiment to measure the :math:`x - y` plane dephasing rate. # section: overview diff --git a/qiskit_experiments/visualization/drawers/base_drawer.py b/qiskit_experiments/visualization/drawers/base_drawer.py index bc3109100e..499f93092f 100644 --- a/qiskit_experiments/visualization/drawers/base_drawer.py +++ b/qiskit_experiments/visualization/drawers/base_drawer.py @@ -103,13 +103,15 @@ class BaseDrawer(ABC): The recommended way to customize the legend entries is as follows: - 1. Set the labels in the ``series_params`` option, keyed on the series names. - 2. Initialize the canvas. - 3. Call relevant drawing methods to create the figure. When calling the drawing - method that creates the graphic you would like to use in the legend, set - ``legend=True``. For example, ``drawer.scatter(...,legend=True)`` would use - the scatter points as the legend graphics for the given series. - 4. Format the canvas and call :meth:`figure` to get the figure. + 1. Set the labels in the ``series_params`` option, keyed on the series names. + 2. Initialize the canvas. + 3. Call relevant drawing methods to create the figure. When calling the drawing + method that creates the graphic you would like to use in the legend, set + ``legend=True``. For example, ``drawer.scatter(...,legend=True)`` would use + the scatter points as the legend graphics for the given series. + 4. Format the canvas and call :meth:`figure` to get the figure. + + | .. rubric:: Options and Figure Options diff --git a/qiskit_experiments/visualization/plotters/base_plotter.py b/qiskit_experiments/visualization/plotters/base_plotter.py index 30688f3f9c..7eb70bd424 100644 --- a/qiskit_experiments/visualization/plotters/base_plotter.py +++ b/qiskit_experiments/visualization/plotters/base_plotter.py @@ -502,9 +502,10 @@ def _configure_drawer(self): """Configures :attr:`drawer` before plotting. The following actions are taken: - 1. ``axis``, ``subplots``, and ``style`` are passed to :attr:`drawer`. - 2. ``figure_options`` in :attr:`drawer` are updated based on values set in - the plotter :attr:`figure_options` + + 1. ``axis``, ``subplots``, and ``style`` are passed to :attr:`drawer`. + 2. ``figure_options`` in :attr:`drawer` are updated based on values set in + the plotter :attr:`figure_options` These steps are different as all figure options could be passed to :attr:`drawer`, if the drawer already has a figure option with the same name. diff --git a/qiskit_experiments/visualization/utils.py b/qiskit_experiments/visualization/utils.py index ba0b5c4267..da68534ed8 100644 --- a/qiskit_experiments/visualization/utils.py +++ b/qiskit_experiments/visualization/utils.py @@ -31,14 +31,15 @@ class DataExtentCalculator: Data is registered with a :class:`DataExtentCalculator` so that the computed extent covers all values in the data array. The extent tuple is computed as follows: - 1. The maximum and minimum values for input data is stored whenever new data - arrays are registered. This is the data-extent: the minimum-area bounding box - that contains all registered data. - 2. The data-extent is enlarged/shrunk by scaling its width and height by - :attr:`multiplier`. - 3. If :attr:`aspect_ratio` is not ``None``, the scaled extent tuple is extended - in one of the dimensions so that the output extent tuple is larger and the - target aspect ratio is achieved. + + 1. The maximum and minimum values for input data is stored whenever new data + arrays are registered. This is the data-extent: the minimum-area bounding box + that contains all registered data. + 2. The data-extent is enlarged/shrunk by scaling its width and height by + :attr:`multiplier`. + 3. If :attr:`aspect_ratio` is not ``None``, the scaled extent tuple is extended + in one of the dimensions so that the output extent tuple is larger and the + target aspect ratio is achieved. """ def __init__(self, multiplier: float = 1.0, aspect_ratio: Optional[float] = 1.0): From 9f314b4b12bf9ee509cb05581cf911e3de3811e7 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 28 Feb 2023 22:46:21 -0500 Subject: [PATCH 34/64] reverted test change and added release note --- docs/conf.py | 2 +- releasenotes/notes/docs-refactoring-9f46f6539f57e8bd.yaml | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 releasenotes/notes/docs-refactoring-9f46f6539f57e8bd.yaml diff --git a/docs/conf.py b/docs/conf.py index 356d282003..bc02b97c0d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -193,7 +193,7 @@ def _get_versions(app, config): context = config.html_context - start_version = (0, 4, 0) + start_version = (0, 5, 0) proc = subprocess.run(["git", "describe", "--abbrev=0"], capture_output=True) proc.check_returncode() current_version = proc.stdout.decode("utf8") diff --git a/releasenotes/notes/docs-refactoring-9f46f6539f57e8bd.yaml b/releasenotes/notes/docs-refactoring-9f46f6539f57e8bd.yaml new file mode 100644 index 0000000000..f6eedcbea2 --- /dev/null +++ b/releasenotes/notes/docs-refactoring-9f46f6539f57e8bd.yaml @@ -0,0 +1,6 @@ +--- +other: + - | + The package documentation has been updated with introductory tutorials and how-tos + for solving specific problems. It is now refactored into four sections: learning + tutorials, how-to recipes, experiment guides, and the API references. \ No newline at end of file From 7ae937a3de9827a015e517d56591dbfe66e545dd Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 1 Mar 2023 01:43:58 -0500 Subject: [PATCH 35/64] update experiment descriptions --- docs/guides/restless_measurements.rst | 6 +++--- docs/guides/t2ramsey_characterization.rst | 2 +- .../analysis/correlated_readout_error_analysis.py | 3 +-- .../library/characterization/ef_spectroscopy.py | 2 +- .../library/characterization/fine_amplitude.py | 2 +- qiskit_experiments/library/characterization/fine_drag.py | 4 ++-- .../library/characterization/local_readout_error.py | 2 +- .../library/characterization/qubit_spectroscopy.py | 2 +- qiskit_experiments/library/characterization/ramsey_xy.py | 2 +- .../library/characterization/resonator_spectroscopy.py | 2 +- qiskit_experiments/library/characterization/t2hahn.py | 2 +- qiskit_experiments/library/characterization/tphi.py | 2 +- qiskit_experiments/library/characterization/zz_ramsey.py | 2 +- qiskit_experiments/library/quantum_volume/qv_experiment.py | 2 +- .../randomized_benchmarking/interleaved_rb_experiment.py | 2 +- .../library/randomized_benchmarking/rb_experiment.py | 6 +++--- qiskit_experiments/library/tomography/mit_qpt_experiment.py | 3 ++- qiskit_experiments/library/tomography/mit_qst_experiment.py | 3 ++- qiskit_experiments/library/tomography/qpt_experiment.py | 2 +- qiskit_experiments/library/tomography/qst_experiment.py | 2 +- 20 files changed, 27 insertions(+), 26 deletions(-) diff --git a/docs/guides/restless_measurements.rst b/docs/guides/restless_measurements.rst index e4e1a04316..97f12dabad 100644 --- a/docs/guides/restless_measurements.rst +++ b/docs/guides/restless_measurements.rst @@ -30,15 +30,15 @@ Therefore, the measured outcomes of the restless experiments require post-proces The following example, taken from Ref. [1], illustrates what happens to the single measurement outcomes represented as complex numbers in the IQ plane in a restless setting. Here, we run three circuits with an identity gate and three circuits with -an X gate, each followed by a measurement. The numbers in the IQ shots indicate the +an :math:`X` gate, each followed by a measurement. The numbers in the IQ shots indicate the order in which the shots were acquired. The IQ plane on the left shows the single measurement shots gathered when the qubits are reset. Here, the blue and red points, -corresponding to measurements following the Id and X gates, are associated with the +corresponding to measurements following the :math:`Id` and :math:`X` gates, are associated with the :math:`\left\vert0\right\rangle` and :math:`\left\vert1\right\rangle` states, respectively. By contrast, with restless measurements the qubit is not reset after a measurement. As one can see in the IQ plane on the right the single measurement -outcomes of the Id and X circuits no longer match with the +outcomes of the :math:`Id`` and :math:`X` circuits no longer match with the :math:`\left\vert0\right\rangle` and :math:`\left\vert1\right\rangle` states, respectively. This is why restless measurements need special post-processing. diff --git a/docs/guides/t2ramsey_characterization.rst b/docs/guides/t2ramsey_characterization.rst index 6d4aa18206..cdf9e6dcba 100644 --- a/docs/guides/t2ramsey_characterization.rst +++ b/docs/guides/t2ramsey_characterization.rst @@ -5,7 +5,7 @@ The purpose of the :math:`T_2` Ramsey experiment is to determine two of the qubit's properties: *Ramsey* or *detuning frequency* and :math:`T_2^\ast`. In this experiment, we would like to get a more precise estimate of the qubit's frequency given a rough estimate. The difference between the frequency used for the -control rotation pulses, and the precise frequency is called the +control rotation pulses and the qubit transition frequency is called the *detuning frequency*. This part of the experiment is called a *Ramsey Experiment*. :math:`T_2^\ast` represents the rate of decay toward a mixed state, when the qubit is initialized to the diff --git a/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py b/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py index 6d33e57031..faf650d823 100644 --- a/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py @@ -22,8 +22,7 @@ class CorrelatedReadoutErrorAnalysis(BaseAnalysis): - r""" - Correlated readout error characterization analysis + r"""An analysis to characterize correlated readout error. # section: overview diff --git a/qiskit_experiments/library/characterization/ef_spectroscopy.py b/qiskit_experiments/library/characterization/ef_spectroscopy.py index 4a36684de5..ab5795269d 100644 --- a/qiskit_experiments/library/characterization/ef_spectroscopy.py +++ b/qiskit_experiments/library/characterization/ef_spectroscopy.py @@ -23,7 +23,7 @@ class EFSpectroscopy(QubitSpectroscopy): - """Class that runs spectroscopy on the e-f transition by scanning the frequency. + """A spectroscopy experiment to obtain a frequency sweep of the qubit's e-f transition. # section: overview The circuits produced by spectroscopy, i.e. diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index 0a72399885..f5551650e5 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -27,7 +27,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): - r"""Error amplifying fine amplitude experiment. + r"""An experiment to determine the optimal pulse amplitude by amplifying gate errors. # section: overview diff --git a/qiskit_experiments/library/characterization/fine_drag.py b/qiskit_experiments/library/characterization/fine_drag.py index eecfb658fc..0bc93dd5e2 100644 --- a/qiskit_experiments/library/characterization/fine_drag.py +++ b/qiskit_experiments/library/characterization/fine_drag.py @@ -26,7 +26,7 @@ class FineDrag(BaseExperiment, RestlessMixin): - r"""Fine DRAG experiment. + r"""An experiment that performs fine characterizations of DRAG pulse coefficients. # section: overview @@ -44,7 +44,7 @@ class FineDrag(BaseExperiment, RestlessMixin): meas: 1/══════════════════════════════════════════════════════╩═ 0 - Here, Pre and Post designate gates that may be pre-appended and and post-appended, + Here, "Pre" and "Post" designate gates that may be pre-appended and and post-appended, respectively, to the repeated sequence of Rp - Rz - Rp - Rz gates. When calibrating a pulse with a target rotation angle of π the Pre and Post gates are Id and RYGate(π/2), respectively. When calibrating a pulse with a target rotation angle of π/2 the Pre and diff --git a/qiskit_experiments/library/characterization/local_readout_error.py b/qiskit_experiments/library/characterization/local_readout_error.py index e73b74f88d..a26bb68f87 100644 --- a/qiskit_experiments/library/characterization/local_readout_error.py +++ b/qiskit_experiments/library/characterization/local_readout_error.py @@ -25,7 +25,7 @@ class LocalReadoutError(BaseExperiment): - r"""Class for local readout error characterization experiment. + r"""An experiment for characterizing local readout error. # section: overview diff --git a/qiskit_experiments/library/characterization/qubit_spectroscopy.py b/qiskit_experiments/library/characterization/qubit_spectroscopy.py index d2cc65be9e..62d6f15ba4 100644 --- a/qiskit_experiments/library/characterization/qubit_spectroscopy.py +++ b/qiskit_experiments/library/characterization/qubit_spectroscopy.py @@ -25,7 +25,7 @@ class QubitSpectroscopy(Spectroscopy): - """Class that runs spectroscopy by sweeping the qubit frequency. + """A spectroscopy experiment to obtain a frequency sweep of the qubit. # section: overview The circuits produced by spectroscopy, i.e. diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py index 6a6e967de1..db5a390f79 100644 --- a/qiskit_experiments/library/characterization/ramsey_xy.py +++ b/qiskit_experiments/library/characterization/ramsey_xy.py @@ -27,7 +27,7 @@ class RamseyXY(BaseExperiment, RestlessMixin): - r"""Ramsey XY experiment to measure the frequency of a qubit. + r"""A sign-sensitive experiment to measure the frequency of a qubit. # section: overview diff --git a/qiskit_experiments/library/characterization/resonator_spectroscopy.py b/qiskit_experiments/library/characterization/resonator_spectroscopy.py index 99417e702b..a54e695681 100644 --- a/qiskit_experiments/library/characterization/resonator_spectroscopy.py +++ b/qiskit_experiments/library/characterization/resonator_spectroscopy.py @@ -28,7 +28,7 @@ class ResonatorSpectroscopy(Spectroscopy): - """Perform spectroscopy on the readout resonator. + """An experiment to perform frequency spectroscopy of the readout resonator. # section: overview This experiment does spectroscopy on the readout resonator. It applies the following diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py index 017b99e798..865ed4e463 100644 --- a/qiskit_experiments/library/characterization/t2hahn.py +++ b/qiskit_experiments/library/characterization/t2hahn.py @@ -26,7 +26,7 @@ class T2Hahn(BaseExperiment): - r"""T2 Hahn Echo Experiment. + r"""An experiment to measure the dephasing time using Hahn echos. # section: overview diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index 1c8ba0456e..fda55fcd0a 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -28,7 +28,7 @@ class Tphi(BatchExperiment): - r"""An experiment to measure the :math:`x - y` plane dephasing rate. + r"""An experiment to measure the qubit dephasing rate in the :math:`x - y` plane. # section: overview diff --git a/qiskit_experiments/library/characterization/zz_ramsey.py b/qiskit_experiments/library/characterization/zz_ramsey.py index 8168b6d741..0554153cd0 100644 --- a/qiskit_experiments/library/characterization/zz_ramsey.py +++ b/qiskit_experiments/library/characterization/zz_ramsey.py @@ -27,7 +27,7 @@ class ZZRamsey(BaseExperiment): - r"""Experiment to characterize the static :math:`ZZ` interaction for a qubit pair + r"""An experiment to characterize the static :math:`ZZ` interaction for a qubit pair. # section: overview diff --git a/qiskit_experiments/library/quantum_volume/qv_experiment.py b/qiskit_experiments/library/quantum_volume/qv_experiment.py index 2f81d5d90b..37ed9a3971 100644 --- a/qiskit_experiments/library/quantum_volume/qv_experiment.py +++ b/qiskit_experiments/library/quantum_volume/qv_experiment.py @@ -34,7 +34,7 @@ class QuantumVolume(BaseExperiment): - """Quantum Volume Experiment class. + """An experiment to measure the largest random square circuit that can be run on a processor. # section: overview Quantum Volume (QV) is a single-number metric that can be measured using a concrete protocol diff --git a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py index 20f030c4c4..fb41a133a5 100644 --- a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py +++ b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py @@ -33,7 +33,7 @@ class InterleavedRB(StandardRB): - """Interleaved randomized benchmarking experiment. + """An experiment to characterize the error rate of a specific gate on a device. # section: overview Interleaved Randomized Benchmarking (RB) is a method diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py index cc85f4e4ee..1007543ff6 100644 --- a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py +++ b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py @@ -54,11 +54,11 @@ class StandardRB(BaseExperiment, RestlessMixin): - """Standard randomized benchmarking experiment. + """An experiment to characterize the error rate of a gate set on a device. # section: overview Randomized Benchmarking (RB) is an efficient and robust method - for estimating the average error-rate of a set of quantum gate operations. + for estimating the average error rate of a set of quantum gate operations. See `Qiskit Textbook `_ for an explanation on the RB method. @@ -91,7 +91,7 @@ def __init__( """Initialize a standard randomized benchmarking experiment. Args: - physical_qubits: list of physical qubits for the experiment. + physical_qubits: List of physical qubits for the experiment. lengths: A list of RB sequences lengths. backend: The backend to run the experiment on. num_samples: Number of samples to generate for each sequence length. diff --git a/qiskit_experiments/library/tomography/mit_qpt_experiment.py b/qiskit_experiments/library/tomography/mit_qpt_experiment.py index 403a7f9b1e..d477a3a004 100644 --- a/qiskit_experiments/library/tomography/mit_qpt_experiment.py +++ b/qiskit_experiments/library/tomography/mit_qpt_experiment.py @@ -25,7 +25,8 @@ class MitigatedProcessTomography(BatchExperiment): - """Readout error mitigated quantum process tomography experiment. + """A batched experiment to characterize readout error then perform process tomography + for doing readout error mitigated process tomography. # section: overview Readout error mitigated Quantum process tomography is a batch diff --git a/qiskit_experiments/library/tomography/mit_qst_experiment.py b/qiskit_experiments/library/tomography/mit_qst_experiment.py index 44629f8252..1a64eefb83 100644 --- a/qiskit_experiments/library/tomography/mit_qst_experiment.py +++ b/qiskit_experiments/library/tomography/mit_qst_experiment.py @@ -25,7 +25,8 @@ class MitigatedStateTomography(BatchExperiment): - """Readout error mitigated quantum state tomography experiment. + """A batched experiment to characterize readout error then perform state tomography + for doing readout error mitigated state tomography. # section: overview Readout error mitigated quantum state tomography is a batch diff --git a/qiskit_experiments/library/tomography/qpt_experiment.py b/qiskit_experiments/library/tomography/qpt_experiment.py index c83380dba2..125e13e8ad 100644 --- a/qiskit_experiments/library/tomography/qpt_experiment.py +++ b/qiskit_experiments/library/tomography/qpt_experiment.py @@ -28,7 +28,7 @@ class ProcessTomography(TomographyExperiment): - """Quantum process tomography experiment. + """An experiment to reconstruct the quantum channel from measurement data. # section: overview Quantum process tomography (QPT) is a method for experimentally diff --git a/qiskit_experiments/library/tomography/qst_experiment.py b/qiskit_experiments/library/tomography/qst_experiment.py index bc68a42ff6..24e31f2b1f 100644 --- a/qiskit_experiments/library/tomography/qst_experiment.py +++ b/qiskit_experiments/library/tomography/qst_experiment.py @@ -27,7 +27,7 @@ class StateTomography(TomographyExperiment): - """Quantum state tomography experiment. + """An experiment to reconstruct the quantum state from measurement data. # section: overview Quantum state tomography (QST) is a method for experimentally From c26afd6ccf14ab5ebac819c45ae9f335bbc46835 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 1 Mar 2023 01:48:33 -0500 Subject: [PATCH 36/64] updated calibrations tutorial --- docs/tutorials/calibrations.rst | 200 +++++++++++--------------------- 1 file changed, 67 insertions(+), 133 deletions(-) diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index 29f0196e12..1c9e9ceed4 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -1,5 +1,5 @@ Calibrations: Schedules and gate parameters from experiments -############################################################ +============================================================ To produce high fidelity quantum operations, we want to be able to run good gates. The calibration module in Qiskit Experiments allows users to run experiments to find the @@ -7,10 +7,7 @@ pulse shapes and parameter values that maximize the fidelity of the resulting qu operations. Calibration experiments encapsulate the internal processes and allow experimenters to perform calibration operations in a quicker way. Without the experiments module, we would need to define pulse schedules and plot the resulting measurement -data manually (see also the `Qiskit textbook `_ for calibrating qubits with Qiskit Terra). - -Calibrating single-qubit gates on a pulse backend -================================================= +data manually. In this tutorial, we demonstrate how to calibrate single-qubit gates using the calibration framework in Qiskit Experiments. You can run these experiments on any @@ -39,17 +36,17 @@ backend configuration: assert backend_config.open_pulse, "Backend doesn't support Pulse" For the purposes of the tutorial, we will run experiments on our test pulse -backend, ``SingleTransmonTestBackend``, a backend that simulates the underlying pulses -with Qiskit Dynamics on a three-level model of a transmon. We will run experiments to +backend, :class:`.SingleTransmonTestBackend`, a backend that simulates the underlying pulses +with :doc:`Qiskit Dynamics ` on a three-level model of a transmon. We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses, and choose the value of the DRAG parameter that minimizes leakage. The calibration framework requires the user to -- Setup an instance of Calibrations, +- Set up an instance of :class:`.Calibrations`, -- Run calibration experiments, found in ``qiskit_experiments.library.calibration``. +- Run calibration experiments found in :mod:`qiskit_experiments.library.calibration`. -Note that the values of the parameters stored in the instance of the ``Calibrations`` class +Note that the values of the parameters stored in the instance of the :class:`.Calibrations` class will automatically be updated by the calibration experiments. This automatic updating can also be disabled using the ``auto_update`` flag. @@ -70,13 +67,13 @@ This automatic updating can also be disabled using the ``auto_update`` flag. cals=Calibrations.from_backend(backend) print(cals.get_inst_map()) -The two functions below show how to setup an instance of Calibrations. +The two functions below show how to set up an instance of :class:`.Calibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices -are automatically mapped to the channel index when get_schedule is called. +are automatically mapped to the channel index when :meth:`.Calibrations.get_schedule` is called. .. jupyter-execute:: @@ -215,14 +212,14 @@ Calibrating the pulse amplitudes with a Rabi experiment In the Rabi experiment we apply a pulse at the frequency of the qubit and scan its amplitude to find the amplitude that creates a rotation -of a desired angle. We do this with the calibration experiment ``RoughXSXAmplitudeCal``. -This is a specialization of the ``Rabi`` experiment that will update the calibrations -for both the ``X`` pulse and the ``SX`` pulse using a single experiment. +of a desired angle. We do this with the calibration experiment :class:`.RoughXSXAmplitudeCal`. +This is a specialization of the :class:`.Rabi` experiment that will update the calibrations +for both the :math:`X` pulse and the :math:`SX` pulse using a single experiment. .. jupyter-execute:: from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal - rabi = RoughXSXAmplitudeCal(qubit, cals, backend=backend, amplitudes=np.linspace(-0.1, 0.1, 51)) + rabi = RoughXSXAmplitudeCal([qubit], cals, backend=backend, amplitudes=np.linspace(-0.1, 0.1, 51)) The rough amplitude calibration is therefore a Rabi experiment in which each circuit contains a pulse with a gate. Different circuits correspond to pulses @@ -253,7 +250,7 @@ The table above shows that we have now updated the amplitude of our :math:`\pi` from 0.5 to the value obtained in the most recent Rabi experiment. Importantly, since we linked the amplitudes of the ``x`` and ``y`` schedules we will see that the amplitude of the ``y`` schedule has also been updated -as seen when requesting schedules form the ``Calibrations`` instance. +as seen when requesting schedules from the :class:`.Calibrations` instance. Furthermore, we used the result from the Rabi experiment to also update the value of the ``sx`` pulse. @@ -338,121 +335,39 @@ negative amplitude. .. _fine-amplitude-cal: -Fine amplitude calibration --------------------------- - -The :class:`.FineAmplitude` experiment and its subclass experiments repeats -a gate :math:`N` times with a pulse to amplify the under or over-rotations -in the gate to determine the optimal amplitude. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.fine_amplitude import FineXAmplitudeCal - amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name="x") - amp_x_cal.circuits()[5].draw(output="mpl") - -.. jupyter-execute:: - - data_fine = amp_x_cal.run().block_for_results() - data_fine.figure(0) - -.. jupyter-execute:: - - print(data_fine.analysis_results("d_theta")) - -The cell below shows how the amplitude is updated based on the error in the rotation angle measured by the FineXAmplitude experiment. Note that this calculation is automatically done by the Amplitude.update function. - -.. jupyter-execute:: - - dtheta = data_fine.analysis_results("d_theta").value.nominal_value - target_angle = np.pi - scale = target_angle / (target_angle + dtheta) - pulse_amp = cals.get_parameter_value("amp", qubit, "x") - print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") - print(f"Thus, scale the {pulse_amp:.4f} pulse amplitude by {scale:.3f} to obtain {pulse_amp*scale:.5f}.") - -Observe, once again, that the calibrations have automatically been updated. - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - -To check that we have managed to reduce the error in the rotation angle we will run the fine amplitude calibration experiment once again. - -.. jupyter-execute:: - - data_fine2 = amp_x_cal.run().block_for_results() - data_fine2.figure(0) - -.. jupyter-execute:: - - print(data_fine2.analysis_results("d_theta")) - -As can be seen from the data above and the analysis result below -we have managed to reduce the error in the rotation angle dtheta. - -Fine amplitude calibration of the :math:`\pi`/2 rotation --------------------------------------------------------- - -We now wish to calibrate the amplitude of the :math:`\pi/2` rotation. - -.. jupyter-execute:: - - from qiskit_experiments.library.calibration.fine_amplitude import FineSXAmplitudeCal - - amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name="sx") - amp_sx_cal.circuits()[5].draw(output="mpl") - -.. jupyter-execute:: - - data_fine_sx = amp_sx_cal.run().block_for_results() - data_fine_sx.figure(0) - -.. jupyter-execute:: - - print(data_fine_sx.analysis_results(0)) - -.. jupyter-execute:: - - print(data_fine_sx.analysis_results("d_theta")) - -.. jupyter-execute:: - - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - - Fine calibrations of a pulse amplitude -====================================== +-------------------------------------- The amplitude of a pulse can be precisely calibrated using error amplifying gate sequences. These gate sequences apply the same gate a variable number of times. Therefore, if each gate has a small error :math:`d\theta` in the rotation angle then a sequence of :math:`n` gates will have a rotation error of :math:`n` * :math:`d\theta`. +The :class:`.FineAmplitude` experiment and its subclass experiments implements +these sequences to obtain the correction value of imperfect pulses. We will first examine +how to detect imperfect pulses using the characterization version of these experiments, +then update calibrations with a calibration experiment. .. jupyter-execute:: - import numpy as np from qiskit.pulse import InstructionScheduleMap - import qiskit.pulse as pulse - from qiskit_experiments.library import FineXAmplitude, FineSXAmplitude - from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend + from qiskit_experiments.library import FineXAmplitude .. jupyter-execute:: backend = SingleTransmonTestBackend() qubit = 0 -Fine `X` gate amplitude calibration ------------------------------------ +Detecting over- and under-rotated pulses +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -We will run the error amplifying experiments with our own pulse schedules -on which we purposefully add over and under rotations. -To do this we create an instruction to schedule map which we populate with +We now run the error amplifying experiments with our own pulse schedules +on which we purposefully add over- and under-rotations to observe their effects. +To do this, we create an instruction to schedule map which we populate with the schedules we wish to work with. This instruction schedule map is then given to the transpile options of the experiment so that the Qiskit transpiler can attach the pulse schedules to the gates in the experiments. -We base all our pulses on the default X pulse of "SingleTransmonTestBackend". +We base all our pulses on the default :math:`X` pulse of :class:`.SingleTransmonTestBackend`. .. jupyter-execute:: @@ -460,10 +375,10 @@ We base all our pulses on the default X pulse of "SingleTransmonTestBackend". d0, inst_map = pulse.DriveChannel(qubit), pulse.InstructionScheduleMap() -We now take the ideal x pulse amplitude reported by the backend and +We now take the ideal :math:`X` pulse amplitude reported by the backend and add/subtract a 2% over/underrotation to it by scaling the ideal amplitude and see -if the experiment can detect this over/underrotation. We replace the default X pulse -in the instruction schedule map with this over/underrotated pulse. +if the experiment can detect this over/underrotation. We replace the default :math:`X` pulse +in the instruction schedule map with this over/under-rotated pulse. .. jupyter-execute:: @@ -477,8 +392,8 @@ in the instruction schedule map with this over/underrotated pulse. pulse.play(pulse.Drag(x_pulse.duration, over_amp, x_pulse.sigma, x_pulse.beta), d0) inst_map.add("x", (qubit,), x_over) -Let's look at one of the circuits of the FineXAmplitude experiment. -To calibrate the X gate we add an SX gate before the X gates to move the ideal population +Let's look at one of the circuits of the :class:`.FineXAmplitude` experiment. +To calibrate the :math:`X` gate, we add an :math:`SX` gate before the :math:`X` gates to move the ideal population to the equator of the Bloch sphere where the sensitivity to over/under rotations is the highest. .. jupyter-execute:: @@ -491,13 +406,13 @@ to the equator of the Bloch sphere where the sensitivity to over/under rotations # do the experiment exp_data_over = overamp_cal.run(backend).block_for_results() - print(f"The ping-pong pattern points on the figure below indicate") - print(f"an over rotation which makes the initial state rotate more than pi.") - print(f"Therefore, the miscalibrated X gate makes the qubit stay away from the Bloch sphere equator.") exp_data_over.figure(0) -We now look at a pulse with an under rotation to see how the FineXAmplitude experiment -detects this error. We will compare the results to the over rotation above. +The ping-pong pattern on the figure indicates an over-rotation which makes the initial state +rotate more than :math:`\pi`. + +We now look at a pulse with an under rotation to see how the :class:`.FineXAmplitude` experiment +detects this error. We will compare the results to the over-rotation above. .. jupyter-execute:: @@ -513,10 +428,10 @@ detects this error. We will compare the results to the over rotation above. exp_data_under = underamp_cal.run(backend).block_for_results() exp_data_under.figure(0) -Similarly to the over rotation, the under rotated pulse creates +Similarly to the over-rotation, the under-rotated pulse creates qubit populations that do not lie on the equator of the Bloch sphere. However, compared to the ping-pong pattern of the over rotated pulse, -the under rotated pulse produces a flipped ping-pong pattern. +the under rotated pulse produces an inverted ping-pong pattern. This allows us to determine not only the magnitude of the rotation error but also its sign. @@ -533,29 +448,39 @@ but also its sign. print(f"On the other hand, we measued a deviation of {dtheta_under:.3f} rad in under-rotated pulse case.") print(f"Thus, scale the {under_amp:.4f} pulse amplitude by {scale_under:.3f} to obtain {under_amp*scale_under:.5f}.") -Analyzing a :math:`\pi`/2 pulse -------------------------------- -The amplitude of the `SX` gate is calibrated with the FineSXAmplitude experiment. -Unlike the FineXAmplitude experiment, the FineSXAmplitude experiment -does not require other gates than the SX gate since the number of repetitions +Calibrating a :math:`\pi`/2 :math:`X` pulse +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Now we apply the same principles to a different example using the calibration version of +a Fine Amplitude experiment. +The amplitude of the :math:`SX` gate, which is an :math:`X` pulse with half the amplitude, +is calibrated with the :class:`.FineSXAmplitudeCal` experiment. +Unlike the :class:`.FineSXAmplitude` experiment, the :class:`.FineSXAmplitudeCal` experiment +does not require other gates than the :math:`SX` gate since the number of repetitions can be chosen such that the ideal population is always on the equator of the Bloch sphere. -To demonstrate the FineSXAmplitude experiment, we now create a SX pulse by +To demonstrate the :class:`.FineSXAmplitudeCal` experiment, we now create a :math:`SX` pulse by dividing the amplitude of the X pulse by two. We expect that this pulse might have a small rotation error which we want to correct. - .. jupyter-execute:: + from qiskit_experiments.library import FineSXAmplitudeCal + # build sx_pulse with the default x_pulse from defaults and add it to the InstructionScheduleMap sx_pulse = pulse.Drag(x_pulse.duration, 0.5*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name="SXp_d0") with pulse.build(name='sx') as sched: pulse.play(sx_pulse,d0) inst_map.add("sx", (qubit,), sched) - # do the expeirment - amp_cal = FineSXAmplitude(qubit, backend) + amp_cal = FineSXAmplitudeCal([qubit], cals, backend=backend, schedule_name="sx") + amp_cal.circuits()[4].draw(output="mpl") + +Let's run the calibration experiment: + +.. jupyter-execute:: + amp_cal.set_transpile_options(inst_map=inst_map) exp_data_x90p = amp_cal.run().block_for_results() exp_data_x90p.figure(0) @@ -590,16 +515,25 @@ to turn it into a sharp :math:`\pi/2` rotation. data_x90p.figure(0) You can now see that the correction to the pulse amplitude has allowed us -to improve our SX gate as shown by the analysis result below. +to improve our :math:`SX` gate as shown by the analysis result below. .. jupyter-execute:: # check the dtheta print(data_x90p.analysis_results("d_theta")) +Observe, once again, that the calibrations have automatically been updated. + +.. jupyter-execute:: + + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] +See also +-------- +* API documentation: :mod:`~qiskit_experiments.calibration_management` and :mod:`~qiskit_experiments.library.calibration` +* Qiskit Textbook: `Calibrating Qubits with Qiskit Pulse `__ From e6bc8f4a0fcb0feaed988d0b097993e07189ac40 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Wed, 1 Mar 2023 14:02:28 -0500 Subject: [PATCH 37/64] removed ibmq-provider code and added migration note --- docs/howtos/cloud_service.rst | 97 +++++------------- .../experiment_cloud_service/filter.png | Bin 25819 -> 0 bytes .../experiment_cloud_service/metadata.png | Bin 123038 -> 0 bytes .../experiment_cloud_service/t1_deleted.png | Bin 283940 -> 0 bytes .../t1_experiment.png | Bin 125324 -> 0 bytes .../verify_experiment.png | Bin 112524 -> 0 bytes .../web_tags_share.png | Bin 16131 -> 0 bytes docs/howtos/new_experimentdata.rst | 14 ++- docs/tutorials/calibrations.rst | 35 ++----- docs/tutorials/getting_started.rst | 6 +- 10 files changed, 47 insertions(+), 105 deletions(-) delete mode 100644 docs/howtos/experiment_cloud_service/filter.png delete mode 100644 docs/howtos/experiment_cloud_service/metadata.png delete mode 100644 docs/howtos/experiment_cloud_service/t1_deleted.png delete mode 100644 docs/howtos/experiment_cloud_service/t1_experiment.png delete mode 100644 docs/howtos/experiment_cloud_service/verify_experiment.png delete mode 100644 docs/howtos/experiment_cloud_service/web_tags_share.png diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index e8185f50c7..8b15605a86 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -17,25 +17,28 @@ Solution Saving ~~~~~~ -You will need to have :mod:`qiskit-ibmq-provider` installed locally -and an account in the Qiskit cloud service. You must run the experiment on a real IBM -backend to be able to save it. You can save the experiment data by calling ``save()``: +.. note:: + This recipe requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, + consult the `migration guide `_.\ -.. jupyter-input:: +You must run the experiment on a real IBM +backend and not a simulator to be able to save the experiment data. This is done by calling +:meth:`~.ExperimentData.save`: - from qiskit import IBMQ - IBMQ.load_account() - provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main") - backend = provider.get_backend("ibmq_lima") +.. jupyter-input:: + from qiskit_ibm_provider import IBMProvider from qiskit_experiments.library.characterization import T1 import numpy as np + + provider = IBMProvider() + backend = provider.get_backend("ibmq_lima") t1_delays = np.arange(1e-6, 600e-6, 50e-6) exp = T1(qubit=0, delays=t1_delays) - t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() + t1_expdata = exp.run(backend=backend).block_for_results() t1_expdata.save() .. jupyter-output:: @@ -119,65 +122,15 @@ The analysis results have been retrieved as well: Discussion ---------- -Calling :meth:`.ExperimentData.save` before the experiment is complete will +Note that calling :meth:`~.ExperimentData.save` before the experiment is complete will instantiate an experiment entry in the database, but it will not have complete data. To fix this, you can call :meth:`~.ExperimentData.save` again once the experiment is done running. -.. jupyter-input:: - - from qiskit import IBMQ - IBMQ.load_account() - provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main") - backend = provider.get_backend("ibmq_lima") - - t1_expdata = exp.run(backend=backend, shots=1000).block_for_results() - t1_expdata.save() - -.. jupyter-output:: - - You can view the experiment online at https://quantum-computing.ibm.com/experiments/10a43cb0-7cb9-41db-ad74-18ea6cf63704") - - -You can also view the results at the `IBM Quantum Experiments -pane `__ -on the cloud. - -By default, the interface displays all experiments you have privilege to -see, but this link shows your own experiments. You can change that -setting by clicking on the All Experiments dropdown. You can also filter -by device, date, provider, and result by clicking on the filter icon. - -.. image:: ./experiment_cloud_service/filter.png - -| - -Individual experiment pages show the plot, and one or more important -analysis results, which for the :math:`T_1` experiment is the fitted -:math:`T_1` value. - -.. image:: ./experiment_cloud_service/t1_experiment.png - -| - -The metadata field shows experiment metadata included in the :class:`.ExperimentData` object. - -.. image:: ./experiment_cloud_service/metadata.png - -| - -You can change the quality and verify/unverify the results upon -selection of an analysis result. Quality is an automatic parameter -generated by the experiment analysis based on pre-set criteria. The verification field is for a -human to determine whether the result is acceptable. - -.. image:: ./experiment_cloud_service/verify_experiment.png - - Auto-saving an experiment ~~~~~~~~~~~~~~~~~~~~~~~~~ -The :meth:`.ExperimentData.auto_save` feature automatically saves changes to the +The :meth:`~.ExperimentData.auto_save` feature automatically saves changes to the :class:`.ExperimentData` object to the cloud service whenever it's updated. .. jupyter-input:: @@ -198,7 +151,7 @@ Deleting an experiment Both figures and analysis results can be deleted. Note that unless you have auto save on, the update has to be manually saved to the remote -database by calling :meth:`.ExperimentData.save`. Because there are two analysis +database by calling :meth:`~.ExperimentData.save`. Because there are two analysis results, one for the T1 parameter and one for the curve fitting results, we must delete twice to fully remove the analysis results. @@ -214,11 +167,6 @@ delete twice to fully remove the analysis results. Are you sure you want to delete the analysis result? [y/N]: y Are you sure you want to delete the analysis result? [y/N]: y -The web interface shows that both the figure and analysis result have been -deleted: - -.. image:: ./experiment_cloud_service/t1_deleted.png - Tagging and sharing experiments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -231,7 +179,16 @@ For example, an experiment can be tagged and made public with the following code t1_expdata.share_level = "public" t1_expdata.notes = "Example note." -These fields can also be updated in the web interface. For more information about -using the interface, consult its -`documentation `__. +Web interface +~~~~~~~~~~~~~ + +You can also view experiment results as well as change the tags and share level at the `IBM Quantum Experiments +pane `__ +on the cloud. The documentation below explains how to view, search, and share experiment +data entries. + +See also +-------- + +* `Experiments web interface documentation `__ diff --git a/docs/howtos/experiment_cloud_service/filter.png b/docs/howtos/experiment_cloud_service/filter.png deleted file mode 100644 index 974c08c1d84a3078ed65e613b74b4f2e54a99088..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25819 zcmdSBby!v3_C88?=cY?aYHvWgyQCFFIs~MjQ*VPKFzz;z}H67XB5 z)o~0423E{cQc_i3Qj%KL(e9O{wHXWyQ-X=1A)Xv7Q=gHMp<&-J3p1Ldn|gS7w7Q{R zPuoaads}BtTTVv0uI>*I&JTFa4w!tk_Btz!XzJ@v?0u}eFBB6$`w+-`;;oQ$ckj0% zYY3zLP>^R+Mw;;R=Yurl6bAJUZWB6`cfDBw0p|w~57|%r^|Sigj@(ur z9$JYX9v<8vW4GTiFpwX^E;Z=OuAn)J03v;ErY-;K=~I};z%>dCY?vhs0&oQj{Ez}a zFfj0mp)kn6Up(MPDhKYLPmw@5@c&%HWJ13vt|2Ke5B$|IaWpftb+WK?E{GK(0d6&8 zsj2O({ZvW7#LkA@$kfi*jNRSF9@+#($Xx)qv@vrwqIS2jwsjJ47pDF74FTX9`ZfnG 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zi$z0tbGVtCs%GF+&fSS9m+;qMh-Unv(R5^>X&m*ekYo!U7lcEefY|DSRgsCL)rNli+}5Ou8{@2a~Zj)MaQ== z1cARaRrJl_>GB7d{y71?L5Ao1)r)Z$h(|Yv-MzxL+JEml^rIMOxTX@?5bPh{?8pwB zMkQ@C;y+Jg2b@MH=+?LtEE71*U~e^WnmqKfpLY4EY9vyr~btDxWId!s9#{J6u@Y~!FT~*v>(1Q#+Gr1Q0 z0%kx&oxpZFN4x%}SwbcHZwoFC{~Vpw^@eRbz=XG9 zT_GLtyYFW|v%Z}h^_O^gVb{sOKkA}7VE6wC?fpOdVYBTDBFMggrhJ1%g!9B=aG6I zsppXv9BIKn)q;F_&GcV>p}ndsf9K(mi2kXFj>K{#mLvOlqyaHIuCT5zNVM_O>C z1xH$NqyaHIuCT5zNVM_O>C1xH%&|Fafcx<5hsB}k=zsP=^6FTfvV1r7PUyJpY- E1H8o|{r~^~ diff --git a/qiskit_experiments/framework/restless_mixin.py b/qiskit_experiments/framework/restless_mixin.py index 42666e8d56..e8a17ea4ec 100644 --- a/qiskit_experiments/framework/restless_mixin.py +++ b/qiskit_experiments/framework/restless_mixin.py @@ -31,11 +31,11 @@ class RestlessMixin: """A mixin to facilitate restless experiments. - This class defines the following methods + This class defines the following methods: - - :meth:`~.RestlessMixin.enable_restless` - - :meth:`~.RestlessMixin._get_restless_processor` - - :meth:`~.RestlessMixin._t1_check` + - :meth:`~.RestlessMixin.enable_restless` + - :meth:`~.RestlessMixin._get_restless_processor` + - :meth:`~.RestlessMixin._t1_check` A restless enabled experiment is an experiment that can be run in a restless measurement setting. In restless measurements, the qubit is not reset after diff --git a/requirements-dev.txt b/requirements-dev.txt index 42963d3110..e24fa0f60f 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -5,8 +5,8 @@ pylint==2.7.1 jinja2==3.0.3 sphinx~=5.0 jupyter-sphinx>=0.4.0 -# Temporary qiskit-sphinx-theme git repository dependency until the next release -git+https://github.com/Qiskit/qiskit_sphinx_theme.git +# Temporary pinned qiskit-sphinx-theme dependency until its next release +git+https://github.com/Qiskit/qiskit_sphinx_theme.git@edbff26d59aa194d25d87ef37f052e3043c9c822 sphinx-autodoc-typehints<=1.20.2 sphinx-design==0.3.0 pygments>=2.4 From eea7382b73f712a34f73c1114654386570e8eaf3 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 9 Mar 2023 22:04:39 -0500 Subject: [PATCH 55/64] change experiment guide to manual --- CONTRIBUTING.md | 4 +- docs/GUIDELINES.md | 24 +++++------ docs/_ext/custom_styles/styles.py | 4 +- docs/_static/{guides.png => manuals.png} | Bin docs/apidocs/index.rst | 9 ++-- docs/conf.py | 30 ++++++++------ docs/howtos/cloud_service.rst | 4 +- docs/howtos/index.rst | 13 +++--- docs/howtos/new_experimentdata.rst | 4 +- docs/index.rst | 39 ++++++++++-------- .../benchmarking/quantum_volume.rst | 0 .../benchmarking/randomized_benchmarking.rst | 0 .../benchmarking/state_tomography.rst | 0 .../characterization/t1.rst | 0 .../characterization/t2hahn.rst | 0 .../characterization/t2ramsey.rst | 0 .../characterization/tphi.rst | 0 docs/{guides => manuals}/index.rst | 13 ++++-- .../measurement/readout_mitigation.rst | 0 .../measurement/restless_measurements.rst | 0 .../measurement/restless_shots.png | Bin docs/tutorials/data_processor.rst | 2 +- .../tutorials/images/compositeexperiments.png | Bin 763049 -> 209682 bytes docs/tutorials/images/experimentarch.png | Bin 221072 -> 186493 bytes docs/tutorials/index.rst | 13 +++--- qiskit_experiments/framework/__init__.py | 1 - .../library/calibration/rough_drag_cal.py | 2 +- .../characterization/cr_hamiltonian.py | 2 +- .../library/characterization/drag.py | 2 +- .../characterization/fine_amplitude.py | 2 +- .../library/characterization/rabi.py | 2 +- .../library/characterization/t1.py | 4 +- .../library/characterization/t2hahn.py | 4 +- .../library/characterization/t2ramsey.py | 4 +- .../library/characterization/tphi.py | 4 +- .../docs-refactoring-9f46f6539f57e8bd.yaml | 2 +- 36 files changed, 98 insertions(+), 90 deletions(-) rename docs/_static/{guides.png => manuals.png} (100%) rename docs/{guides => manuals}/benchmarking/quantum_volume.rst (100%) rename docs/{guides => manuals}/benchmarking/randomized_benchmarking.rst (100%) rename docs/{guides => manuals}/benchmarking/state_tomography.rst (100%) rename docs/{guides => manuals}/characterization/t1.rst (100%) rename docs/{guides => manuals}/characterization/t2hahn.rst (100%) rename docs/{guides => manuals}/characterization/t2ramsey.rst (100%) rename docs/{guides => manuals}/characterization/tphi.rst (100%) rename docs/{guides => manuals}/index.rst (81%) rename docs/{guides => manuals}/measurement/readout_mitigation.rst (100%) rename docs/{guides => manuals}/measurement/restless_measurements.rst (100%) rename docs/{guides => manuals}/measurement/restless_shots.png (100%) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 0419621ff0..a07f57051a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -326,8 +326,8 @@ This will build all the documentation into `docs/_build/html`. The main page manually: * `tutorials/`: Contains the built tutorials. -* `howtos/`: Contains the built how-tos. -* `guides/`: Contains the built experiment guides. +* `howtos/`: Contains the built how-to guides. +* `manuals/`: Contains the built experiment manuals. * `apidocs/`: Contains the API docs automatically compiled from module docstrings. * `release_notes.html`: Contains the release notes. diff --git a/docs/GUIDELINES.md b/docs/GUIDELINES.md index f3ad03f1d2..861b92a6ea 100644 --- a/docs/GUIDELINES.md +++ b/docs/GUIDELINES.md @@ -7,8 +7,8 @@ Contents: - [Introduction](#introduction) - [General formatting guidelines](#general-formatting-guidelines) - [Tutorials](#tutorials) - - [How-to recipes](#how-to-recipes) - - [Experiment guides](#experiment-guides) + - [How-to guides](#how-to-guides) + - [Experiment manuals](#experiment-manuals) - [API documentation](#api-documentation) - [Experiment class documentation](#experiment-class-documentation) - [Analysis class documentation](#analysis-class-documentation) @@ -19,8 +19,8 @@ Contents: Qiskit Experiments documentation is split into four sections: - Tutorials for learning the package from the ground up -- How-to recipes for solving specific problems -- Experiment guides for information on specific experiments +- How-to guides for solving specific problems +- Experiment manuals for information on specific experiments - API reference for technical documentation All documentation is written in reStructuredText format and then built into formatted @@ -75,13 +75,13 @@ tutorials: be used to the old usage pattern. -### How-to recipes +### How-to guides The title of a how-to should clearly describe what problem it's solving. It should be an action that follows "How to". The text itself has up to four sections, but only the first two are required: -- Problem: This section should describe the user problem that your recipe is providing a +- Problem: This section should describe the user problem that your guide is providing a direct solution for in second person. This should ideally be a one-liner so that users can quickly scan it and see if it’s relevant to what they’re trying to do. @@ -132,9 +132,9 @@ See also ``` -### Experiment guides +### Experiment manuals -The main goal of `qiskit-experiment` experiment guides is to serve as user guides for +The main goal of `qiskit-experiment` experiment manuals is to serve as user manuals for the various package components such as the characterization and calibration experiments. To this end, each guide should introduce the cover the main (if not all) use-cases of the documented functionality, including code examples and expected outputs. Another @@ -147,8 +147,8 @@ with a code example and example outputs by printing relevant analysis results an figures. Required and common parameters, such as experiment and analysis options, shoud be covered. -See the [Randomized Benchmarking](https://qiskit.org/documentation/experiments/guides/benchmarking/randomized_benchmarking.html) -guide and its [source code](docs/guides/benchmarking/randomized_benchmarking.rst) for an +See the [Randomized Benchmarking](https://qiskit.org/documentation/experiments/manuals/benchmarking/randomized_benchmarking.html) +guide and its [source code](docs/manuals/benchmarking/randomized_benchmarking.rst) for an example. Here is a simple template for a guide: ``` @@ -266,8 +266,8 @@ There are several predefined sections for the class docstring. you can use the above macro, where you can provide a string for the hyperlink and the destination location separated by single comma. - # section: guide - Optional. Link to user guide of this experiment if one exists. + # section: manual + Optional. Link to manuals of this experiment if one exists. # section: see_also Optional. You can list relevant experiment or module. diff --git a/docs/_ext/custom_styles/styles.py b/docs/_ext/custom_styles/styles.py index eb1b994881..d411965881 100644 --- a/docs/_ext/custom_styles/styles.py +++ b/docs/_ext/custom_styles/styles.py @@ -161,7 +161,7 @@ class ExperimentDocstring(QiskitExperimentDocstring): "warning": load_standard_section, "overview": load_standard_section, "reference": load_standard_section, - "guide": load_standard_section, + "manual": load_standard_section, "analysis_ref": load_standard_section, "experiment_opts": None, "transpiler_opts": None, @@ -266,7 +266,7 @@ class AnalysisDocstring(QiskitExperimentDocstring): "fit_model": load_standard_section, "fit_parameters": load_fit_parameters, "reference": load_standard_section, - "guide": load_standard_section, + "manual": load_standard_section, "analysis_opts": None, "example": load_standard_section, "note": load_standard_section, diff --git a/docs/_static/guides.png b/docs/_static/manuals.png similarity index 100% rename from docs/_static/guides.png rename to docs/_static/manuals.png diff --git a/docs/apidocs/index.rst b/docs/apidocs/index.rst index ea9b00c5be..7533030ccd 100644 --- a/docs/apidocs/index.rst +++ b/docs/apidocs/index.rst @@ -1,6 +1,3 @@ -.. _qiskit-experiments: - -============= API Reference ============= @@ -9,7 +6,7 @@ API Reference This package is still under active development and it is very likely that there will be breaking API changes in future releases. If you encounter any bugs, please open an issue on - `Github `_. + `GitHub `_. The API documentation is organized into two sections below. The package modules include the framework, the experiment library, experiment modules, and test utilities. Experiment modules are @@ -17,7 +14,7 @@ the main categories of the experiment library itself, such as qubit characteriza and experimental suites like tomography. Package Modules -=============== +--------------- .. toctree:: :maxdepth: 1 @@ -33,7 +30,7 @@ Package Modules test Experiment Modules -================== +------------------ .. toctree:: :maxdepth: 1 diff --git a/docs/conf.py b/docs/conf.py index 7fc1b7f70c..11e01a99f9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -89,19 +89,19 @@ nbsphinx_widgets_path = "" exclude_patterns = ["_build", "**.ipynb_checkpoints"] -# Thumbnails for experiment guides from output images +# Thumbnails for experiment manuals from output images # These should ideally be automatically generated using a custom macro to specify # chosen cells for thumbnails, like the nbsphinx-gallery tag nbsphinx_thumbnails = { - "guides/benchmarking/quantum_volume": "_images/quantum_volume_2_0.png", - "guides/measurement/readout_mitigation": "_images/readout_mitigation_4_0.png", - "guides/benchmarking/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", - "guides/measurement/restless_measurements": "_images/restless_shots.png", - "guides/benchmarking/state_tomography": "_images/state_tomography_3_0.png", - "guides/characterization/t1": "_images/t1_0_0.png", - "guides/characterization/t2ramsey": "_images/t2ramsey_4_0.png", - "guides/characterization/tphi": "_images/tphi_8_0.png", - "guides/characterization/t2hahn": "_images/t2hahn_5_0.png", + "manuals/benchmarking/quantum_volume": "_images/quantum_volume_2_0.png", + "manuals/measurement/readout_mitigation": "_images/readout_mitigation_4_0.png", + "manuals/benchmarking/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", + "manuals/measurement/restless_measurements": "_images/restless_shots.png", + "manuals/benchmarking/state_tomography": "_images/state_tomography_3_0.png", + "manuals/characterization/t1": "_images/t1_0_0.png", + "manuals/characterization/t2ramsey": "_images/t2ramsey_4_0.png", + "manuals/characterization/tphi": "_images/tphi_8_0.png", + "manuals/characterization/t2hahn": "_images/t2hahn_5_0.png", } # Add `data keys` and `style parameters` alias. Needed for `expected_*_data_keys` methods in @@ -169,9 +169,8 @@ html_context = { "analytics_enabled": True, "expandable_sidebar": True, -} # enable built-in analytics +} -# html_sidebars = {'**': ['globaltoc.html']} html_last_updated_fmt = "%Y/%m/%d" html_theme_options = { @@ -236,3 +235,10 @@ def _get_version_label(current_version): def setup(app): app.connect("config-inited", _get_versions) + app.connect("autodoc-skip-member", maybe_skip_member) + + +def maybe_skip_member(app, what, name, obj, skip, options): + if not skip: + return name in {"analysis", "set_run_options"} and what == "attribute" + return skip diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index 7a32b3c8d6..1b3a5019e1 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -2,7 +2,7 @@ Save and load experiment data with the cloud service ==================================================== .. note:: - This recipe is only for those who have access to the cloud service. You can + This guide is only for those who have access to the cloud service. You can check whether you do by logging into the IBM Quantum interface and seeing if you can see the `database `__. @@ -18,7 +18,7 @@ Saving ~~~~~~ .. note:: - This recipe requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, + This guide requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, consult the `migration guide `_.\ You must run the experiment on a real IBM diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index 8f86897150..4fc1fcab39 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -1,10 +1,9 @@ -####### -How-Tos -####### +How-To Guides +============= -This section of the documentation provides concrete recipes for how to do specific -useful actions in Qiskit Experiments. It is recommended that you first familiarize -with :ref:`the basics ` of the package before using these recipes. +This section of the documentation provides concrete step-by-step instructions for how to +do specific useful actions in Qiskit Experiments. It is recommended that you first +familiarize with :ref:`the basics ` of the package before using these guides. .. toctree:: :caption: How to... @@ -15,7 +14,7 @@ with :ref:`the basics ` of the package before using these recipes. | -If there are recipes on solving specific problems that you'd like to see added, please +If there are guides on solving specific problems that you'd like to see added, please `file an issue on GitHub `_. | diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst index 9d314df31a..bb7107f86d 100644 --- a/docs/howtos/new_experimentdata.rst +++ b/docs/howtos/new_experimentdata.rst @@ -11,7 +11,7 @@ Solution -------- .. note:: - This recipe requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, + This guide requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, consult the `migration guide `_.\ Use the code template below. You need to know the exact experiment you @@ -42,7 +42,7 @@ ran and its options, as well as the IDs of the jobs that were executed. Discussion ---------- -This recipe is helpful for cases such as a lost connection during experiment execution, +This guide is helpful for cases such as a lost connection during experiment execution, where the jobs may have finished running on the remote backends but the :class:`.ExperimentData` class returned upon completion of an experiment does not contain correct results. diff --git a/docs/index.rst b/docs/index.rst index ddddafd719..2807a7aaae 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,13 +1,14 @@ -################################ -Qiskit Experiments documentation -################################ +.. _qiskit-experiments: + +Qiskit Experiments Documentation +================================ .. warning:: This package is still under active development and it is very likely that there will be breaking API changes in future releases. If you encounter any bugs, please open an issue on - `Github `_. + `GitHub `_. Qiskit Experiments provides both a :doc:`library ` of standard quantum characterization, calibration, and verification experiments, and a @@ -39,13 +40,13 @@ We've divided up the documentation into four sections with different purposes: To the learning tutorials - .. grid-item-card:: How-Tos + .. grid-item-card:: How-To Guides .. image:: _static/howtos.png :target: howtos/index.html - These individual how-to recipes provide short and direct solutions to some commonly - asked questions for Qiskit Experiments users. You'll find in these recipes: + These standalone how-to guides provide short and direct solutions to some commonly + asked questions for Qiskit Experiments users. You'll find in these guides: * How to :doc:`re-instantiate experiment data for an existing experiment ` * How to :doc:`customize the splitting of circuits into jobs ` @@ -56,26 +57,27 @@ We've divided up the documentation into four sections with different purposes: :expand: :color: secondary - To the how-to recipes + To the how-to guides - .. grid-item-card:: Experiment Guides + .. grid-item-card:: Experiment Manuals - .. image:: _static/guides.png - :target: guides/index.html + .. image:: _static/manuals.png + :target: manuals/index.html - These are in-depth guides to key experiments in the package, describing - their background, principle, and how to run them in Qiskit Experiments. You'll find in these guides: + These are in-depth manuals to key experiments in the package, describing their + background, principle, and how to run them in Qiskit Experiments. You'll find in + these manuals: - * How to analyze 1- and 2-qubit errors in :doc:`randomized benchmarking ` - * How to calculate the speedup from using :doc:`restless measurements ` + * How to analyze 1- and 2-qubit errors in :doc:`randomized benchmarking ` + * How to calculate the speedup from using :doc:`restless measurements ` +++ - .. button-ref:: guides/index + .. button-ref:: manuals/index :expand: :color: secondary - To the experiment guides + To the experiment manuals .. grid-item-card:: API Reference @@ -114,9 +116,10 @@ We've divided up the documentation into four sections with different purposes: :hidden: howtos/index - guides/index + manuals/index apidocs/index release_notes + Development Branch Docs GitHub | diff --git a/docs/guides/benchmarking/quantum_volume.rst b/docs/manuals/benchmarking/quantum_volume.rst similarity index 100% rename from docs/guides/benchmarking/quantum_volume.rst rename to docs/manuals/benchmarking/quantum_volume.rst diff --git a/docs/guides/benchmarking/randomized_benchmarking.rst b/docs/manuals/benchmarking/randomized_benchmarking.rst similarity index 100% rename from docs/guides/benchmarking/randomized_benchmarking.rst rename to docs/manuals/benchmarking/randomized_benchmarking.rst diff --git a/docs/guides/benchmarking/state_tomography.rst b/docs/manuals/benchmarking/state_tomography.rst similarity index 100% rename from docs/guides/benchmarking/state_tomography.rst rename to docs/manuals/benchmarking/state_tomography.rst diff --git a/docs/guides/characterization/t1.rst b/docs/manuals/characterization/t1.rst similarity index 100% rename from docs/guides/characterization/t1.rst rename to docs/manuals/characterization/t1.rst diff --git a/docs/guides/characterization/t2hahn.rst b/docs/manuals/characterization/t2hahn.rst similarity index 100% rename from docs/guides/characterization/t2hahn.rst rename to docs/manuals/characterization/t2hahn.rst diff --git a/docs/guides/characterization/t2ramsey.rst b/docs/manuals/characterization/t2ramsey.rst similarity index 100% rename from docs/guides/characterization/t2ramsey.rst rename to docs/manuals/characterization/t2ramsey.rst diff --git a/docs/guides/characterization/tphi.rst b/docs/manuals/characterization/tphi.rst similarity index 100% rename from docs/guides/characterization/tphi.rst rename to docs/manuals/characterization/tphi.rst diff --git a/docs/guides/index.rst b/docs/manuals/index.rst similarity index 81% rename from docs/guides/index.rst rename to docs/manuals/index.rst index ba0fa25a46..a8117496d5 100644 --- a/docs/guides/index.rst +++ b/docs/manuals/index.rst @@ -1,10 +1,11 @@ -##################### -Experiment Guides -##################### +Experiment Manuals +================== -These experiment guides are in-depth dives into individual experiments, their +These experiment manuals are in-depth dives into individual experiments, their operational principles, and how to run them in Qiskit Experiments. +.. _benchmarking: + Benchmarking Experiments ------------------------ @@ -16,6 +17,8 @@ metrics, such as the space-time volume of circuits that can be successfully exec benchmarking/* +.. _qubit characterization: + Qubit Characterization Experiments ---------------------------------- @@ -26,6 +29,8 @@ These experiment measure specific properties of a qubit. characterization/* +.. _measurement-related: + Measurement-Related Experiments ------------------------------- diff --git a/docs/guides/measurement/readout_mitigation.rst b/docs/manuals/measurement/readout_mitigation.rst similarity index 100% rename from docs/guides/measurement/readout_mitigation.rst rename to docs/manuals/measurement/readout_mitigation.rst diff --git a/docs/guides/measurement/restless_measurements.rst b/docs/manuals/measurement/restless_measurements.rst similarity index 100% rename from docs/guides/measurement/restless_measurements.rst rename to docs/manuals/measurement/restless_measurements.rst diff --git a/docs/guides/measurement/restless_shots.png b/docs/manuals/measurement/restless_shots.png similarity index 100% rename from docs/guides/measurement/restless_shots.png rename to docs/manuals/measurement/restless_shots.png diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 6d45a01af8..5b70f5c793 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -212,4 +212,4 @@ PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 See also -------- -Experiment guide: :doc:`/guides/measurement/restless_measurements` +Experiment manual: :doc:`/manuals/measurement/restless_measurements` diff --git a/docs/tutorials/images/compositeexperiments.png b/docs/tutorials/images/compositeexperiments.png index b076f9b107dd8767fa9abb12c8622051197143d3..fe1fe776ef453b8dc03a96aca311660068d8c0b4 100644 GIT binary patch literal 209682 zcmdSBWmsHGur`Woa1X%=Zh-*7B}j00w-DTQa0pIt2tk9pySoO5!QFL&JA6ZA@BN*7 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zFVwKV*}o1`*IoxOg)7Jh=IL(y*MamcTao)j?>v|JwJUS&e3POopBlWR&ja6efqvBg zmp!Cg1wJmcFBkN2j*2?|57xU82!GoWn7Q^2Td+m zmr+UO+F9N75%5I(7-Cdd3AQoAz0XumcmIcA_?JEZbv*wBC4Lg+Kia{6^Zbigy1}2A aTPeFA2g7Y_zH9;hUD3RDDfi-?hyMp`1N2P* diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 421da6592d..310c4fdfea 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -1,6 +1,5 @@ -######### Tutorials -######### +========= These tutorials assume some familiarity with Qiskit (on the level of the :external+qiskit:doc:`introductory tutorials `) but no knowledge of Qiskit Experiments. @@ -9,7 +8,7 @@ They're suitable for beginners who want to get started with the package. .. _basics: The Basics -========== +---------- .. toctree:: :maxdepth: 2 @@ -17,8 +16,8 @@ The Basics intro getting_started -Exploring modules -================= +Exploring Modules +----------------- .. toctree:: :maxdepth: 1 @@ -28,8 +27,8 @@ Exploring modules curve_analysis visualization -Customizing experiments -======================= +Customizing Experiments +----------------------- .. toctree:: :maxdepth: 1 diff --git a/qiskit_experiments/framework/__init__.py b/qiskit_experiments/framework/__init__.py index 98c9cf7367..e25b1db496 100644 --- a/qiskit_experiments/framework/__init__.py +++ b/qiskit_experiments/framework/__init__.py @@ -124,7 +124,6 @@ BackendTiming RestlessMixin -.. _create-experiment: """ from qiskit.providers.options import Options from qiskit_experiments.framework.backend_data import BackendData diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py index c9cdf1363b..aa95365056 100644 --- a/qiskit_experiments/library/calibration/rough_drag_cal.py +++ b/qiskit_experiments/library/calibration/rough_drag_cal.py @@ -33,7 +33,7 @@ class RoughDragCal(BaseCalibrationExperiment, RoughDrag): # section: see_also :class:`.RoughDrag` - # section: guide + # section: manual :ref:`DRAG Calibration` """ diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py index c07516bda6..d46e84232b 100644 --- a/qiskit_experiments/library/characterization/cr_hamiltonian.py +++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py @@ -122,7 +122,7 @@ class CrossResonanceHamiltonian(BaseExperiment): # section: reference .. ref_arxiv:: 1 1603.04821 - # section: guide + # section: manual .. ref_website:: Qiskit Textbook 6.7, https://qiskit.org/textbook/ch-quantum-hardware/hamiltonian-tomography.html """ diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index 7f4b60d709..be34588f24 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -70,7 +70,7 @@ class RoughDrag(BaseExperiment, RestlessMixin): .. ref_arxiv:: 2 0901.0534 .. ref_arxiv:: 3 1509.05470 - # section: guide + # section: manual :ref:`DRAG Calibration` """ diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index 668d48c287..dd25cd0347 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -85,7 +85,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): # section: reference .. ref_arxiv:: 1 1504.06597 - # section: guide + # section: manual :ref:`fine-amplitude-cal` """ diff --git a/qiskit_experiments/library/characterization/rabi.py b/qiskit_experiments/library/characterization/rabi.py index 13ac8e0aff..d3cd9ceb6e 100644 --- a/qiskit_experiments/library/characterization/rabi.py +++ b/qiskit_experiments/library/characterization/rabi.py @@ -47,7 +47,7 @@ class Rabi(BaseExperiment, RestlessMixin): The user provides his own schedule for the Rabi at initialization which must have one free parameter, i.e. the amplitude to scan and a drive channel which matches the qubit. - # section: guide + # section: manual :ref:`Rabi Calibration` See also `Qiskit Textbook Date: Thu, 9 Mar 2023 22:06:48 -0500 Subject: [PATCH 56/64] update jinja template updated logic so that the attribute section is only rendered if there are valid elements --- docs/_templates/autosummary/class.rst | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst index cb865ca173..cc5a9b736c 100644 --- a/docs/_templates/autosummary/class.rst +++ b/docs/_templates/autosummary/class.rst @@ -15,13 +15,21 @@ {% block attributes_summary %} {% if attributes %} + {% set count = namespace(value=0) %} + + {% for item in attributes %} + {% if not item.startswith('_') %} + {% set count.value = count.value + 1 %} + {% if count.value == 1 %} .. rubric:: Attributes .. autosummary:: :toctree: ../stubs/ - {% for item in all_attributes %} - .. autoattribute:: {{ item }} - {%- endfor %} + {% endif %} + + {{ name }}.{{ item }} + {% endif %} + {% endfor %} {% endif %} {% endblock %} From 49d3016944efabd29b391bc950cf2a4cfefef574 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 9 Mar 2023 22:48:32 -0500 Subject: [PATCH 57/64] skipped members throwing warnings Linux builds don't like to see class variables, and they're already documented via the docstring so we can just skip them --- docs/conf.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 11e01a99f9..2c4895d7bd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -238,7 +238,34 @@ def setup(app): app.connect("autodoc-skip-member", maybe_skip_member) +from qiskit_experiments.curve_analysis import ParameterRepr +from qiskit_experiments.curve_analysis import SeriesDef +from qiskit_experiments.curve_analysis import CurveData + + def maybe_skip_member(app, what, name, obj, skip, options): + skip_names = [ + "analysis", + "set_run_options", + "data_allocation", + "labels", + "shots", + "x", + "y", + "y_err", + "name", + "filter_kwargs", + "fit_func", + "signature", + ] + skip_members = [ + ParameterRepr.repr, + ParameterRepr.unit, + SeriesDef.plot_color, + SeriesDef.plot_symbol, + SeriesDef.model_description, + SeriesDef.canvas, + ] if not skip: - return name in {"analysis", "set_run_options"} and what == "attribute" + return (name in skip_names or obj in skip_members) and what == "attribute" return skip From edb8ef8cebcacdbbfc626293555ae75e77e9b8c1 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 9 Mar 2023 23:35:26 -0500 Subject: [PATCH 58/64] fixed user manual section in docstrings --- docs/_ext/custom_styles/formatter.py | 6 +++--- docs/_ext/custom_styles/styles.py | 2 +- docs/index.rst | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index ca606f0522..03665b49d7 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -90,9 +90,9 @@ def format_see_also(self, lines: List[str]) -> List[str]: return format_lines @_check_no_indent - def format_guide(self, lines: List[str]) -> List[str]: - """Format user guide section.""" - format_lines = [".. rubric:: User Guide", ""] + def format_manual(self, lines: List[str]) -> List[str]: + """Format user manual section.""" + format_lines = [".. rubric:: User Manual", ""] format_lines.extend(lines) format_lines.append("") diff --git a/docs/_ext/custom_styles/styles.py b/docs/_ext/custom_styles/styles.py index d411965881..ae9c3b47e5 100644 --- a/docs/_ext/custom_styles/styles.py +++ b/docs/_ext/custom_styles/styles.py @@ -322,7 +322,7 @@ class VisualizationDocstring(QiskitExperimentDocstring): "warning": load_standard_section, "overview": load_standard_section, "reference": load_standard_section, - "guide": load_standard_section, + "manual": load_standard_section, "opts": None, # For standard options "figure_opts": None, # For figure options "example": load_standard_section, diff --git a/docs/index.rst b/docs/index.rst index 2807a7aaae..e3b0686a9b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -119,8 +119,8 @@ We've divided up the documentation into four sections with different purposes: manuals/index apidocs/index release_notes - Development Branch Docs GitHub + Development Branch Docs | From e1ea74c1d9761f6b5be26cf71bcc31085b8722d0 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Fri, 10 Mar 2023 21:43:28 -0500 Subject: [PATCH 59/64] turned off multiprocessing in default tox env Because of issues on mac, `tox -edocs` doesn't have multiprocessing anymore. the `docs-parallel` env has been added for CI and Linux builders. `docsnorst` renamed to `docs-minimal`. The class template for methods has been updated like attributes so the header doesn't render unless there are valid members. --- .github/workflows/main.yml | 2 +- CONTRIBUTING.md | 8 +- docs/GUIDELINES.md | 40 ++++--- docs/_ext/jupyter_execute_custom.py | 4 +- docs/_templates/autosummary/class.rst | 18 ++- docs/conf.py | 42 +++---- docs/howtos/cloud_service.rst | 8 +- docs/manuals/characterization/t1.rst | 6 +- docs/tutorials/curve_analysis.rst | 105 +++++++++--------- .../curve_analysis/curve_analysis.py | 2 +- tox.ini | 11 +- 11 files changed, 123 insertions(+), 123 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index f4c3907a27..4761fece4c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -97,7 +97,7 @@ jobs: python -m pip install -U tox sudo apt-get install -y pandoc graphviz - name: Build Docs - run: tox -edocs -- -W + run: tox -edocs-parallel -- -W - name: Compress Artifacts run: | mkdir artifacts diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a07f57051a..e15a04e054 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -335,12 +335,10 @@ If you encounter a build error involving `config-inited`, you need to be in the the qiskit-experiments git repository then run `git remote add upstream https://github.com/Qiskit/qiskit-experiments` and `git fetch upstream` before building. -To build release notes and API docs without executing the Jupyter cells in the `.rst` -files, which is a relatively slow process, you can run +There are a few other build options available: - tox -edocsnorst - -instead. +* `tox -edocs-minimal`: build documentation without executing Jupyter code cells +* `tox -edocs-parallel`: do a full build with multiprocessing (may crash on Macs) ### Adding deprecation warnings diff --git a/docs/GUIDELINES.md b/docs/GUIDELINES.md index 861b92a6ea..8ed4ba4a45 100644 --- a/docs/GUIDELINES.md +++ b/docs/GUIDELINES.md @@ -68,11 +68,15 @@ The learning tutorials are for users who are familiar with Python and Qiskit and the Qiskit Experiments package. Here are what to keep in mind when writing and updating tutorials: +- The tutorials should be suitable for progressive learning, starting with simple + instructions and gradually adding complexity. For example, T1 is a much better + starting experiment than cross resonance hamiltonian tomography. Each new bit of + added complexity that the user hasn't seen before should be explained. - Whenever possible, external resources should be linked to. For example, classes and - methods in Qiskit should be linked + methods in Qiskit should be linked. - If you make changes to the basic API shown in the tutorials, it's important to update - them. Consider adding a special note for major recent changes to inform users who may - be used to the old usage pattern. + the corresponding part in the tutorials. Consider adding a special note for major + recent changes to inform users who may be used to the old usage pattern. ### How-to guides @@ -94,16 +98,16 @@ first two are required: - See also: Links to other relevant documentation or resources. -Here is a template for how-tos: +Here is a template for how-to guides: ``` -Write a how-to -=============== +Write a how-to guide +==================== Problem ------- -You want to write a how-to. +You want to write a how-to guide. Solution -------- @@ -136,20 +140,20 @@ See also The main goal of `qiskit-experiment` experiment manuals is to serve as user manuals for the various package components such as the characterization and calibration experiments. -To this end, each guide should introduce the cover the main (if not all) use-cases of -the documented functionality, including code examples and expected outputs. Another +To this end, each document should introduce the cover the main (if not all) use cases of +the experiment functionality, including code examples and expected outputs. Another objective of the documentation is to provide the user with basic background on each -experiment method. Hence a good practice would be to have in the beginning of the -documentation a short background explanation, preferably 1 or 2 paragraphs long which -includes the main literature references as well as a link to the relevant chapter in the -Qiskit textbook, if available. The common use cases of the experiment should be covered -with a code example and example outputs by printing relevant analysis results and plot -figures. Required and common parameters, such as experiment and analysis options, shoud -be covered. +experiment method. The start of the manual should have a short background explanation +for what the experiment does, preferably 1 or 2 paragraphs long, which includes the main +literature references as well as a link to the relevant chapter in the Qiskit textbook, +if available. The common use cases of the experiment should be covered with a code +example and example outputs by printing relevant analysis results and plot figures. +Required and common parameters, such as experiment and analysis options, should be +covered. See the [Randomized Benchmarking](https://qiskit.org/documentation/experiments/manuals/benchmarking/randomized_benchmarking.html) guide and its [source code](docs/manuals/benchmarking/randomized_benchmarking.rst) for an -example. Here is a simple template for a guide: +example. Here is a simple template for a manual: ``` New Experiment @@ -160,7 +164,7 @@ ideally provided to the level of someone who has taken a background course in qu computing. References are provided to the original paper where the experiment was described, if relevant, and to good resources for understanding it. -Running new experiment +Running the experiment ---------------------- Here caveats about the specific implementation of the experiment in this package are diff --git a/docs/_ext/jupyter_execute_custom.py b/docs/_ext/jupyter_execute_custom.py index 8c8b3d40f0..2bc463f0c0 100644 --- a/docs/_ext/jupyter_execute_custom.py +++ b/docs/_ext/jupyter_execute_custom.py @@ -20,12 +20,12 @@ class JupyterCellCheckEnv(JupyterCell): """This class overrides the JupyterCell class in :mod:`jupyter-sphinx` - to skip cell execution when `QISKIT_DOCS_SKIP_RST` is true in the environment. + to skip cell execution when `QISKIT_DOCS_SKIP_EXECUTE` is true in the environment. """ def run(self): [cell] = super().run() - if os.getenv("QISKIT_DOCS_SKIP_RST", False): + if os.getenv("QISKIT_DOCS_SKIP_EXECUTE", False): cell["execute"] = False cell["hide_code"] = False return [cell] diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst index cc5a9b736c..f6e8a53f58 100644 --- a/docs/_templates/autosummary/class.rst +++ b/docs/_templates/autosummary/class.rst @@ -15,6 +15,8 @@ {% block attributes_summary %} {% if attributes %} + {# This counter lets us only render the heading if there's at least + one valid entry. #} {% set count = namespace(value=0) %} {% for item in attributes %} @@ -36,17 +38,29 @@ {% block methods_summary %} {% if methods %} + {% set count = namespace(value=0) %} + {% for item in all_methods %} + + {%- if not item.startswith('_') or item in ['__call__', '__mul__', '__getitem__', '__len__'] %} + {% set count.value = count.value + 1 %} + {% if count.value == 1 %} .. rubric:: Methods .. autosummary:: :toctree: ../stubs/ - {% for item in all_methods %} - {%- if not item.startswith('_') or item in ['__call__', '__mul__', '__getitem__', '__len__'] %} + {% endif %} {{ name }}.{{ item }} {%- endif -%} {%- endfor %} {% for item in inherited_members %} {%- if item in ['__call__', '__mul__', '__getitem__', '__len__'] %} + {% set count.value = count.value + 1 %} + {% if count.value == 1 %} + .. rubric:: Methods + + .. autosummary:: + :toctree: ../stubs/ + {% endif %} {{ name }}.{{ item }} {%- endif -%} {%- endfor %} diff --git a/docs/conf.py b/docs/conf.py index 2c4895d7bd..c2008e9a61 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,38 +17,39 @@ # full list see the documentation: # http://www.sphinx-doc.org/en/master/config +""" +Sphinx documentation builder. +""" + +import os +import sys +import subprocess +import datetime + # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # -import os -import sys -import subprocess sys.path.insert(0, os.path.abspath(".")) sys.path.append(os.path.abspath("./_ext")) sys.path.append(os.path.abspath("..")) - -""" -Sphinx documentation builder -""" - # Set env flag so that we can doc functions that may otherwise not be loaded # see for example interactive visualizations in qiskit.visualization. os.environ["QISKIT_DOCS"] = "TRUE" # -- Project information ----------------------------------------------------- -project = "Qiskit Experiments" -copyright = "2021, Qiskit Development Team" # pylint: disable=redefined-builtin -author = "Qiskit Development Team" - # The short X.Y version version = "0.5" # The full version, including alpha/beta/rc tags release = "0.5.0" +project = f"Qiskit Experiments {version}" +copyright = f"2021-{datetime.date.today().year}, Qiskit Development Team" # pylint: disable=redefined-builtin +author = "Qiskit Development Team" + # -- General configuration --------------------------------------------------- @@ -108,19 +109,10 @@ # visualization module and `default_style` method in `PlotStyle` respectively. napoleon_custom_sections = [("data keys", "params_style"), ("style parameters", "params_style")] -# ----------------------------------------------------------------------------- -# Autosummary -# ----------------------------------------------------------------------------- - autosummary_generate = True -# ----------------------------------------------------------------------------- -# Autodoc -# ----------------------------------------------------------------------------- - autodoc_default_options = {"inherited-members": None} - # If true, figures, tables and code-blocks are automatically numbered if they # have a caption. numfig = True @@ -189,10 +181,6 @@ } -# Current scipy hosted docs are missing the object.inv file so leaving this -# commented out until the missing file is added back. -# 'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None)} - # Prepend warning for development docs: if os.getenv("EXPERIMENTS_DEV_DOCS", None): @@ -238,9 +226,11 @@ def setup(app): app.connect("autodoc-skip-member", maybe_skip_member) +# Hardcoded list of class variables to skip in autodoc to avoid warnings +# Should come up with better way to address this + from qiskit_experiments.curve_analysis import ParameterRepr from qiskit_experiments.curve_analysis import SeriesDef -from qiskit_experiments.curve_analysis import CurveData def maybe_skip_member(app, what, name, obj, skip, options): diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index 1b3a5019e1..ec9a68f093 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -53,9 +53,7 @@ Let's load a `previous T1 experiment `__ (requires login to view), which we've made public by editing the ``Share level`` field: -.. jupyter-execute:: - :hide-output: - :raises: +.. jupyter-input:: from qiskit_experiments.framework.experiment_data import ExperimentData service = ExperimentData.get_service_from_backend(backend) @@ -64,9 +62,7 @@ experiment after time :math:`t` is :math:`e^{-t/T_1}`, +probability to measure :math:`|1\rangle` after time :math:`t` is :math:`e^{-t/T_1}`, for a constant :math:`T_1` (the coherence time), which is our target number. Since state preparation and measurement errors do exist, the qubit’s decay towards the ground state assumes the form :math:`Ae^{-t/T_1} + B`, for parameters :math:`A, T_1`, and :math:`B`, -which we deduce form the probability estimates. To this end, the -:math:`T_1` experiment internally calls the ``curve_fit`` method of -``scipy.optimize``. +which we deduce from the probability estimates. The following code demonstrates a basic run of a :math:`T_1` experiment for qubit 0. diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 2ff337fbe1..3cfc82bd4d 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -15,7 +15,7 @@ analyses that inherits from the base class. .. _curve_analysis_overview: -Curve analysis overview +Curve Analysis overview ----------------------- The base class :class:`.CurveAnalysis` implements the multi-objective optimization on @@ -81,10 +81,11 @@ Since series A and B share the parameters in this example, :math:`\Theta_{\rm fi and the fixed parameters are :math:`\Theta_{\rm fix} = \{ p_1 \}` as mentioned. Thus, :math:`\Theta = \{ p_0, p_1, p_2 \}`. -Experiment for each series can perform individual parameter sweep for :math:`x_A` and :math:`x_B`, -and experiment data yield outcomes :math:`y_A` and :math:`y_B`, which might be different size. -Data processing function may also compute :math:`\sigma_A` and :math:`\sigma_B` which are -the uncertainty of outcomes arising from the sampling error or measurement error. +Experiment for each series can perform individual parameter sweep for :math:`x_A` and +:math:`x_B`, and experiment data yield outcomes :math:`y_A` and :math:`y_B`, which might +be different size. Data processing function may also compute :math:`\sigma_A` and +:math:`\sigma_B` which are the uncertainty of outcomes arising from the sampling error +or measurement error. More specifically, the curve analysis defines following data model. @@ -107,16 +108,15 @@ the analysis code for a particular experiment. .. _curve_analysis_define_group: -Defining New Group ------------------- +Defining new models +------------------- -The fit model is defined by the `LMFIT`_ ``Model``. -If you are familiar with this package, you can skip this section. -The LMFIT package manages complicated fit function and offers several algorithms -to solve non-linear least-square problems. -Basically the Qiskit curve analysis delegates the core fitting functionality to this package. +The fit model is defined by the `LMFIT`_ ``Model``. If you are familiar with this +package, you can skip this section. The LMFIT package manages complicated fit function +and offers several algorithms to solve non-linear least-square problems. Curve Analysis +delegates the core fitting functionality to this package. -You can intuitively write the definition of model, as shown below: +You can intuitively write the definition of a model, as shown below: .. jupyter-input:: @@ -208,13 +208,12 @@ with different trigonometric functions. .. _curve_analysis_fixed_param: -Fitting with Fixed Parameters +Fitting with fixed parameters ----------------------------- -You can also remain certain parameters unchanged during the fitting by specifying -the parameter names in the analysis option ``fixed_parameters``. -This feature is useful especially when you want to define a subclass of -a particular analysis class. +You can also keep certain parameters unchanged during the fitting by specifying the +parameter names in the analysis option ``fixed_parameters``. This feature is useful +especially when you want to define a subclass of a particular analysis class. .. jupyter-input:: @@ -260,7 +259,7 @@ every logic defined in the :class:`AnalysisA`. .. _curve_analysis_workflow: -Curve Analysis Workflow +Curve Analysis workflow ----------------------- Typically curve analysis performs fitting as follows. @@ -318,33 +317,28 @@ Finally, it returns the list of created analysis results and Matplotlib figure. .. _curve_analysis_init_guess: -Providing Initial Guesses +Providing initial guesses ------------------------- -When fit is performed without any prior information of parameters, it usually -falls into unsatisfactory result. -User can provide initial guesses and boundaries for the fit parameters -through analysis options ``p0`` and ``bounds``. -These values are the dictionary keyed on the parameter name, -and one can get the list of parameters with the :attr:`CurveAnalysis.parameters`. -Each boundary value can be a tuple of float representing min and max value. - -Apart from user provided guesses, the analysis can systematically generate -those values with the method :meth:`_generate_fit_guesses` which is called with -:class:`CurveData` dataclass. If the analysis contains multiple models definitions, -we can get the subset of curve data with :meth:`CurveData.get_subset_of` with -the name of the series. -A developer can implement the algorithm to generate initial guesses and boundaries -by using this curve data object, which will be provided to the fitter. -Note that there are several common initial guess estimators available in -:mod:`qiskit_experiments.curve_analysis.guess`. - -The :meth:`_generate_fit_guesses` also receives :class:`.FitOptions` instance ``user_opt``, -which contains user provided guesses and boundaries. -This is dictionary-like object consisting of sub-dictionaries for -initial guess ``.p0``, boundary ``.bounds``, and extra options for the fitter. -Note that :class:`.CurveAnalysis` uses SciPy `curve_fit`_ as the least square solver. -See the API documentation for available options. +Fitting without initial guesses for parameters often results in a bad fit. User can +provide initial guesses and boundaries for the fit parameters through analysis options +``p0`` and ``bounds``. These values are the dictionary keyed on the parameter name, and +one can get the list of parameters with the :attr:`CurveAnalysis.parameters`. Each +boundary value can be a tuple of float representing min and max value. + +Apart from user provided guesses, the analysis can systematically generate those values +with the method :meth:`_generate_fit_guesses` which is called with :class:`CurveData` +dataclass. If the analysis contains multiple models definitions, we can get the subset +of curve data with :meth:`.CurveData.get_subset_of` with the name of the series. A +developer can implement the algorithm to generate initial guesses and boundaries by +using this curve data object, which will be provided to the fitter. Note that there are +several common initial guess estimators available in :mod:`curve_analysis.guess`. + +The :meth:`_generate_fit_guesses` also receives the :class:`.FitOptions` instance +``user_opt``, which contains user provided guesses and boundaries. This is +dictionary-like object consisting of sub-dictionaries for initial guess ``.p0``, +boundary ``.bounds``, and extra options for the fitter. See the API +documentation for available options. The :class:`.FitOptions` class implements convenient method :meth:`set_if_empty` to manage conflict with user provided values, i.e. user provided values have higher priority, @@ -364,17 +358,13 @@ thus systematically generated values cannot override user values. return [opt1, opt2] -Here you created two options with different ``p1`` values. -If multiple options are returned like this, the :meth:`_run_curve_fit` method -attempts to fit with all provided options and finds the best outcome with -the minimum reduced chi-square value. -When the fit model contains some parameter that cannot be easily estimated from the -curve data, you can create multiple options with varying the initial guess to -let the fitter find the most reasonable parameters to explain the model. -This allows you to avoid analysis failure with the poor initial guesses. - -.. _curve_fit: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html - +Here you created two options with different ``p1`` values. If multiple options are +returned like this, the :meth:`_run_curve_fit` method attempts to fit with all provided +options and finds the best outcome with the minimum reduced chi-square value. When the +fit model contains some parameter that cannot be easily estimated from the curve data, +you can create multiple options with varying the initial guess to let the fitter find +the most reasonable parameters to explain the model. This allows you to avoid analysis +failure with the poor initial guesses. .. _curve_analysis_quality: @@ -446,3 +436,8 @@ Since this object natively supports error propagation, you don't need to manually recompute the error of new value. .. _ufloat: https://pythonhosted.org/uncertainties/user_guide.html + +See also +-------- + +API documentation: :doc:`Curve Analysis Module ` \ No newline at end of file diff --git a/qiskit_experiments/curve_analysis/curve_analysis.py b/qiskit_experiments/curve_analysis/curve_analysis.py index d04d4fc4a1..bce1fc1866 100644 --- a/qiskit_experiments/curve_analysis/curve_analysis.py +++ b/qiskit_experiments/curve_analysis/curve_analysis.py @@ -41,7 +41,7 @@ class CurveAnalysis(BaseCurveAnalysis): .. rubric:: _run_data_processing This method performs data processing and returns the processed dataset. - By default, it internally calls the :class:`DataProcessor` instance from + By default, it internally calls the :class:`.DataProcessor` instance from the `data_processor` analysis option and processes the experiment data payload to create Y data with uncertainty. X data and other metadata are generated within this method by inspecting the diff --git a/tox.ini b/tox.ini index 331e325721..f499f46cba 100644 --- a/tox.ini +++ b/tox.ini @@ -57,15 +57,20 @@ commands = black {posargs} qiskit_experiments test tools setup.py [testenv:docs] passenv = EXPERIMENTS_DEV_DOCS +commands = + sphinx-build -T --keep-going -b html {posargs} docs/ docs/_build/html + +[testenv:docs-parallel] +passenv = EXPERIMENTS_DEV_DOCS commands = sphinx-build -j auto -T --keep-going -b html {posargs} docs/ docs/_build/html -[testenv:docsnorst] +[testenv:docs-minimal] passenv = EXPERIMENTS_DEV_DOCS setenv = - QISKIT_DOCS_SKIP_RST = 1 + QISKIT_DOCS_SKIP_EXECUTE = 1 commands = - sphinx-build -j auto -T --keep-going -b html {posargs} docs/ docs/_build/html + sphinx-build -T --keep-going -b html {posargs} docs/ docs/_build/html [pycodestyle] max-line-length = 100 From c2292acdcdc491eb5487dd4d18817a2c19c8e882 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Mar 2023 14:23:53 -0400 Subject: [PATCH 60/64] Merge main, update theme, and format footnotes --- CONTRIBUTING.md | 3 + docs/conf.py | 2 +- docs/manuals/benchmarking/quantum_volume.rst | 6 +- .../benchmarking/randomized_benchmarking.rst | 102 ++++---------- .../manuals/benchmarking/state_tomography.rst | 10 +- docs/manuals/characterization/t2ramsey.rst | 6 +- docs/manuals/characterization/tphi.rst | 79 ++++++++--- .../measurement/restless_measurements.rst | 38 ++--- docs/tutorials/data_processor.rst | 48 +++---- .../framework/experiment_data.py | 6 +- .../analysis/tphi_analysis.py | 38 +++-- .../library/characterization/tphi.py | 133 ++++++++++++------ .../notes/tphi-option-025f02c2c843c74f.yaml | 8 ++ requirements-dev.txt | 3 +- test/library/characterization/test_tphi.py | 58 ++++++-- 15 files changed, 313 insertions(+), 227 deletions(-) create mode 100644 releasenotes/notes/tphi-option-025f02c2c843c74f.yaml diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e15a04e054..60c55f3438 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -334,6 +334,9 @@ manually: If you encounter a build error involving `config-inited`, you need to be in the root of the qiskit-experiments git repository then run `git remote add upstream https://github.com/Qiskit/qiskit-experiments` and `git fetch upstream` before building. +Trying to rebuild docs over a document tree that's changed can also lead to problems; +in this case, you should delete the `docs/stubs` and `docs/_build` directories before +rebuilding. There are a few other build options available: diff --git a/docs/conf.py b/docs/conf.py index c2008e9a61..1085b52310 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -101,7 +101,7 @@ "manuals/benchmarking/state_tomography": "_images/state_tomography_3_0.png", "manuals/characterization/t1": "_images/t1_0_0.png", "manuals/characterization/t2ramsey": "_images/t2ramsey_4_0.png", - "manuals/characterization/tphi": "_images/tphi_8_0.png", + "manuals/characterization/tphi": "_images/tphi_5_1.png", "manuals/characterization/t2hahn": "_images/t2hahn_5_0.png", } diff --git a/docs/manuals/benchmarking/quantum_volume.rst b/docs/manuals/benchmarking/quantum_volume.rst index af20924c7d..cc9f0e02b3 100644 --- a/docs/manuals/benchmarking/quantum_volume.rst +++ b/docs/manuals/benchmarking/quantum_volume.rst @@ -9,7 +9,7 @@ with high-fidelity operations, high connectivity, large calibrated gate sets, and circuit rewriting toolchains are expected to have higher quantum volumes. See the `Qiskit Textbook `__ -for an explanation on the QV method, which is described in Ref. [1, 2]. +for an explanation on the QV method, which is described in Refs. [1]_ [2]_. The Quantum Volume is determined by the largest successful circuit depth :math:`d_{max}`, and equals to :math:`2^{d_{max}}`. In the QV experiment @@ -174,12 +174,12 @@ Extracting the maximum Quantum Volume. References ---------- -[1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and +.. [1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and Jay M. Gambetta, Validating quantum computers using randomized model circuits, Phys. Rev. A 100, 032328 (2019). https://arxiv.org/pdf/1811.12926 -[2] Petar Jurcevic et. al. Demonstration of quantum volume 64 on +.. [2] Petar Jurcevic et. al. Demonstration of quantum volume 64 on a superconducting quantum computing system, https://arxiv.org/pdf/2008.08571 diff --git a/docs/manuals/benchmarking/randomized_benchmarking.rst b/docs/manuals/benchmarking/randomized_benchmarking.rst index 4616378e50..6d8e61083c 100644 --- a/docs/manuals/benchmarking/randomized_benchmarking.rst +++ b/docs/manuals/benchmarking/randomized_benchmarking.rst @@ -9,7 +9,7 @@ output different from the ground state) are counted, and from this data one can error estimates for the quantum device, by calculating the Error Per Clifford. See the `Qiskit Textbook `__ for an -explanation on the RB method, which is based on Ref. [1, 2]. +explanation on the RB method, which is based on Refs. [1]_ [2]_. .. jupyter-execute:: @@ -54,16 +54,17 @@ The analysis results of the RB Experiment may include: :math:`a \cdot \alpha^m + b`, where :math:`m` is the Clifford length - ``EPG``: The Error Per Gate calculated from the EPC, only for 1-qubit - or 2-qubit quantum gates (see Ref. [3]) + or 2-qubit quantum gates (see [3]_) Running a 1-qubit RB experiment ------------------------------- -Standard RB experiment will provide you gate errors for every basis gates -constituting averaged Clifford gate. Note that you can only obtain a single EPC value :math:`\cal E` -from a single RB experiment. As such, computing the error values for multiple gates :math:`\{g_i\}` -requires some assumption of contribution of each gate to the total depolarizing error. -This is the so called ``gate_error_ratio`` option you can find in analysis options. +The standard RB experiment will provide you gate errors for every basis gate +constituting an averaged Clifford gate. Note that you can only obtain a single EPC value +:math:`\cal E` from a single RB experiment. As such, computing the error values for +multiple gates :math:`\{g_i\}` requires some assumption of contribution of each gate to +the total depolarizing error. This is provided by the ``gate_error_ratio`` analysis +option. Provided that we have :math:`n_i` gates with independent error :math:`e_i` per Clifford, the total EPC is estimated by the composition of error from every basis gate, @@ -121,7 +122,7 @@ However, the EPC value obtained by the experiment indicates a depolarization which is a composition of underlying error channels for 2Q gates and 1Q gates in each qubit. Usually 1Q gate contribution is small enough to ignore, but in case this contribution is significant comparing to the 2Q gate error, -we can decompose the contribution of 1Q gates [3]. +we can decompose the contribution of 1Q gates [3]_. .. math:: @@ -227,9 +228,8 @@ We transpile the circuit into the backend’s basis gate set: Interleaved RB experiment ------------------------- -Interleaved RB experiment is used to estimate the gate error of the -interleaved gate (see Ref. [4]). In addition to the usual RB parameters, we also -need to provide: +The interleaved RB experiment is used to estimate the gate error of the interleaved gate +(see [4]_). In addition to the usual RB parameters, we also need to provide: - ``interleaved_element``: the element to interleave, given either as a group element or as an instruction/circuit @@ -244,10 +244,10 @@ The analysis results of the RB Experiment includes the following: Extra analysis results include - ``EPC_systematic_err``: The systematic error of the interleaved gate - error (see Ref. [4]) + error [4]_ - ``EPC_systematic_bounds``: The systematic error bounds of the - interleaved gate error (see Ref. [4]) + interleaved gate error [4]_ Let's run an interleaved RB experiment on two qubits: @@ -275,70 +275,26 @@ Let's run an interleaved RB experiment on two qubits: print(result) +References +---------- -Running a simultaneous RB experiment ------------------------------------- - -We use ``ParallelExperiment`` to run the RB experiment simultaneously on -different qubits (see Ref. [5]) - -.. jupyter-execute:: - - lengths = np.arange(1, 800, 200) - num_samples = 10 - seed = 1010 - qubits = range(3) - - # Run a parallel 1-qubit RB experiment on qubits 0, 1, 2 - exps = [StandardRB([i], lengths, num_samples=num_samples, seed=seed + i) - for i in qubits] - par_exp = ParallelExperiment(exps) - par_expdata = par_exp.run(backend).block_for_results() - par_results = par_expdata.analysis_results() - - -Viewing sub experiment data -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The experiment data returned from a batched experiment also contains -individual experiment data for each sub experiment which can be accessed -using ``child_data`` - -.. jupyter-execute:: +.. [1] Easwar Magesan, J. M. Gambetta, and Joseph Emerson, *Robust + randomized benchmarking of quantum processes*, + https://arxiv.org/abs/1009.3639. - # Print sub-experiment data - for i in qubits: - print(f"Component experiment {i}") - display(par_expdata.child_data(i).figure(0)) - for result in par_expdata.child_data(i).analysis_results(): - print(result) +.. [2] Easwar Magesan, Jay M. Gambetta, and Joseph Emerson, *Characterizing + Quantum Gates via Randomized Benchmarking*, + https://arxiv.org/abs/1109.6887. -References ----------- +.. [3] David C. McKay, Sarah Sheldon, John A. Smolin, Jerry M. Chow, and + Jay M. Gambetta, *Three Qubit Randomized Benchmarking*, + https://arxiv.org/abs/1712.06550. -[1] Easwar Magesan, J. M. Gambetta, and Joseph Emerson, *Robust -randomized benchmarking of quantum processes*, -https://arxiv.org/pdf/1009.3639 - -[2] Easwar Magesan, Jay M. Gambetta, and Joseph Emerson, *Characterizing -Quantum Gates via Randomized Benchmarking*, -https://arxiv.org/pdf/1109.6887 - -[3] David C. McKay, Sarah Sheldon, John A. Smolin, Jerry M. Chow, and -Jay M. Gambetta, *Three Qubit Randomized Benchmarking*, -https://arxiv.org/pdf/1712.06550 - -[4] Easwar Magesan, Jay M. Gambetta, B. R. Johnson, Colm A. Ryan, Jerry -M. Chow, Seth T. Merkel, Marcus P. da Silva, George A. Keefe, Mary B. -Rothwell, Thomas A. Ohki, Mark B. Ketchen, M. Steffen, *Efficient -measurement of quantum gate error by interleaved randomized -benchmarking*, https://arxiv.org/pdf/1203.4550 - -[5] Jay M. Gambetta, A. D. Córcoles, S. T. Merkel, B. R. Johnson, John -A. Smolin, Jerry M. Chow, Colm A. Ryan, Chad Rigetti, S. Poletto, Thomas -A. Ohki, Mark B. Ketchen, and M. Steffen, *Characterization of -addressability by simultaneous randomized benchmarking*, -https://arxiv.org/pdf/1204.6308 +.. [4] Easwar Magesan, Jay M. Gambetta, B. R. Johnson, Colm A. Ryan, Jerry + M. Chow, Seth T. Merkel, Marcus P. da Silva, George A. Keefe, Mary B. + Rothwell, Thomas A. Ohki, Mark B. Ketchen, M. Steffen, *Efficient + measurement of quantum gate error by interleaved randomized + benchmarking*, https://arxiv.org/abs/1203.4550. See also -------- diff --git a/docs/manuals/benchmarking/state_tomography.rst b/docs/manuals/benchmarking/state_tomography.rst index 0b86616258..549ec623b5 100644 --- a/docs/manuals/benchmarking/state_tomography.rst +++ b/docs/manuals/benchmarking/state_tomography.rst @@ -120,9 +120,7 @@ The default fitters is ``linear_inversion``, which reconstructs the state using *dual basis* of the tomography basis. This will typically result in a non-positive reconstructed state. This state is rescaled to be positive-semidefinite (PSD) by computing its eigen-decomposition and -rescaling its eigenvalues using the approach from \*J Smolin, JM -Gambetta, G Smith, Phys. Rev. Lett. 108, 070502 (2012), `open -access `__. +rescaling its eigenvalues using the approach from Ref. [1]_. There are several other fitters are included (See API documentation for details). For example, if ``cvxpy`` is installed we can use the @@ -186,6 +184,12 @@ View component experiment analysis results: print("State Fidelity: {:.5f}".format(fid_result_i.value)) print("State: {}".format(state_result_i.value)) +References +---------- + +.. [1] J Smolin, JM Gambetta, G Smith, Phys. Rev. Lett. 108, 070502 (2012), + `open access `__. + See also -------- diff --git a/docs/manuals/characterization/t2ramsey.rst b/docs/manuals/characterization/t2ramsey.rst index 88529c4b0a..65e1cd2026 100644 --- a/docs/manuals/characterization/t2ramsey.rst +++ b/docs/manuals/characterization/t2ramsey.rst @@ -1,7 +1,7 @@ -T2 Ramsey Characterization -========================== +T2* Ramsey Characterization +=========================== -The purpose of the :math:`T_2` Ramsey experiment is to determine two of the qubit's +The purpose of the :math:`T_2^*` Ramsey experiment is to determine two of the qubit's properties: *Ramsey* or *detuning frequency* and :math:`T_2^\ast`. In this experiment, we would like to get a more precise estimate of the qubit's frequency given a rough estimate. The difference between the frequency used for the control rotation pulses and diff --git a/docs/manuals/characterization/tphi.rst b/docs/manuals/characterization/tphi.rst index 5ace9ff5f6..93d8cad789 100644 --- a/docs/manuals/characterization/tphi.rst +++ b/docs/manuals/characterization/tphi.rst @@ -1,23 +1,30 @@ Tφ Characterization =================== -:math:`\Gamma_\varphi` is defined as the rate of pure dephasing or depolarization in the -:math:`x - y` plane. We compute :math:`\Gamma_\varphi` by computing :math:`\Gamma_2*`, -the transverse relaxation rate, and subtracting :math:`\Gamma_1`, the longitudinal -relaxation rate. The pure dephasing time is defined by :math:`T_\varphi = -1/\Gamma_\varphi`. Or more precisely, :math:`1/T_\varphi = 1/T_{2*} - 1/2T_1` +:math:`T_\varphi`, or :math:`1/\Gamma_\varphi`, is the pure dephasing time of +depolarization in the :math:`x - y` plane of the Bloch sphere. We compute +:math:`\Gamma_\varphi` by computing :math:`\Gamma_2`, the *transverse relaxation rate*, +and subtracting :math:`\Gamma_1`, the *longitudinal relaxation rate*. It follows that +:math:`\frac{1}{T_\varphi} = \frac{1}{T_2} - \frac{1}{2T_1}`. We therefore create a composite experiment consisting of a :math:`T_1` experiment and a -:math:`T_2*` experiment. From the results of these two, we compute the results for +:math:`T_2` experiment. Both Ramsey and Hahn echo experiments can be used here, with +different effects. The :math:`T_2^*` estimate of the Ramsey experiment is sensitive to +inhomogeneous broadening, low-frequency fluctuations that vary between experiments due +to :math:`1/f`-type noise. The :math:`T_{2}` estimate from the Hahn echo (defined as +:math:`T_{2E}` in [#]_) is less sensitive to inhomogeneous broadening due to its +refocusing pulse, and so it is strictly larger than :math:`T_2^*` on a real device. In +superconducting qubits, :math:`T_2^*` tends to be significantly smaller than +:math:`T_1`, so :math:`T_2` is usually used. + +From the :math:`T_1` and :math:`T_2` estimates, we compute the results for :math:`T_\varphi.` .. jupyter-execute:: import numpy as np import qiskit - from qiskit_experiments.library.characterization import Tphi, TphiAnalysis, T1Analysis, T2RamseyAnalysis - -.. jupyter-execute:: + from qiskit_experiments.library.characterization import Tphi # An Aer simulator from qiskit.providers.fake_provider import FakeVigo @@ -36,49 +43,75 @@ We therefore create a composite experiment consisting of a :math:`T_1` experimen delays_t1 = np.arange(1e-6, 300e-6, 10e-6) delays_t2 = np.arange(1e-6, 50e-6, 2e-6) - +By default, the :class:`.Tphi` experiment will use the Hahn echo experiment for its transverse +relaxation time estimate. We can see that the component experiments of the batch +:class:`.Tphi` experiment are what we expect for :class:`.T1` and :class:`.T2Hahn`: .. jupyter-execute:: - # Create an experiment for qubit 0 with the specified time intervals - exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2, osc_freq=1e5) - - tphi_analysis = TphiAnalysis([T1Analysis(), T2RamseyAnalysis()]) - expdata = exp.run(backend=backend, analysis=tphi_analysis, seed_simulator=101).block_for_results() - result = expdata.analysis_results("T_phi") + exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2, num_echoes=1) + exp.component_experiment(0).circuits()[-1].draw("mpl") .. jupyter-execute:: - # Print the result for T_phi - print(result) + exp.component_experiment(1).circuits()[-1].draw("mpl") +Run the experiment and print results: .. jupyter-execute:: - # It is possible to see the results of the sub-experiments: - print(expdata) + expdata = exp.run(backend=backend, seed_simulator=100).block_for_results() + result = expdata.analysis_results("T_phi") + print(result) +You can also retrieve the results and figures of the constituent experiments. :class:`.T1`: .. jupyter-execute:: print(expdata.analysis_results("T1")) + display(expdata.figure(0)) + +And :class:`.T2Hahn`: .. jupyter-execute:: - display(expdata.figure(0)) + print(expdata.analysis_results("T2")) + display(expdata.figure(1)) +Let's now run the experiment with :class:`.T2Ramsey` by setting the ``t2type`` option to +``ramsey`` and specifying ``osc_freq``. Now the second component experiment is a Ramsey +experiment: .. jupyter-execute:: - print(expdata.analysis_results("T2star")) + exp = Tphi(physical_qubits=[0], + delays_t1=delays_t1, + delays_t2=delays_t2, + t2type="ramsey", + osc_freq=1e5) + + exp.component_experiment(1).circuits()[-1].draw("mpl") +Run and display results: .. jupyter-execute:: + expdata = exp.run(backend=backend, seed_simulator=100).block_for_results() + print(expdata.analysis_results("T_phi")) display(expdata.figure(1)) +Because we are using a simulator that doesn't model inhomogeneous broadening, the +:math:`T_2` and :math:`T_2^*` values are not significantly different. On a real +superconducting device, :math:`T_{\varphi}` should be significantly larger when the Hahn +echo experiment is used. + +References +---------- + +.. [#] Krantz, Philip, et al. *A Quantum Engineer's Guide to Superconducting Qubits*. + `arXiv:1904.06560 (2019) `_. + See also -------- * API documentation: :mod:`~qiskit_experiments.library.characterization.Tphi` - diff --git a/docs/manuals/measurement/restless_measurements.rst b/docs/manuals/measurement/restless_measurements.rst index 97f12dabad..7475fe9a71 100644 --- a/docs/manuals/measurement/restless_measurements.rst +++ b/docs/manuals/measurement/restless_measurements.rst @@ -27,7 +27,7 @@ allow the readout resonator to depopulate. When the qubit is not reset it will either be in the :math:`\left\vert0\right\rangle` or in the :math:`\left\vert1\right\rangle` state when the next circuit starts. Therefore, the measured outcomes of the restless experiments require post-processing. -The following example, taken from Ref. [1], illustrates what happens to the single +The following example, taken from Ref. [1]_, illustrates what happens to the single measurement outcomes represented as complex numbers in the IQ plane in a restless setting. Here, we run three circuits with an identity gate and three circuits with an :math:`X` gate, each followed by a measurement. The numbers in the IQ shots indicate the @@ -87,8 +87,10 @@ data processor post-processes the restless measured shots according to the order they were acquired. Furthermore, the appropriate run options are also set. Note that these run options might be unique to IBM Quantum providers. Therefore, execute may fail on non-IBM Quantum providers if the required options are not supported. -After calling :meth:`~.RestlessMixin.enable_restless` the experiment is ready to be run in a restless -mode. With a hardware backend this would be done by calling the ``run`` method + +After calling :meth:`~.RestlessMixin.enable_restless` the experiment is ready to be run +in a restless mode. With a hardware backend, this would be done by calling the +:meth:`~.BaseExperiment.run` method: .. jupyter-input:: @@ -117,17 +119,17 @@ the standard data processor by providing it to the analysis options and telling If you run the experiment in this setting you will see that the data is often unusable which illustrates the importance of the data processing. As detailed -in Ref. [2] restless measurements can be done with a wide variety +in Ref. [2]_, restless measurements can be done with a wide variety of experiments such as fine amplitude and drag error amplifying gate sequences as well as randomized benchmarking. Calculating restless quantum processor speed-ups ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Following Ref. [2], we can compare the time spent by the quantum processor executing +Following Ref. [2]_, we can compare the time spent by the quantum processor executing restless and standard jobs. This allows us to compute the effective speed-up we gain when performing restless experiments. Note that we do not consider any classical -run-time contributions such as runtime-compilation or data transfer times [3]. +run-time contributions such as runtime-compilation or data transfer times [3]_. The time to run :math:`K` circuits and gather :math:`N` shots for each circuit is @@ -191,21 +193,21 @@ The example above is applicable to other experiments and shows that restless measurements can greatly speed-up characterization and calibration tasks. References -~~~~~~~~~~ +---------- -[1] Max Werninghaus, Daniel J. Egger, Stefan Filipp, High-speed calibration and -characterization of superconducting quantum processors without qubit reset, -PRX Quantum **2**, 020324 (2021). https://arxiv.org/abs/2010.06576 +.. [1] Max Werninghaus, Daniel J. Egger, Stefan Filipp, High-speed calibration and + characterization of superconducting quantum processors without qubit reset, + PRX Quantum **2**, 020324 (2021). https://arxiv.org/abs/2010.06576. -[2] Caroline Tornow, Naoki Kanazawa, William E. Shanks, Daniel J. Egger, -Minimum quantum run-time characterization and calibration via restless -measurements with dynamic repetition rates, Physics Review Applied **17**, -064061 (2022). https://arxiv.org/abs/2202.06981 +.. [2] Caroline Tornow, Naoki Kanazawa, William E. Shanks, Daniel J. Egger, + Minimum quantum run-time characterization and calibration via restless + measurements with dynamic repetition rates, Physics Review Applied **17**, + 064061 (2022). https://arxiv.org/abs/2202.06981. -[3] Andrew Wack, Hanhee Paik, Ali Javadi-Abhari, Petar Jurcevic, Ismael Faro, -Jay M. Gambetta, Blake R. Johnson, Quality, Speed, and Scale: three key -attributes to measure the performance of near-term quantum computers, -https://arxiv.org/abs/2110.14108 +.. [3] Andrew Wack, Hanhee Paik, Ali Javadi-Abhari, Petar Jurcevic, Ismael Faro, + Jay M. Gambetta, Blake R. Johnson, Quality, Speed, and Scale: three key + attributes to measure the performance of near-term quantum computers, + https://arxiv.org/abs/2110.14108. See also -------- diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 5b70f5c793..66751fcd90 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -31,21 +31,18 @@ create the :class:`.DataAction` nodes that process the data. Data types on IBM Quantum backends ---------------------------------- -IBM Quantum backends can return different types of data. There is -counts data and IQ data [1], referred to as level 2 and level 1 data, -respectively. Level 2 data corresponds -to a dictionary with bit-strings as keys and the number of -times the bit-string was measured as a value. Importantly -for some experiments, the backends can return a lower data level -known as IQ data. Here, I and Q stand -for in phase and quadrature. The IQ are points in the complex plane -corresponding to a time integrated measurement signal which is -reflected or transmitted through the readout resonator depending -on the setup. IQ data can be returned as "single" or "averaged" data. -Here, single means that the outcome of each single shot is returned -while average only returns the average of the IQ points over the -measured shots. The type of data that an experiment should return -is specified by the :meth:`~.BaseExperiment.run_options` of an experiment. +IBM Quantum backends can return different types of data. There is counts data and IQ +data [1]_, referred to as level 2 and level 1 data, respectively. Level 2 data +corresponds to a dictionary with bit-strings as keys and the number of times the +bit-string was measured as a value. Importantly for some experiments, the backends can +return a lower data level known as IQ data. Here, I and Q stand for in phase and +quadrature. The IQ are points in the complex plane corresponding to a time integrated +measurement signal which is reflected or transmitted through the readout resonator +depending on the setup. IQ data can be returned as "single" or "averaged" data. Here, +single means that the outcome of each single shot is returned while average only returns +the average of the IQ points over the measured shots. The type of data that an +experiment should return is specified by the :meth:`~.BaseExperiment.run_options` of an +experiment. Processing data of different types ---------------------------------- @@ -155,7 +152,7 @@ data processor needs to be changed. display(exp_data.figure(0)) Now, the ``input_key`` is "counts" since that is the key under which the counts -data is saved in instances of ``ExperimentData``. The list of nodes +data is saved in instances of :class:`.ExperimentData`. The list of nodes comprises a single data action which converts the counts to an estimation of the probability of measuring the outcome "1". @@ -190,26 +187,17 @@ the analysis classes need. Typically, you will not need to implement the data processing yourself since Qiskit Experiments has built-in methods that determine the correct instance of :class:`.DataProcessor` for your data. More advanced data processing includes, for example, handling -restless measurements [2, 3]. +:doc:`restless measurements `. References -~~~~~~~~~~ +---------- -[1] Thomas Alexander, Naoki Kanazawa, Daniel J. Egger, Lauren Capelluto, +.. [1] Thomas Alexander, Naoki Kanazawa, Daniel J. Egger, Lauren Capelluto, Christopher J. Wood, Ali Javadi-Abhari, David McKay, Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses, Quantum -Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755 - -[2] Caroline Tornow, Naoki Kanazawa, William E. Shanks, Daniel J. Egger, -Minimum quantum run-time characterization and calibration via restless -measurements with dynamic repetition rates, Physics Review Applied **17**, -064061 (2022). https://arxiv.org/abs/2202.06981 - -[3] Max Werninghaus, Daniel J. Egger, Stefan Filipp, High-speed calibration and -characterization of superconducting quantum processors without qubit reset, -PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 +Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755. See also -------- -Experiment manual: :doc:`/manuals/measurement/restless_measurements` +- Experiment manual: :doc:`/manuals/measurement/restless_measurements` diff --git a/qiskit_experiments/framework/experiment_data.py b/qiskit_experiments/framework/experiment_data.py index 1d831b0d9e..1ecc4a471d 100644 --- a/qiskit_experiments/framework/experiment_data.py +++ b/qiskit_experiments/framework/experiment_data.py @@ -147,10 +147,10 @@ class ExperimentData: This class handles the following: - 1. Storing the data related to an experiment - the experiment's metadata, - the analysis results and the figures + 1. Storing the data related to an experiment: raw data, metadata, analysis results, + and figures 2. Managing jobs and adding data from jobs automatically - 3. Saving/Loading data from the result database + 3. Saving and loading data from the database service | diff --git a/qiskit_experiments/library/characterization/analysis/tphi_analysis.py b/qiskit_experiments/library/characterization/analysis/tphi_analysis.py index 9bd2b69d0d..8cbd07fdd2 100644 --- a/qiskit_experiments/library/characterization/analysis/tphi_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/tphi_analysis.py @@ -17,31 +17,38 @@ from qiskit_experiments.framework import ExperimentData, AnalysisResultData from qiskit_experiments.framework.composite.composite_analysis import CompositeAnalysis -from qiskit_experiments.library.characterization.analysis.t1_analysis import T1Analysis -from qiskit_experiments.library.characterization.analysis.t2ramsey_analysis import T2RamseyAnalysis +from qiskit_experiments.library.characterization.analysis import ( + T1Analysis, + T2HahnAnalysis, + T2RamseyAnalysis, +) from qiskit_experiments.exceptions import QiskitError class TphiAnalysis(CompositeAnalysis): - r""" - Tphi result analysis class. - A class to analyze :math:`T_\phi` experiments. + r"""A class to analyze :math:`T_\phi` experiments. + + # section: see_also + qiskit_experiments.library.characterization.analysis.T1Analysis + qiskit_experiments.library.characterization.analysis.T2HahnAnalysis + qiskit_experiments.library.characterization.analysis.T2RamseyAnalysis + """ def __init__(self, analyses=None): if analyses is None: - analyses = [T1Analysis(), T2RamseyAnalysis()] + analyses = [T1Analysis(), T2HahnAnalysis()] # Validate analyses kwarg if ( len(analyses) != 2 or not isinstance(analyses[0], T1Analysis) - or not isinstance(analyses[1], T2RamseyAnalysis) + or not isinstance(analyses[1], (T2RamseyAnalysis, T2HahnAnalysis)) ): raise QiskitError( - "Invalid component analyses for T2phi, analyses must be a pair of " - "T1Analysis and T2RamseyAnalysis instances." + "Invalid component analyses for Tphi, analyses must be a pair of " + "T1Analysis and T2HahnAnalysis or T2RamseyAnalysis instances." ) super().__init__(analyses, flatten_results=True) @@ -53,15 +60,18 @@ def _run_analysis( _run_analysis for the two sub-experiments. Based on the results, it computes the result for :math:`T_phi`. """ - # Run composite analysis and extract T1 and T2star results + + # Run composite analysis and extract T1 and T2 results analysis_results, figures = super()._run_analysis(experiment_data) t1_result = next(filter(lambda res: res.name == "T1", analysis_results)) - t2star_result = next(filter(lambda res: res.name == "T2star", analysis_results)) + t2_result = next( + filter(lambda res: res.name == "T2star" or res.name == "T2", analysis_results) + ) - # Calculate Tphi from T1 and T2star - tphi = 1 / (1 / t2star_result.value - 1 / (2 * t1_result.value)) + # Calculate Tphi from T1 and T2 + tphi = 1 / (1 / t2_result.value - 1 / (2 * t1_result.value)) quality_tphi = ( - "good" if (t1_result.quality == "good" and t2star_result.quality == "good") else "bad" + "good" if (t1_result.quality == "good" and t2_result.quality == "good") else "bad" ) tphi_result = AnalysisResultData( name="T_phi", diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index 8139eec005..4e35a36ee1 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -23,6 +23,7 @@ from qiskit_experiments.library.characterization import ( T1, T2Ramsey, + T2Hahn, TphiAnalysis, ) @@ -30,46 +31,42 @@ class Tphi(BatchExperiment): r"""An experiment to measure the qubit dephasing rate in the :math:`x - y` plane. - # section: overview + # section: overview - :math:`\Gamma_\varphi` is defined as the rate of pure dephasing - or depolarization in the :math:`x - y` plane. - We compute :math:`\Gamma_\varphi` by computing :math:`\Gamma_2*`, the transverse relaxation rate, - and subtracting :math:`\Gamma_1`, the longitudinal relaxation rate. The pure dephasing time - is defined by :math:`T_\varphi = 1/\Gamma_\varphi`. Or more precisely, + :math:`T_\varphi`, or :math:`1/\Gamma_\varphi`, is the pure dephasing time in + the :math:`x - y` plane of the Bloch sphere. We compute :math:`\Gamma_\varphi` + by computing :math:`\Gamma_2`, the transverse relaxation rate, and subtracting + :math:`\Gamma_1`, the longitudinal relaxation rate. It follows that - :math:`1/T_\varphi = 1/T_{2*} - 1/2T_1`. + :math:`1/T_\varphi = 1/T_2 - 1/2T_1`. - For more details, see :class:`T1` and :class:`T2Ramsey` + <<<<<<< HEAD + For more details, see :class:`T1` and :class:`T2Ramsey` + ======= + The transverse relaxation rate can be estimated by either :math:`T_2` or + :math:`T_2^*` experiments. In superconducting qubits, :math:`T_2^*` tends to be + significantly smaller than :math:`T_1`, so :math:`T_2` is usually used. - # section: analysis_ref - :class:`TphiAnalysis` + .. note:: + In 0.5.0, this experiment changed from using :math:`T_2^*` as the default + to :math:`T_2`. + >>>>>>> upstream/main - # section: reference - .. ref_arxiv:: 1 1904.06560v5 + # section: analysis_ref + :class:`TphiAnalysis` - # section: manual - :doc:`/manuals/characterization/tphi` - """ + # section: reference + .. ref_arxiv:: 1 1904.06560 - def set_experiment_options(self, **fields): - """Set the experiment options. - Args: - fields: The fields defining the options + # section: manual + :doc:`/manuals/characterization/tphi` - Raises: - QiskitError : Error for invalid input option. - """ - # propagate options to the sub-experiments. - for key in fields: - if key == "delays_t1": - self.component_experiment(0).set_experiment_options(delays=fields["delays_t1"]) - elif key == "delays_t2": - self.component_experiment(1).set_experiment_options(delays=fields["delays_t2"]) - elif key == "osc_freq": - self.component_experiment(1).set_experiment_options(osc_freq=fields["osc_freq"]) - else: - raise QiskitError(f"Tphi experiment does not support option {key}") + # section: see_also + qiskit_experiments.library.characterization.t1 + qiskit_experiments.library.characterization.t2ramsey + qiskit_experiments.library.characterization.t2hahn + + """ @qubit_deprecate() def __init__( @@ -77,28 +74,76 @@ def __init__( physical_qubits: Sequence[int], delays_t1: List[Union[List[float], np.array]], delays_t2: List[Union[List[float], np.array]], + t2type: str = "hahn", osc_freq: float = 0.0, + num_echoes: int = 1, backend: Optional[Backend] = None, ): """Initialize the experiment object. Args: - physical_qubits: a single-element sequence containing the qubit under test - delays_t1: delay times of the T1 experiment - delays_t2: delay times of the T2* experiment - osc_freq: the oscillation frequency induced using by the user for T2Ramsey - backend: Optional, the backend on which to run the experiment + physical_qubits: A single-element sequence containing the qubit under test. + t2type: What type of T2/T2* experiment to use. Can be either "ramsey" for + :class:`.T2Ramsey` to be used, or "hahn" for :class:`.T2Hahn`. Defaults + to "hahn". + delays_t1: Delay times of the T1 experiment. + delays_t2: Delay times of the T2 experiment. + osc_freq: The oscillation frequency induced for T2Ramsey. Only used when + ``t2type`` is set to "ramsey". + num_echoes: The number of echoes to perform for T2Hahn. Only used when + ``t2type`` is set to "hahn". + backend: Optional, the backend on which to run the experiment. + + Raises: + QiskitError: If an invalid ``t2type`` is provided. """ exp_t1 = T1(physical_qubits=physical_qubits, delays=delays_t1, backend=backend) - exp_t2 = T2Ramsey( - physical_qubits=physical_qubits, - delays=delays_t2, - backend=backend, - osc_freq=osc_freq, - ) + + exp_options = {"delays_t1": delays_t1, "delays_t2": delays_t2} + + if t2type == "ramsey": + exp_t2 = T2Ramsey( + physical_qubits=physical_qubits, + delays=delays_t2, + backend=backend, + osc_freq=osc_freq, + ) + exp_options["osc_freq"] = osc_freq + elif t2type == "hahn": + exp_t2 = T2Hahn( + physical_qubits=physical_qubits, + delays=delays_t2, + backend=backend, + num_echoes=num_echoes, + ) + exp_options["num_echoes"] = num_echoes + else: + raise QiskitError(f"Invalid T2 experiment type {t2type} specified.") + analysis = TphiAnalysis([exp_t1.analysis, exp_t2.analysis]) # Create batch experiment super().__init__([exp_t1, exp_t2], backend=backend, analysis=analysis) - self.set_experiment_options(delays_t1=delays_t1, delays_t2=delays_t2) + self.set_experiment_options(**exp_options) + + def set_experiment_options(self, **fields): + """Set the experiment options. + Args: + fields: The fields defining the options. + + Raises: + QiskitError: Invalid input option. + """ + # propagate options to the sub-experiments. + for key in fields: + if key == "delays_t1": + self.component_experiment(0).set_experiment_options(delays=fields["delays_t1"]) + elif key == "delays_t2": + self.component_experiment(1).set_experiment_options(delays=fields["delays_t2"]) + elif key == "osc_freq" and isinstance(self.component_experiment(1), T2Ramsey): + self.component_experiment(1).set_experiment_options(osc_freq=fields["osc_freq"]) + elif key == "num_echoes" and isinstance(self.component_experiment(1), T2Hahn): + self.component_experiment(1).set_experiment_options(num_echoes=fields["num_echoes"]) + else: + raise QiskitError(f"Tphi experiment does not support option {key}.") diff --git a/releasenotes/notes/tphi-option-025f02c2c843c74f.yaml b/releasenotes/notes/tphi-option-025f02c2c843c74f.yaml new file mode 100644 index 0000000000..93ade0476e --- /dev/null +++ b/releasenotes/notes/tphi-option-025f02c2c843c74f.yaml @@ -0,0 +1,8 @@ +--- +upgrades: + - | + The :class:`.Tphi` has been changed to use :class:`.T2Hahn` as the default T2 + estimate because it provides a more meaningful measurement on superconducting + devices. An option ``t2type`` has been added to allow the user to toggle between + using :math:`T_2^*` from :class:`.T2Ramsey` by specifying "ramsey" or :math:`T_2` + from :class:`.T2Hahn`, which is the default value "hahn". diff --git a/requirements-dev.txt b/requirements-dev.txt index e24fa0f60f..48296535bc 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -5,8 +5,7 @@ pylint==2.7.1 jinja2==3.0.3 sphinx~=5.0 jupyter-sphinx>=0.4.0 -# Temporary pinned qiskit-sphinx-theme dependency until its next release -git+https://github.com/Qiskit/qiskit_sphinx_theme.git@edbff26d59aa194d25d87ef37f052e3043c9c822 +qiskit-sphinx-theme==1.11.0rc1 sphinx-autodoc-typehints<=1.20.2 sphinx-design==0.3.0 pygments>=2.4 diff --git a/test/library/characterization/test_tphi.py b/test/library/characterization/test_tphi.py index fe5b42a2ab..762d63d067 100644 --- a/test/library/characterization/test_tphi.py +++ b/test/library/characterization/test_tphi.py @@ -10,27 +10,37 @@ # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """ -Test T2Ramsey experiment +Test Tphi experiment. """ - from test.base import QiskitExperimentsTestCase -from qiskit_experiments.library import Tphi +from qiskit.exceptions import QiskitError +from qiskit_experiments.library import Tphi, T2Hahn, T2Ramsey from qiskit_experiments.test.noisy_delay_aer_simulator import NoisyDelayAerBackend -from qiskit_experiments.library.characterization.analysis.tphi_analysis import TphiAnalysis +from qiskit_experiments.library.characterization.analysis import ( + TphiAnalysis, + T2RamseyAnalysis, + T2HahnAnalysis, +) class TestTphi(QiskitExperimentsTestCase): - """Test Tphi experiment""" + """Test Tphi experiment.""" __tolerance__ = 0.1 - def test_tphi_end_to_end(self): + def test_tphi_ramsey_end_to_end(self): """ - Run a complete Tphi experiment on a fake Tphi backend + Run a complete Tphi experiment with T2ramsey on a fake Tphi backend. """ delays_t1 = list(range(1, 40, 3)) delays_t2 = list(range(1, 51, 2)) - exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2, osc_freq=0.1) + exp = Tphi( + physical_qubits=[0], + delays_t1=delays_t1, + delays_t2=delays_t2, + t2type="ramsey", + osc_freq=0.1, + ) t1 = 20 t2ramsey = 25 @@ -55,7 +65,13 @@ def test_tphi_with_changing_params(self): """ delays_t1 = list(range(1, 40, 3)) delays_t2 = list(range(1, 50, 2)) - exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2, osc_freq=0.1) + exp = Tphi( + physical_qubits=[0], + delays_t1=delays_t1, + delays_t2=delays_t2, + t2type="ramsey", + osc_freq=0.1, + ) t1 = 20 t2ramsey = 25 @@ -101,9 +117,31 @@ def test_tphi_with_changing_params(self): self.assertListEqual(x_values_t2, new_delays_t2, "Incorrect delays_t2") self.assertEqual(new_freq_t2, new_osc_freq, "Option osc_freq not set correctly") + def test_tphi_t2_option(self): + """Test that Tphi switches between T2Ramsey and T2Hahn correctly.""" + + delays_t1 = list(range(1, 40, 3)) + delays_t2 = list(range(1, 50, 2)) + + exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2, t2type="ramsey") + self.assertTrue(isinstance(exp.component_experiment(1), T2Ramsey)) + self.assertTrue(isinstance(exp.analysis.component_analysis(1), T2RamseyAnalysis)) + with self.assertRaises(QiskitError): # T2Ramsey should not allow a T2Hahn option + exp.set_experiment_options(num_echoes=1) + + exp = Tphi(physical_qubits=[0], delays_t1=delays_t1, delays_t2=delays_t2) + self.assertTrue(isinstance(exp.component_experiment(1), T2Hahn)) + self.assertTrue(isinstance(exp.analysis.component_analysis(1), T2HahnAnalysis)) + with self.assertRaises(QiskitError): # T2Hahn should not allow a T2ramsey option + exp.set_experiment_options(osc_freq=0.0) + def test_roundtrip_serializable(self): """Test round trip JSON serialization""" - exp = Tphi([0], [1], [2], 3) + exp = Tphi([0], [1], [2]) + self.assertRoundTripSerializable(exp, self.json_equiv) + exp = Tphi([0], [1], [2], "hahn", 3) + self.assertRoundTripSerializable(exp, self.json_equiv) + exp = Tphi([0], [1], [2], "ramsey", 0) self.assertRoundTripSerializable(exp, self.json_equiv) def test_analysis_config(self): From 77c0a9f854ce969e9fe4ce46016b7ce70cb00858 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Mar 2023 15:22:27 -0400 Subject: [PATCH 61/64] fixed styling --- docs/tutorials/data_processor.rst | 6 +++--- docs/tutorials/getting_started.rst | 16 +++++++++++----- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 66751fcd90..f09755a8a0 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -193,9 +193,9 @@ References ---------- .. [1] Thomas Alexander, Naoki Kanazawa, Daniel J. Egger, Lauren Capelluto, -Christopher J. Wood, Ali Javadi-Abhari, David McKay, Qiskit Pulse: -Programming Quantum Computers Through the Cloud with Pulses, Quantum -Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755. + Christopher J. Wood, Ali Javadi-Abhari, David McKay, Qiskit Pulse: + Programming Quantum Computers Through the Cloud with Pulses, Quantum + Science and Technology **5**, 044006 (2020). https://arxiv.org/abs/2004.06755. See also -------- diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index c611d28328..15ada32f8c 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -95,8 +95,8 @@ first and last circuits for our :math:`T_1` experiment: As expected, the delay block spans the full range of time values that we specified. -The ExperimentData class -======================== +The :class:`.ExperimentData` class +================================== After instantiating the experiment, we run the experiment by calling :meth:`~.BaseExperiment.run` with our backend of choice. This transpiles our experiment @@ -175,9 +175,15 @@ For further documentation on how to work with UFloats, consult the ``uncertainti Raw circuit output data and its associated metadata can be accessed with the :meth:`~.ExperimentData.data` property. Data is indexed by the circuit it corresponds to. Depending on the measurement level set in the experiment, the raw data will either -be in the key ``counts`` (level 2) or ``memory`` (level 1 IQ data). Circuit metadata -contains information set by the experiment on a circuit-by-circuit basis; ``xval`` is -used by the analysis to extract the x value for each circuit when fitting the data. +be in the key ``counts`` (level 2) or ``memory`` (level 1 IQ data). + +.. note:: + See the :doc:`data processor tutorial ` for more + information on level 1 and level 2 data. + +Circuit metadata contains information set by the experiment on a circuit-by-circuit +basis; ``xval`` is used by the analysis to extract the x value for each circuit when +fitting the data. .. jupyter-execute:: From 2014a59ae1f28d48418a7f5b5444b6a4aa1d2f1f Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Mar 2023 15:58:54 -0400 Subject: [PATCH 62/64] added docstring --- qiskit_experiments/library/characterization/tphi.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index 7f2a2fffd5..9d38ab64cb 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -48,6 +48,9 @@ class Tphi(BatchExperiment): In 0.5.0, this experiment changed from using :math:`T_2^*` as the default to :math:`T_2`. + # section: analysis_ref + :class:`.TPhiAnalysis` + # section: reference .. ref_arxiv:: 1 1904.06560 From e14dec1abc8b71e4da312d161ceac7d63a1428b6 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Mar 2023 18:18:08 -0400 Subject: [PATCH 63/64] addressed review comments --- docs/howtos/job_splitting.rst | 11 +- docs/howtos/new_experimentdata.rst | 24 +++- docs/manuals/benchmarking/quantum_volume.rst | 10 +- docs/tutorials/custom_experiment.rst | 129 +++++++++++++------ 4 files changed, 119 insertions(+), 55 deletions(-) diff --git a/docs/howtos/job_splitting.rst b/docs/howtos/job_splitting.rst index 36d460b0b2..0785551e67 100644 --- a/docs/howtos/job_splitting.rst +++ b/docs/howtos/job_splitting.rst @@ -21,17 +21,18 @@ You can set the ``max_circuits`` option manually when running an experiment: The experiment class will split its circuits into jobs such that no job has more than ``max_circuits`` number of jobs. -Furthermore, the :class:`.BatchExperiment` class has the experiment option ``separate_jobs`` -which will run circuits of different sub-experiments in different jobs: +Furthermore, the :class:`.BatchExperiment` class has the experiment option +``separate_jobs`` which will run circuits of different sub-experiments in different +jobs: .. jupyter-input:: batch_exp = BatchExperiment([exp, exp]) batch_exp.set_experiment_options(separate_jobs=True) -Note that the purpose of :class:`.ParallelExperiment` is to run -circuits on disjoint sets of qubits simultaneously, -therefore this option is not of much use for parallel experiments. +Note that this option is only available to :class:`.BatchExperiment` objects. To manage +job splitting when using :class:`.ParallelExperiment`, you can make a nested batch +experiment of parallel experiments. Discussion ---------- diff --git a/docs/howtos/new_experimentdata.rst b/docs/howtos/new_experimentdata.rst index bb7107f86d..24dcc11fd7 100644 --- a/docs/howtos/new_experimentdata.rst +++ b/docs/howtos/new_experimentdata.rst @@ -14,8 +14,9 @@ Solution This guide requires :mod:`qiskit-ibm-provider`. For how to migrate from the deprecated :mod:`qiskit-ibmq-provider` to :mod:`qiskit-ibm-provider`, consult the `migration guide `_.\ -Use the code template below. You need to know the exact experiment you -ran and its options, as well as the IDs of the jobs that were executed. +Use the code template below. You need to recreate the exact experiment you ran and its +options, as well as the IDs of the jobs that were executed. The jobs must be accessible +through the provider that you use. .. jupyter-input:: @@ -47,11 +48,20 @@ where the jobs may have finished running on the remote backends but the :class:`.ExperimentData` class returned upon completion of an experiment does not contain correct results. -You may also want to rerun the analysis of a previously-run experiment with different -options when you instantiate this new :class:`.ExperimentData` object. -Here's a code snippet where we reconstruct a parallel experiment -consisting of randomized benchmarking experiments, then change the gate error ratio -as well as the line plot color of the first component experiment. +Recreation of the experiment object is often done by rerunning the code that you ran +previously to create it. It may sometimes be helpful instead to save an experiment and +restore it later with the following lines of code: + +.. jupyter-input:: + + serialized_exp = json.dumps(Experiment.config()) + Experiment.from_config(json.loads(serialized_exp)) + +You may also want to rerun the analysis with different options of a previously-run +experiment when you instantiate this new :class:`.ExperimentData` object. Here's a code +snippet where we reconstruct a parallel experiment consisting of randomized benchmarking +experiments, then change the gate error ratio as well as the line plot color of the +first component experiment. .. jupyter-input:: diff --git a/docs/manuals/benchmarking/quantum_volume.rst b/docs/manuals/benchmarking/quantum_volume.rst index cc9f0e02b3..e57a192c2c 100644 --- a/docs/manuals/benchmarking/quantum_volume.rst +++ b/docs/manuals/benchmarking/quantum_volume.rst @@ -175,13 +175,13 @@ References ---------- .. [1] Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and -Jay M. Gambetta, Validating quantum computers using randomized model -circuits, Phys. Rev. A 100, 032328 (2019). -https://arxiv.org/pdf/1811.12926 + Jay M. Gambetta, Validating quantum computers using randomized model + circuits, Phys. Rev. A 100, 032328 (2019). + https://arxiv.org/pdf/1811.12926 .. [2] Petar Jurcevic et. al. Demonstration of quantum volume 64 on -a superconducting quantum computing system, -https://arxiv.org/pdf/2008.08571 + a superconducting quantum computing system, + https://arxiv.org/pdf/2008.08571 See also -------- diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 6693c72b20..aecebc95c6 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -62,12 +62,12 @@ The :meth:`.BaseAnalysis._run_analysis` method should return a pair :class:`.AnalysisResultData` objects and ``figures`` is a list of :class:`matplotlib.figure.Figure` objects. -The :mod:`~qiskit_experiments.data_processing` module contains classes for +The :doc:`Data Processor ` module contains classes for building data processor workflows to help with advanced analysis of experiment data. If you want to customize the figures of the experiment, consult the -:doc:`visualization module ` tutorial. +:doc:`Visualization tutorial `. @@ -78,25 +78,38 @@ Here is a barebones template to help you get started with customization: .. jupyter-input:: + from qiskit.circuit import QuantumCircuit + from typing import List, Optional, Sequence + from qiskit.providers.backend import Backend + from qiskit_experiments.framework import BaseExperiment, Options + class CustomExperiment(BaseExperiment): """Custom experiment class template.""" - def __init__(self, qubits=None): + def __init__(self, + physical_qubits: Sequence[int], + backend: Optional[Backend] = None): """Initialize the experiment.""" - if qubits is None: - qubits = [0] - super().__init__(qubits, analysis=FakeAnalysis()) + super().__init__(physical_qubits, + analysis = CustomAnalysis(), + backend = backend) - def circuits(self): + def circuits(self) -> List[QuantumCircuit]: """Generate the list of circuits to be run.""" - return [] + circuits = [] + # Generate circuits and populate metadata here + for i in loops: + circ = QuantumCircuit(self.num_qubits) + circ.metadata = {} + circuits.append(circ) + return circuits @classmethod def _default_experiment_options(cls) -> Options: - """Update default experiment options here.""" + """Set default experiment options here.""" options = super()._default_experiment_options() options.update_options( - dummy_option=None, + dummy_option = None, ) return options @@ -104,21 +117,43 @@ And the corresponding analysis class template: .. jupyter-input:: + import matplotlib + from typing import Tuple, List + from qiskit_experiments.framework import ( + BaseAnalysis, + Options, + ExperimentData, + AnalysisResultData + ) + class CustomAnalysis(BaseAnalysis): """Custom analysis class template.""" - def __init__(self, **kwargs): - super().__init__() - self._kwargs = kwargs + @classmethod + def _default_options(cls) -> Options: + """Set default analysis options. Plotting is on by default.""" + + options = super()._default_options() + options.dummy_analysis_option = None + options.plot = True + options.ax = None + return options + + def _run_analysis( + self, + experiment_data: ExperimentData + ) -> Tuple[List[AnalysisResultData], List["matplotlib.figure.Figure"]]: + """Run the analysis.""" + + # Process the data here - def _run_analysis(self, experiment_data, **options): - seed = options.get("seed", None) - rng = np.random.default_rng(seed=seed) analysis_results = [ - AnalysisResultData(f"result_{i}", value) for i, value in enumerate(rng.random(3)) + AnalysisResultData(name="dummy result", value=data) ] - return analysis_results, None - + figures = [] + if self.options.plot: + figures.append(self._plot(data)) + return analysis_results, figures Now we'll use what we've learned so far to make an entirely new experiment using the :class:`.BaseExperiment` template. @@ -142,9 +177,9 @@ measurement readout error. To start, we write our own ``__init__()`` method to take as input the circuit that we want to twirl on. We also want to give the user the option to specify which -physical qubits to run the circuit over, and which qubits to measure over. If the user -doesn't specify these options, we default both to the list of qubits starting with 0 with -the length of the number of qubits in the circuit for both. +physical qubits to run the circuit over and which qubits to measure over. If the user +doesn't specify these options, we default both to the list of qubits starting with 0 and +up to the length of the number of qubits in the circuit - 1 for both. .. jupyter-input:: @@ -161,7 +196,8 @@ the length of the number of qubits in the circuit for both. measured_qubits=None, physical_qubits=None, backend=None, - **experiment_options + num_samples=10, + seed=None ): """Basic randomized Z-basis measurement experiment via a Pauli frame transformation @@ -183,13 +219,12 @@ the length of the number of qubits in the circuit for both. self._measured_qubits = measured_qubits # Set any init optinos - self.set_experiment_options(**experiment_options) + self.set_experiment_options(num_samples=num_samples, seed=seed) Notice that when we called ``super().__init__``, we provided the list of physical qubits, the name of our analysis class, and the backend, which is optionally specified by the user at this stage. - Now we consider default experiment options. Because randomness is involved, it is good practice to allow the user to set a seed. We would also like the user to be able to set how many repetitions of the circuit to run: @@ -210,8 +245,8 @@ Now we write the ``circuits()`` method. We need to take the input circuit in ``self._circuit`` and add our random Paulis as well as measurement at the end. We use the built-in property :attr:`~.BaseExperiment.num_qubits` of :class:`~.BaseExperiment` to get the number of qubits in the experiment. We keep track of the list of qubits and -classical Note that the circuits themselves are always built on qubits `0` to `length of -the circuit - 1`, and not the actual physical qubit indices given in +classical registers. Note that the circuits themselves are always built on qubits `0` to +`length of the circuit - 1`, and not the actual physical qubit indices given in ``physical_qubits``, as discussed in :doc:`getting_started`. .. jupyter-input:: @@ -259,7 +294,7 @@ across ``meas_nc`` qubits. Now we construct the circuits by composing the original circuit with a Pauli frame then adding a measurement at the end only to the measurement qubits. Metadata containing -the classical measurement register and the applied Pauli, and the +the classical measurement register and the applied Pauli is added to each of the circuits to tell the analysis class how to restore the original results. To make restoration easier, we store Paulis in the :class:`x symplectic form ` in ``metadata["rm_sig"]`` @@ -311,9 +346,6 @@ counts from the original experiment. class RandomizedMeasurementAnalysis(BaseAnalysis): """Analysis for randomized measurement experiment.""" - # Helper dict to swap a clbit value - _swap_bit = {"0": "1", "1": "0"} - def _run_analysis(self, experiment_data): combined_counts = {} @@ -349,6 +381,8 @@ output if the Pauli corresponding to that bit has a nonzero signature. .. jupyter-input:: ... + # Helper dict to swap a clbit value + _swap_bit = {"0": "1", "1": "0"} @classmethod def _swap_bitstring(cls, bitstring, sig): @@ -376,7 +410,8 @@ output if the Pauli corresponding to that bit has a nonzero signature. measured_qubits=None, physical_qubits=None, backend=None, - **experiment_options + num_samples=10, + seed=None ): if physical_qubits is None: @@ -390,7 +425,7 @@ output if the Pauli corresponding to that bit has a nonzero signature. self._circuit = circuit self._measured_qubits = measured_qubits - self.set_experiment_options(**experiment_options) + self.set_experiment_options(num_samples=num_samples, seed=seed) @classmethod def _default_experiment_options(cls): @@ -496,7 +531,7 @@ To test our code, we first simulate a noisy backend with asymmetric readout erro noise_model.add_all_qubit_readout_error([[1 - p1g0, p1g0], [p0g1, 1 - p0g1]]) noise_backend = AerSimulator(noise_model=noise_model) -Let's use a GHZ circuit as the input. +Let's use a GHZ circuit as the input: .. jupyter-execute:: @@ -522,7 +557,8 @@ Check that the experiment is appending a random Pauli and measurements as expect exp = RandomizedMeasurement(qc, num_samples=num_samples) exp.circuits()[0].draw("mpl") -We now run the experiment with a GHZ circuit on an ideal backend: +We now run the experiment with a GHZ circuit on an ideal backend, whic produces nearly +perfect symmetrical results between :math:`|0000\rangle` and :math:`|1111\rangle`: .. jupyter-execute:: @@ -554,6 +590,23 @@ from running GHZ circuit itself: "Asymmetric meas error (Direct)", "Asymmetric meas error (Randomized)"]) -The asymmetric measurement of the original circuit on this backend (Direct on the plot legend) -has been successfully symmetrized by the application of randomized measurement -(Randomized on the plot legend). +For a GHZ state, we expect a symmetric noise model to also produce symmetric readout +results. The asymmetric measurement of the original circuit on this backend (Direct on +the plot legend) has been successfully symmetrized by the application of randomized +measurement (Randomized on the plot legend). + +Note that since this experiment tracks the original and added classical registers, it is +possible for the original circuit to have its own mid-circuit measurements that would be +unaffected by the added randomized measurements, which use its own classical registers: + +.. jupyter-execute:: + + qc = QuantumCircuit(nq) + qc.h(0) + qc.measure_all() + qc.barrier() + for i in range(1, nq): + qc.cx(i-1, i) + + exp = RandomizedMeasurement(qc, num_samples=num_samples) + exp.circuits()[0].draw() \ No newline at end of file From a0e2b40a51526416903b93ce5ffed40b3a5a1643 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 13 Mar 2023 18:54:46 -0400 Subject: [PATCH 64/64] minor edits --- .../benchmarking/randomized_benchmarking.rst | 15 +++++++--- docs/tutorials/custom_experiment.rst | 30 +++++++++---------- 2 files changed, 26 insertions(+), 19 deletions(-) diff --git a/docs/manuals/benchmarking/randomized_benchmarking.rst b/docs/manuals/benchmarking/randomized_benchmarking.rst index 6d8e61083c..a1c6fa874c 100644 --- a/docs/manuals/benchmarking/randomized_benchmarking.rst +++ b/docs/manuals/benchmarking/randomized_benchmarking.rst @@ -208,21 +208,28 @@ contribution of depolarization from single-qubit error channels. Displaying the RB circuits -------------------------- -Generating an example RB circuit: +The default RB circuit output shows Clifford blocks: .. jupyter-execute:: # Run an RB experiment on qubit 0 - exp = StandardRB(physical_qubits=[0], lengths=[10], num_samples=1, seed=seed) + exp = StandardRB(physical_qubits=[0], lengths=[2], num_samples=1, seed=seed) c = exp.circuits()[0] + c.draw("mpl") -We transpile the circuit into the backend’s basis gate set: +You can decompose the circuit into underlying gates: + +.. jupyter-execute:: + + c.decompose().draw("mpl") + +And see the transpiled circuit using the basis gate set of the backend: .. jupyter-execute:: from qiskit import transpile basis_gates = backend.configuration().basis_gates - print(transpile(c, basis_gates=basis_gates)) + transpile(c, basis_gates=basis_gates).draw("mpl") Interleaved RB experiment diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index aecebc95c6..ff0ec51f10 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -70,7 +70,6 @@ If you want to customize the figures of the experiment, consult the :doc:`Visualization tutorial `. - Custom experiment template -------------------------- @@ -113,7 +112,11 @@ Here is a barebones template to help you get started with customization: ) return options -And the corresponding analysis class template: +Notice that when we called ``super().__init__``, we provided the list of physical +qubits, the name of our analysis class, and the backend, which is optionally specified +by the user at this stage. + +The corresponding custom analysis class template: .. jupyter-input:: @@ -175,11 +178,13 @@ are then combined across samples to return a single counts dictionary for the original circuit. This has the effect of Pauli twirling and symmetrizing the measurement readout error. -To start, we write our own ``__init__()`` method to take as input the circuit that -we want to twirl on. We also want to give the user the option to specify which -physical qubits to run the circuit over and which qubits to measure over. If the user -doesn't specify these options, we default both to the list of qubits starting with 0 and -up to the length of the number of qubits in the circuit - 1 for both. +To start, we write our own ``__init__()`` method to take as input the circuit that we +want to twirl on. We also want to give the user the option to specify which physical +qubits to run the circuit over, which qubits to measure over, the number of samples to +repeat, and the seed for the random generator. If the user doesn't specify these +options, we default the qubits to the list of qubits starting with 0 and up to the +length of the number of qubits in the circuit - 1 for both, and the number of samples +to 10. .. jupyter-input:: @@ -221,13 +226,8 @@ up to the length of the number of qubits in the circuit - 1 for both. # Set any init optinos self.set_experiment_options(num_samples=num_samples, seed=seed) -Notice that when we called ``super().__init__``, we provided the list of physical qubits, -the name of our analysis class, and the backend, which is optionally specified by the -user at this stage. - -Now we consider default experiment options. Because randomness is involved, -it is good practice to allow the user to set a seed. We would also like the user to -be able to set how many repetitions of the circuit to run: +Now we consider default experiment options. We choose to only let the user change +the number of samples and seed after instantiation by updating the experiment options. .. jupyter-input:: @@ -609,4 +609,4 @@ unaffected by the added randomized measurements, which use its own classical reg qc.cx(i-1, i) exp = RandomizedMeasurement(qc, num_samples=num_samples) - exp.circuits()[0].draw() \ No newline at end of file + exp.circuits()[0].draw("mpl") \ No newline at end of file

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z{{!-MZ*S#^jqf`OBW`mp?I3G=%9CU-BEArhTd%5X`&UvYs%%rp2A$hNolD*6?Iea$ z8wrWxR{P9TZ>DLcH>$fK6ZSd5{!Y5VCN7kI`OtyN`SfU z@w*G&aIMd*JkRJY`ibN^E(6vZc}*M51IaAUZtEZ-EpDZKTov7g$Q*>Axcwd&!~z8( z6A;mPN!+E_p1%#~BG!1P{i~dB9ymt|!DG#r=D3#5a6@l01~982?%4-Yjai`lhQAgeFoq*W$R1Z~ qM`_.\ + Use the code template below. You need to know the exact experiment you -ran and its options, the IDs of the jobs that were executed. You also need the -``backend`` object corresponding to the backend that the experiment was run on. +ran and its options, as well as the IDs of the jobs that were executed. .. jupyter-input:: from qiskit_experiments.framework import ExperimentData + from qiskit_ibm_provider import IBMProvider # The experiment you ran experiment = Experiment(**opts) @@ -24,8 +28,10 @@ ran and its options, the IDs of the jobs that were executed. You also need the # List of job IDs for the experiment job_ids= [job1, job2, ...] + provider = IBMProvider() + data = ExperimentData(experiment = experiment) - data.add_jobs([backend.retrieve_job(job_id) for job_id in job_ids]) + data.add_jobs([provider.retrieve_job(job_id) for job_id in job_ids]) experiment.analysis.run(data) # Block execution of subsequent code until analysis is complete @@ -62,7 +68,7 @@ as well as the line plot color of the first component experiment. ) data = ExperimentData(experiment=pexp) - data.add_jobs([backend_real.retrieve_job(job_id) for job_id in job_ids]) + data.add_jobs([provider.retrieve_job(job_id) for job_id in job_ids]) pexp.analysis.run(data) See Also diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index 1c9e9ceed4..d441620cee 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -10,37 +10,16 @@ module, we would need to define pulse schedules and plot the resulting measureme data manually. In this tutorial, we demonstrate how to calibrate single-qubit gates using the -calibration framework in Qiskit Experiments. You can run these experiments on any -backend with Pulse enabled: - -.. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - -.. jupyter-execute:: - - from qiskit import IBMQ - IBMQ.load_account() - provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') - backend = provider.get_backend('ibmq_lima') - -We can verify whether the backend supports Pulse features by checking the -backend configuration: - -.. jupyter-execute:: - - backend_config = backend.configuration() - assert backend_config.open_pulse, "Backend doesn't support Pulse" - -For the purposes of the tutorial, we will run experiments on our test pulse +calibration framework in Qiskit Experiments. We will run experiments on our test pulse backend, :class:`.SingleTransmonTestBackend`, a backend that simulates the underlying pulses -with :doc:`Qiskit Dynamics ` on a three-level model of a transmon. We will run experiments to +with `Qiskit Dynamics `_ on a +three-level model of a transmon. You can run these experiments on any real backend with +Pulse enabled (see :external+qiskit:doc:`tutorials/circuits_advanced/08_gathering_system_information`). + +. We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses, and choose the value of the DRAG parameter that minimizes leakage. The calibration framework requires -the user to +the user to: - Set up an instance of :class:`.Calibrations`, diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index e590a0e45f..41e2231ada 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -198,7 +198,7 @@ There are four types of options one can set for an experiment: Run options ----------- -These options are passed to the experiment's ``run()`` method and then to the ``run()`` +These options are passed to the experiment's :meth:`~.BaseExperiment.run` method and then to the ``run()`` method of your specified backend. Any run option that your backend supports can be set: .. jupyter-input:: @@ -207,7 +207,7 @@ method of your specified backend. Any run option that your backend supports can meas_level=MeasLevel.CLASSIFIED, meas_return="avg") -Consult the documentation of :meth:`qiskit.providers.ibmq.IBMQBackend.run` or +Consult the documentation of :meth:`qiskit.execute_function` or the run method of your specific backend type for valid options. Transpile options @@ -229,7 +229,7 @@ These options are unique to each experiment class. Many experiment options can b upon experiment instantiation, but can also be explicitly set via :meth:`~BaseExperiment.set_experiment_options`: -.. code-block:: +.. jupyter-input:: exp = T1(physical_qubits=(i,), delays=delays) exp.set_experiment_options(delays=new_delays) From 3dca48082aafa89cd667efbae09a1c7b029d9746 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 2 Mar 2023 16:21:37 -0500 Subject: [PATCH 38/64] downgrade sphinx version and update contributor guide --- CONTRIBUTING.md | 67 +++++++------ docs/GUIDELINES.md | 97 +++++++++++++++++++ docs/tutorials/GUIDELINES.md | 39 -------- .../characterization/fine_amplitude.py | 6 +- requirements-dev.txt | 4 +- tox.ini | 4 +- 6 files changed, 140 insertions(+), 77 deletions(-) create mode 100644 docs/GUIDELINES.md delete mode 100644 docs/tutorials/GUIDELINES.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e38527d07a..efdcfa4976 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -10,27 +10,30 @@ Qiskit Experiments are documented below. ### Contents - + [Proposing a new experiment](#proposing-a-new-experiment) - + [Choosing an issue to work on](#choosing-an-issue-to-work-on) - + [Pull request checklist](#pull-request-checklist) - + [Code style](#code-style) - + [Testing your code](#testing-your-code) - - [STDOUT/STDERR and logging capture](#stdoutstderr-and-logging-capture) - + [Changelog generation](#changelog-generation) - + [Release notes](#release-notes) - - [Adding a new release note](#adding-a-new-release-note) - * [Linking to issues](#linking-to-issues) - - [Generating release notes](#generating-release-notes) - + [Documentation](#documentation) - + [Experiment class documentation](#experiment-class-documentation) - + [Analysis class documentation](#analysis-class-documentation) - + [Populating the table of contents](#populating-the-table-of-contents) - + [Updating the tutorials](#updating-the-tutorials) - + [Building documentation locally](#building-documentation-locally) - + [Adding deprecation warnings](#adding-deprecation-warnings) - + [Development cycle](#development-cycle) - + [Branches](#branches) - + [Release cycle](#release-cycle) +- [Contributing Guide](#contributing-guide) + - [Contributing to Qiskit Experiments](#contributing-to-qiskit-experiments) + - [Contents](#contents) + - [Proposing a new experiment](#proposing-a-new-experiment) + - [Choosing an issue to work on](#choosing-an-issue-to-work-on) + - [Pull request checklist](#pull-request-checklist) + - [Code style](#code-style) + - [Testing your code](#testing-your-code) + - [STDOUT/STDERR and logging capture](#stdoutstderr-and-logging-capture) + - [Changelog generation](#changelog-generation) + - [Release notes](#release-notes) + - [Adding a new release note](#adding-a-new-release-note) + - [Linking to issues](#linking-to-issues) + - [Generating release notes](#generating-release-notes) + - [Documentation](#documentation) + - [Experiment class documentation](#experiment-class-documentation) + - [Analysis class documentation](#analysis-class-documentation) + - [Populating the table of contents](#populating-the-table-of-contents) + - [Updating the documentation](#updating-the-documentation) + - [Building documentation locally](#building-documentation-locally) + - [Adding deprecation warnings](#adding-deprecation-warnings) + - [Development cycle](#development-cycle) + - [Branches](#branches) + - [Release cycle](#release-cycle) ### Proposing a new experiment @@ -65,9 +68,9 @@ When submitting a pull request for review, please ensure that: 1. The code follows the code style of the project and successfully passes the tests. 2. The API documentation has been updated accordingly. -3. You have updated the relevant tutorial or write a new one. In case the PR needs to be +3. You have updated the relevant documentation or written new docs. In case the PR needs to be merged without delay (e.g. for a high priority fix), open an issue for updating or - adding the tutorial later. + adding the documentation later. 4. You've added tests that cover the changes you've made, if relevant. 5. If your change has an end user facing impact (new feature, deprecation, removal, etc.), you've added or updated a reno release note for that change and tagged the PR @@ -374,7 +377,7 @@ You should complete or update the class documentation and method documentation f the destination location separated by single comma. # section: tutorial - Optional. Link to tutorial of this experiment if one exists. + Optional. Link to documentation of this experiment if one exists. # section: see_also Optional. You can list relevant experiment or module. @@ -534,14 +537,14 @@ qiskit_experiments/library/__init__.py Here the reference start with `~`. We only add experiment classes to the table of the entire library. -#### Updating the tutorials +#### Updating the documentation -Any change that would affect an existing tutorial or a new feature that requires a -tutorial should be updated correspondingly. Before updating a tutorial, review the -[existing tutorials](https://qiskit.org/documentation/experiments/tutorials/index.html) for their style and content, and read the [tutorial guidelines](docs/tutorials/GUIDELINES.md) - for further details. +Any change that would affect existing documentation, or a new feature that requires a +documentation, should be updated correspondingly. Before updating, review the +[existing documentation](https://qiskit.org/documentation/experiments) for their style and content, and read the [documentation guidelines](docs/GUIDELINES.md) +for further details. -Tutorials are written in reStructuredText format and then built into Jupyter notebooks. +Documentation is written in reStructuredText format and then built into Jupyter notebooks. Code cells can be written using `jupyter-execute` blocks, which will be automatically executed, with both code and output shown to the user: @@ -560,7 +563,9 @@ to the user, use the `:hide-code:` and `:hide-output:` directives: from qiskit.test.ibmq_mock import mock_get_backend backend = mock_get_backend('FakeLima') -To ignore an error from a Jupyter cell block, use the `:raises:` directive. +To display a block without actually executing the code, use the `.. jupyter-input::` +and `.. jupyter-output::` directives. To ignore an error from a Jupyter cell block, +use the `:raises:` directive. #### Building documentation locally To check what the rendered html output of the API documentation, tutorials, and release diff --git a/docs/GUIDELINES.md b/docs/GUIDELINES.md new file mode 100644 index 0000000000..6258932471 --- /dev/null +++ b/docs/GUIDELINES.md @@ -0,0 +1,97 @@ +# Guidelines for writing documentation + +Read the [contributing guidelines](CONTRIBUTING.md) before proceeding. +## Introduction + +Qiskit Experiments Documentation is split into four sections: + +- tutorials for learning the package from the ground up +- how-tos for solving specific problems +- guides for information on specific experiments +- API reference for technical documentation on + +Below we provide templates and guidelines for each of these types of documentation. + +### How-to recipe + +The title of a how-to should clearly describe what problem it's solving. It should be an action that follows "How to". The text itself has up to four sections, but only the first two are required: + +- Problem: This section should describe the user problem that your recipe is providing a direct solution for in second person. This should ideally be a one-liner so that users can quickly scan it and see if it’s relevant to what they’re trying to do. + +- Solution: This section should describe possible solutions for the problem with code snippets and text before and after that describe what is needed to run the code, as well as what it generates and how this solves the problem. + +- Discussion: This section can go into detail on when this kind of problem can arise, caveats to running the code, and any related. + +- See also: Links to other relevant documentation or resources. + +Here is a template for how-tos: + +``` +Write a how-to +=============== + +Problem +------- + +You want to write a how-to. + +Solution +-------- + +First, you need to have a specific problem in mind that you want to solve with your +how-to. This might be a problem you encountered when using Qiskit Experiments yourself, +for example. You then need to have a solution that you can describe with words and code +examples. + +Discussion +---------- + +Not every type of information is suitable for a how-to. For example, if it's essential +information that newcomers to the package should know, then it should go in the tutorials +section. + +Subsection +~~~~~~~~~~ + +You can add subsections whenever appropriate. + +See also +-------- + +* `The Qiskit Docs Guide `__ +``` + + + +### Experiment guides + +The main goal of `qiskit-experiment` experiment guides is to serve as user guides for +the various package components such as the characterization and calibration +experiments. To this end, each documentation should cover the main (if not all) use-cases +of the documented functionality, including code examples and expected outputs. +Another objective of the documentation is to provide the user with basic background +on each experiment method. Hence a good practice would +be to have in the beginning of the documentation a short background explanation, +preferably 1 or 2 paragraphs long which includes the main literature references +as well as a link to the relevant chapter in the Qiskit textbook, if available. See for example the +[Randomized Benchmarking](randomized_benchmarking.ipynb) documentation. + +Below are more concrete guidelines pertaining to various documentation aspects: + +## Formatting guidelines +* For experiments, documentation title should be just the name of the experiment. Use regular capitalization. +* For sub titles of how-to steps - use present progressive. E.e. "Saving exp data to the DB" (instead of "Save exp data to the DB") +* Use math notation as much as possible (e.g. use $\frac{\pi}{2}$ instead of pi-half or pi/2) +* Use headers, subheaders, subsubheaders etc. for hierarchical text organization. No need to number the headers +* Use device names as shown in the IBM Quantum Services dashboard, e.g. ibmq_lima instead of IBMQ Lima +* put identifier names (e.g. osc_freq) in code blocks using backticks, i.e. `osc_freq` + +## Content guidelines + +* First section should be a general explanation on the topic. Put 2-3 most relevant references (papers and Qiskit textbook) +* Cover the common use-cases of the documented functionality (e.g. experiment) +* For each use-case, provide an example output, such as console printings and plot figures +* Cover all the required and common params (e.g. experiment and analysis options) +* For an experiment documentation, cover using the experiment in a composite experiment setting + + diff --git a/docs/tutorials/GUIDELINES.md b/docs/tutorials/GUIDELINES.md deleted file mode 100644 index fdb127c6e5..0000000000 --- a/docs/tutorials/GUIDELINES.md +++ /dev/null @@ -1,39 +0,0 @@ -# Guidelines for writing tutorials - -First read the overall project contributing guidelines. These are all -included in the qiskit documentation: - -https://qiskit.org/documentation/contributing_to_qiskit.html - -## Introduction - -The main goal of `qiskit-experiment` tutorials is to serve as user guides for -the various package components such as the characterization and calibration -experiments. To this end each tutorial should cover the main (if not all) use-cases -of the documented functionality, including code examples and expected outputs. -Another objective of the tutorials is to provide the user with basic background -on each experiment method. Hence a good practice would -be to have in the beginning of the tutorial a short background explanation, -preferably 1 or 2 paragraphs long which includes the main literature references -as well as a link to the relevant chapter in the Qiskit textbook, if available. See for example the -[Randomized Benchmarking](randomized_benchmarking.ipynb) tutorial. - -Below are more concrete guidelines pertaining to various tutorial aspects: - -## Formatting guidelines -* For experiments, tutorial title should be just the name of the experiment. Use regular capitalization. -* For sub titles of how-to steps - use present progressive. E.e. "Saving exp data to the DB" (instead of "Save exp data to the DB") -* Use math notation as much as possible (e.g. use $\frac{\pi}{2}$ instead of pi-half or pi/2) -* Use headers, subheaders, subsubheaders etc. for hierarchical text organization. No need to number the headers -* Use device names as shown in the IBM Quantum Services dashboard, e.g. ibmq_lima instead of IBMQ Lima -* put identifier names (e.g. osc_freq) in code blocks using backticks, i.e. `osc_freq` - -## Content guidelines - -* First section should be a general explanation on the topic. Put 2-3 most relevant references (papers and Qiskit textbook) -* Cover the common use-cases of the documented functionality (e.g. experiment) -* For each use-case, provide an example output, such as console printings and plot figures -* Cover all the required and common params (e.g. experiment and analysis options) -* For an experiment tutorial, cover using the experiment in a composite experiment setting - - diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index f5551650e5..cc88cf4811 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -46,7 +46,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): Here, Gate is the name of the gate which will be repeated. The user can optionally add a square-root of X pulse before the gates are repeated. This square-root of X pulse allows the analysis to differentiate between over rotations and under rotations in the case of - pi-pulses. Importantly, the resulting data is analyzed by a fit to a cosine function in + :math:`\pi`-pulses. Importantly, the resulting data is analyzed by a fit to a cosine function in which we try to determine the over/under rotation given an intended rotation angle per gate which must also be specified by the user. @@ -54,7 +54,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): the equator of the Bloch sphere. This is why users should insert a square-root of X pulse before running calibrations for :math:`\pm\pi` rotations. When all data points are close to the equator, it is difficult for a fitter to infer the overall scale of the error. When - calibrating a :math:`pi` rotation, one can use ``add_xp_circuit = True`` to insert one + calibrating a :math:`\pi` rotation, one can use ``add_xp_circuit = True`` to insert one circuit that puts the qubit in the excited state to set the scale for the other circuits. Furthermore, when running calibrations for :math:`\pm\pi/2` rotations users are advised to use an odd number of repetitions, e.g. [1, 2, 3, 5, 7, ...] to ensure that the ideal @@ -86,7 +86,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): .. ref_arxiv:: 1 1504.06597 # section: tutorial - :doc:`/guides/calibrations` + :ref:`fine-amplitude-cal` """ diff --git a/requirements-dev.txt b/requirements-dev.txt index 7dcc5b06ef..88e54c0cfa 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,11 +3,11 @@ stestr astroid==2.5 pylint==2.7.1 jinja2==3.0.3 -sphinx==5.2.3 +sphinx~=4.0 jupyter-sphinx>=0.4.0 qiskit-sphinx-theme>=1.10.2 sphinx-autodoc-typehints<=1.20.2 -sphinx-design +sphinx-design==0.3.0 pygments>=2.4 reno>=3.4.0 nbsphinx diff --git a/tox.ini b/tox.ini index 2e22ce4bc3..444c9346ca 100644 --- a/tox.ini +++ b/tox.ini @@ -61,14 +61,14 @@ commands = black {posargs} qiskit_experiments test tools setup.py [testenv:docs] passenv = EXPERIMENTS_DEV_DOCS commands = - sphinx-build -b html {posargs} docs/ docs/_build/html + sphinx-build -j auto -T --keep-going -b -b html {posargs} docs/ docs/_build/html [testenv:docsnorst] passenv = EXPERIMENTS_DEV_DOCS setenv = QISKIT_DOCS_SKIP_RST = 1 commands = - sphinx-build -b html {posargs} docs/ docs/_build/html + sphinx-build -j auto -T --keep-going -b html {posargs} docs/ docs/_build/html [pycodestyle] max-line-length = 100 From 0980d6b7a1c8146b55f4eaf2777a66ca5e332799 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 2 Mar 2023 16:32:52 -0500 Subject: [PATCH 39/64] fix sphinx build command --- tox.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tox.ini b/tox.ini index 444c9346ca..66faeb7444 100644 --- a/tox.ini +++ b/tox.ini @@ -61,7 +61,7 @@ commands = black {posargs} qiskit_experiments test tools setup.py [testenv:docs] passenv = EXPERIMENTS_DEV_DOCS commands = - sphinx-build -j auto -T --keep-going -b -b html {posargs} docs/ docs/_build/html + sphinx-build -j auto -T --keep-going -b html {posargs} docs/ docs/_build/html [testenv:docsnorst] passenv = EXPERIMENTS_DEV_DOCS From ca7c937de340fcedb422f82fc42281906ae7a830 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 2 Mar 2023 18:13:30 -0500 Subject: [PATCH 40/64] force utf-8 output encoding --- docs/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index bc02b97c0d..d1dd2d7b7c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -149,6 +149,8 @@ # html_theme = "qiskit_sphinx_theme" # use the theme in subdir 'theme' +html_output_encoding = "utf-8" + html_context = { "analytics_enabled": True, "expandable_sidebar": True, From 89f714c0df18bec063c7849787007b4ccecc8d49 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Fri, 3 Mar 2023 16:46:01 -0500 Subject: [PATCH 41/64] restore sphinx version and remove some unnecessary template files --- docs/_static/style.css | 23 ----------------------- docs/_templates/theme_variables.jinja | 9 --------- docs/conf.py | 5 +---- requirements-dev.txt | 2 +- 4 files changed, 2 insertions(+), 37 deletions(-) delete mode 100644 docs/_static/style.css delete mode 100644 docs/_templates/theme_variables.jinja diff --git a/docs/_static/style.css b/docs/_static/style.css deleted file mode 100644 index 9735898477..0000000000 --- a/docs/_static/style.css +++ /dev/null @@ -1,23 +0,0 @@ -.wy-nav-content { - max-width: 90% !important; -} - -.wy-side-scroll { - background: #8c8c8c; -} - -.pre { - color: #BE8184; -} - -.sd-card-img-top, -.sd-card img { - margin-left: auto; - margin-right: auto; - width: auto; - height: 100%; -} - -.sd-card-header .sd-card-text { - font-weight: bold; -} \ No newline at end of file diff --git a/docs/_templates/theme_variables.jinja b/docs/_templates/theme_variables.jinja deleted file mode 100644 index f2c185bbe4..0000000000 --- a/docs/_templates/theme_variables.jinja +++ /dev/null @@ -1,9 +0,0 @@ -{%- set external_urls = { - 'github': 'https://github.com/Qiskit/qiskit-experiments', - 'docs': 'https://qiskit.org/documentation/', - 'slack': 'https://qiskit.slack.com', - 'home': 'https://qiskit.org/', - 'resources': 'https://qiskit.org/learn', - 'tutorials': 'https://qiskit.org/documentation/experiments/tutorials/index.html', -} --%} diff --git a/docs/conf.py b/docs/conf.py index d1dd2d7b7c..ece071b60c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -77,9 +77,8 @@ "autodoc_visualization", "jupyter-execute-checkenv", ] -html_static_path = ["_static"] templates_path = ["_templates"] -html_css_files = ["style.css", "custom.css", "gallery.css"] +html_css_files = ["custom.css", "gallery.css"] nbsphinx_timeout = 360 nbsphinx_execute = os.getenv("QISKIT_DOCS_BUILD_TUTORIALS", "never") @@ -149,8 +148,6 @@ # html_theme = "qiskit_sphinx_theme" # use the theme in subdir 'theme' -html_output_encoding = "utf-8" - html_context = { "analytics_enabled": True, "expandable_sidebar": True, diff --git a/requirements-dev.txt b/requirements-dev.txt index 88e54c0cfa..0da406279d 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,7 +3,7 @@ stestr astroid==2.5 pylint==2.7.1 jinja2==3.0.3 -sphinx~=4.0 +sphinx~=5.0 jupyter-sphinx>=0.4.0 qiskit-sphinx-theme>=1.10.2 sphinx-autodoc-typehints<=1.20.2 From aff707998df5fa2fb3503570a6ad0a542892ed3b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Fri, 3 Mar 2023 16:47:48 -0500 Subject: [PATCH 42/64] updated links --- docs/index.rst | 9 +++++---- docs/tutorials/custom_experiment.rst | 2 +- docs/tutorials/getting_started.rst | 2 +- docs/tutorials/visualization.rst | 2 +- 4 files changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index bf5acb95e0..aa2586371c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -49,7 +49,7 @@ We've divided up the documentation into four sections with different purposes: These individual how-to recipes provide short and direct solutions to some commonly asked questions for Qiskit Experiments users. You'll find in these recipes: - * How to :doc:`instantiate experiment data for an existing experiment ` + * How to :doc:`re-instantiate experiment data for an existing experiment ` * How to :doc:`customize the splitting of circuits into jobs ` +++ @@ -69,7 +69,8 @@ We've divided up the documentation into four sections with different purposes: These are in-depth guides to key experiments in the package, describing their background, principle, and how to run them in Qiskit Experiments. You'll find in these guides: - * What is :doc:`randomized benchmarking ` and when is it useful + * How to analyze 1- and 2-qubit errors in :doc:`randomized benchmarking ` + * How to calculate the speedup from using :doc:`restless measurements ` +++ @@ -90,8 +91,8 @@ We've divided up the documentation into four sections with different purposes: Qiskit Experiments and how to use them, suitable for those working closely with specific parts of the package or writing your custom code. You'll find in these references: - * What are all possible input parameters to the :class:`.CurveAnalysis` class - + * Parameters, attributes, and methods of the :class:`.BaseExperiment` class + * Default experiment, transpile, and run options for the :class:`.T1` experiment +++ .. button-ref:: apidocs/index diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 40ec08c4bb..0a0e03147b 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -3,7 +3,7 @@ Writing your own experiment Qiskit Experiments is designed to be easily customizable. If you would like to run an experiment that's similar to an existing experiment in the -:doc:`library `, you can subclass the existing experiment and analysis +:doc:`library `, you can subclass the existing experiment and analysis classes. You can also write your own experiment class from the ground up by subclassing the :class:`.BaseExperiment` class. We will discuss both cases in this tutorial. diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 41e2231ada..999c6f0134 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -137,7 +137,7 @@ and analysis, respectively: print(exp_data.analysis_status()) Once the analysis is complete, figures are retrieved using the :meth:`~.ExperimentData.figure` method. See the -:doc:`visualization` tutorial on how to customize figures for an experiment. For our :math:`T_1` +:doc:`visualization module ` tutorial on how to customize figures for an experiment. For our :math:`T_1` experiment, we have a single figure showing the raw data and fit to the exponential decay model of the :math:`T_1` experiment: diff --git a/docs/tutorials/visualization.rst b/docs/tutorials/visualization.rst index d8fd0241cd..cd32209d01 100644 --- a/docs/tutorials/visualization.rst +++ b/docs/tutorials/visualization.rst @@ -301,4 +301,4 @@ overriding the :meth:`~.BasePlotter._default_figure_options` method in your plot See also -------- -API documentation: :doc:`/apidocs/visualization` \ No newline at end of file +API documentation: :doc:`Visualization Module ` \ No newline at end of file From cdbc362001cd1222a7e8242204d82a2f128cb4d0 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sun, 5 Mar 2023 01:57:28 -0500 Subject: [PATCH 43/64] fixed override warning and tutorial updates --- docs/_ext/jupyter-execute-checkenv.py | 2 +- docs/guides/state_tomography.rst | 11 +- ...experiment.rst => t1_characterization.rst} | 0 docs/tutorials/calibrations.rst | 220 ++++++++---------- qiskit_experiments/visualization/__init__.py | 14 +- 5 files changed, 116 insertions(+), 131 deletions(-) rename docs/guides/{t1_experiment.rst => t1_characterization.rst} (100%) diff --git a/docs/_ext/jupyter-execute-checkenv.py b/docs/_ext/jupyter-execute-checkenv.py index 6e8e75047d..fe123115b5 100644 --- a/docs/_ext/jupyter-execute-checkenv.py +++ b/docs/_ext/jupyter-execute-checkenv.py @@ -32,4 +32,4 @@ def run(self): def setup(app: Sphinx): - app.add_directive("jupyter-execute", JupyterCellCheckEnv) + app.add_directive("jupyter-execute", JupyterCellCheckEnv, override=True) diff --git a/docs/guides/state_tomography.rst b/docs/guides/state_tomography.rst index c0f17d7c4c..89ec10a121 100644 --- a/docs/guides/state_tomography.rst +++ b/docs/guides/state_tomography.rst @@ -17,9 +17,10 @@ Quantum State Tomography State Tomography Experiment --------------------------- -To run a state tomography experiment, we initialize the experiment with a -circuit to prepare the state to be measured. We can also pass in an -``Operator``, or a ``Statevector`` to describe the preparation circuit. +To run a state tomography experiment, we initialize the experiment with a circuit to +prepare the state to be measured. We can also pass in an +:class:`~qiskit.quantum_info.Operator`, or a :class:`~qiskit.quantum_info.Statevector` +to describe the preparation circuit. .. jupyter-execute:: @@ -54,8 +55,12 @@ The main result for tomography is the fitted state, which is stored as a state_result = qstdata1.analysis_results("state") print(state_result.value) +We can also visualize the density matrix: +.. jupyter-execute:: + from qiskit.visualization import plot_state_city + plot_state_city(qstdata1.analysis_results("state").value, title='Density Matrix') The state fidelity of the fitted state with the ideal state prepared by the input circuit is stored in the ``"state_fidelity"`` result field. diff --git a/docs/guides/t1_experiment.rst b/docs/guides/t1_characterization.rst similarity index 100% rename from docs/guides/t1_experiment.rst rename to docs/guides/t1_characterization.rst diff --git a/docs/tutorials/calibrations.rst b/docs/tutorials/calibrations.rst index d441620cee..9021a6887e 100644 --- a/docs/tutorials/calibrations.rst +++ b/docs/tutorials/calibrations.rst @@ -1,22 +1,23 @@ Calibrations: Schedules and gate parameters from experiments ============================================================ -To produce high fidelity quantum operations, we want to be able to run good gates. The -calibration module in Qiskit Experiments allows users to run experiments to find the -pulse shapes and parameter values that maximize the fidelity of the resulting quantum -operations. Calibration experiments encapsulate the internal processes and allow -experimenters to perform calibration operations in a quicker way. Without the experiments -module, we would need to define pulse schedules and plot the resulting measurement -data manually. - -In this tutorial, we demonstrate how to calibrate single-qubit gates using the -calibration framework in Qiskit Experiments. We will run experiments on our test pulse -backend, :class:`.SingleTransmonTestBackend`, a backend that simulates the underlying pulses -with `Qiskit Dynamics `_ on a -three-level model of a transmon. You can run these experiments on any real backend with -Pulse enabled (see :external+qiskit:doc:`tutorials/circuits_advanced/08_gathering_system_information`). - -. We will run experiments to +To produce high fidelity quantum operations, we want to be able to run good gates. The +calibration module in Qiskit Experiments allows users to run experiments to find the +pulse shapes and parameter values that maximize the fidelity of the resulting quantum +operations. Calibration experiments encapsulate the internal processes and allow +experimenters to perform calibration operations in a quicker way. Without the +experiments module, we would need to define pulse schedules and plot the resulting +measurement data manually. + +In this tutorial, we demonstrate how to calibrate single-qubit gates using the +calibration framework in Qiskit Experiments. We will run experiments on our test pulse +backend, :class:`.SingleTransmonTestBackend`, a backend that simulates the underlying +pulses with `Qiskit Dynamics `_ on a +three-level model of a transmon. You can also run these experiments on any real backend +with Pulse enabled (see +:external+qiskit:doc:`tutorials/circuits_advanced/08_gathering_system_information`). + +We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses, and choose the value of the DRAG parameter that minimizes leakage. The calibration framework requires the user to: @@ -121,7 +122,7 @@ we change the default value of the pulse duration to 320 samples print(cals.get_inst_map()) # check the new cals's InstructionScheduleMap made from the library print(cals.get_schedule('x',(0,))) # check one of the schedules built from the new calibration -We are going to run the spectroscopy, Rabi, DRAG, and fine-amplitude calibration experiments +We are going to run the spectroscopy, Rabi, DRAG, and fine amplitude calibration experiments one after another and update the parameters after every experiment, keeping track of parameter values. @@ -142,16 +143,26 @@ We see this in the table below as an empty tuple ``()`` in the qubits column. Observe that the parameter values of ``y`` do not appear in this table as they are given by the values of ``x``. .. jupyter-execute:: + :hide-code: + :hide-output: - columns_to_show = ["parameter", "qubits", "schedule", "value", "date_time"] + # dataframe styling + pd.set_option('display.precision', 5) + pd.set_option('display.html.border', 1) + pd.set_option('display.max_colwidth', 24) + +.. jupyter-execute:: + + columns_to_show = ["parameter", "qubits", "schedule", "value", "date_time"] pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))[columns_to_show] +Instantiate the experiment and draw the first circuit in the sweep: .. jupyter-execute:: freq01_estimate = backend.defaults().qubit_freq_est[qubit] - frequencies = np.linspace(freq01_estimate -15e6, freq01_estimate + 15e6, 51) - spec = RoughFrequencyCal(qubit, cals, frequencies, backend=backend) + frequencies = np.linspace(freq01_estimate-15e6, freq01_estimate+15e6, 51) + spec = RoughFrequencyCal([qubit], cals, frequencies, backend=backend) spec.set_experiment_options(amp=0.005) .. jupyter-execute:: @@ -159,10 +170,14 @@ Observe that the parameter values of ``y`` do not appear in this table as they a circuit = spec.circuits()[0] circuit.draw(output="mpl") +We can also visualize the pulse schedule for the circuit: + .. jupyter-execute:: - next(iter(circuit.calibrations["Spec"].values())).draw() # let's check the schedule - + next(iter(circuit.calibrations["Spec"].values())).draw() + circuit.calibrations["Spec"] + +Run the calibration experiment: .. jupyter-execute:: @@ -176,9 +191,9 @@ Observe that the parameter values of ``y`` do not appear in this table as they a The instance of ``calibrations`` has been automatically updated with the measured -frequency, as shown below. -In addition to the columns shown below, the calibrations also store the group to which a value belongs, -whether a values is valid or not and the experiment id that produce a value. +frequency, as shown below. In addition to the columns shown below, ``calibrations`` also +store the group to which a value belongs, whether a values is valid or not and the +experiment id that produce a value. .. jupyter-execute:: @@ -251,7 +266,7 @@ Saving and loading calibrations The values of the calibrated parameters can be saved to a .csv file and reloaded at a later point in time. -.. jupyter-execute:: +.. jupyter-input:: cals.save(file_type="csv", overwrite=True, file_prefix="PulseBackend") @@ -261,7 +276,7 @@ Since the schedules are currently not stored we need to call our ``setup_cals`` or use a library to populate an instance of Calibrations with the template schedules. By contrast, the value of the parameters will be recovered from the file. -.. jupyter-execute:: +.. jupyter-input:: cals = Calibrations.from_backend(backend, library) cals.load_parameter_values(file_name="PulseBackendparameter_values.csv") @@ -278,8 +293,8 @@ Calibrating the value of the DRAG coefficient A Derivative Removal by Adiabatic Gate (DRAG) pulse is designed to minimize leakage and phase errors to a neighbouring transition. It is a standard pulse with an additional derivative component. It is designed to reduce the frequency spectrum of a -normal pulse near the :math:`|1> - |2>` transition, -reducing the chance of leakage to the :math:`|2>` state. +normal pulse near the :math:`|1\rangle - |2\rangle` transition, +reducing the chance of leakage to the :math:`|2\rangle` state. The optimal value of the DRAG parameter is chosen to minimize both leakage and phase errors resulting from the AC Stark shift. The pulse envelope is :math:`f(t)=\Omega_x(t)+j\beta\frac{\rm d}{{\rm d}t}\Omega_x(t)`. @@ -295,7 +310,7 @@ negative amplitude. .. jupyter-execute:: from qiskit_experiments.library import RoughDragCal - cal_drag = RoughDragCal(qubit, cals, backend=backend, betas=np.linspace(-20, 20, 25)) + cal_drag = RoughDragCal([qubit], cals, backend=backend, betas=np.linspace(-20, 20, 25)) cal_drag.set_experiment_options(reps=[3, 5, 7]) cal_drag.circuits()[5].draw(output='mpl') @@ -317,15 +332,14 @@ negative amplitude. Fine calibrations of a pulse amplitude -------------------------------------- -The amplitude of a pulse can be precisely calibrated using -error amplifying gate sequences. These gate sequences apply -the same gate a variable number of times. Therefore, if each gate -has a small error :math:`d\theta` in the rotation angle then -a sequence of :math:`n` gates will have a rotation error of :math:`n` * :math:`d\theta`. -The :class:`.FineAmplitude` experiment and its subclass experiments implements -these sequences to obtain the correction value of imperfect pulses. We will first examine -how to detect imperfect pulses using the characterization version of these experiments, -then update calibrations with a calibration experiment. +The amplitude of a pulse can be precisely calibrated using error amplifying gate +sequences. These gate sequences apply the same gate a variable number of times. +Therefore, if each gate has a small error :math:`d\theta` in the rotation angle then a +sequence of :math:`n` gates will have a rotation error of :math:`n` * :math:`d\theta`. +The :class:`.FineAmplitude` experiment and its subclass experiments implements these +sequences to obtain the correction value of imperfect pulses. We will first examine how +to detect imperfect pulses using the characterization version of these experiments, then +update calibrations with a calibration experiment. .. jupyter-execute:: @@ -340,13 +354,13 @@ then update calibrations with a calibration experiment. Detecting over- and under-rotated pulses ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -We now run the error amplifying experiments with our own pulse schedules -on which we purposefully add over- and under-rotations to observe their effects. -To do this, we create an instruction to schedule map which we populate with -the schedules we wish to work with. This instruction schedule map is then -given to the transpile options of the experiment so that -the Qiskit transpiler can attach the pulse schedules to the gates in the experiments. -We base all our pulses on the default :math:`X` pulse of :class:`.SingleTransmonTestBackend`. +We now run the error amplifying experiments with our own pulse schedules on which we +purposefully add over- and under-rotations to observe their effects. To do this, we +create an instruction to schedule map which we populate with the schedules we wish to +work with. This instruction schedule map is then given to the transpile options of the +experiment so that the Qiskit transpiler can attach the pulse schedules to the gates in +the experiments. We base all our pulses on the default :math:`X` pulse of +:class:`.SingleTransmonTestBackend`. .. jupyter-execute:: @@ -354,10 +368,10 @@ We base all our pulses on the default :math:`X` pulse of :class:`.SingleTransmon d0, inst_map = pulse.DriveChannel(qubit), pulse.InstructionScheduleMap() -We now take the ideal :math:`X` pulse amplitude reported by the backend and -add/subtract a 2% over/underrotation to it by scaling the ideal amplitude and see -if the experiment can detect this over/underrotation. We replace the default :math:`X` pulse -in the instruction schedule map with this over/under-rotated pulse. +We now take the ideal :math:`X` pulse amplitude reported by the backend and add/subtract +a 2% over/underrotation to it by scaling the ideal amplitude and see if the experiment +can detect this over/underrotation. We replace the default :math:`X` pulse in the +instruction schedule map with this over/under-rotated pulse. .. jupyter-execute:: @@ -371,27 +385,28 @@ in the instruction schedule map with this over/under-rotated pulse. pulse.play(pulse.Drag(x_pulse.duration, over_amp, x_pulse.sigma, x_pulse.beta), d0) inst_map.add("x", (qubit,), x_over) -Let's look at one of the circuits of the :class:`.FineXAmplitude` experiment. -To calibrate the :math:`X` gate, we add an :math:`SX` gate before the :math:`X` gates to move the ideal population -to the equator of the Bloch sphere where the sensitivity to over/under rotations is the highest. +Let's look at one of the circuits of the :class:`.FineXAmplitude` experiment. To +calibrate the :math:`X` gate, we add an :math:`SX` gate before the :math:`X` gates to +move the ideal population to the equator of the Bloch sphere where the sensitivity to +over/under rotations is the highest. .. jupyter-execute:: - overamp_cal = FineXAmplitude(qubit, backend=backend) - overamp_cal.set_transpile_options(inst_map=inst_map) - overamp_cal.circuits()[4].draw(output='mpl') + overamp_exp = FineXAmplitude(qubit, backend=backend) + overamp_exp.set_transpile_options(inst_map=inst_map) + overamp_exp.circuits()[4].draw(output='mpl') .. jupyter-execute:: # do the experiment - exp_data_over = overamp_cal.run(backend).block_for_results() + exp_data_over = overamp_exp.run(backend).block_for_results() exp_data_over.figure(0) -The ping-pong pattern on the figure indicates an over-rotation which makes the initial state -rotate more than :math:`\pi`. +The ping-pong pattern on the figure indicates an over-rotation which makes the initial +state rotate more than :math:`\pi`. -We now look at a pulse with an under rotation to see how the :class:`.FineXAmplitude` experiment -detects this error. We will compare the results to the over-rotation above. +We now look at a pulse with an under rotation to see how the :class:`.FineXAmplitude` +experiment detects this error. We will compare the results to the over-rotation above. .. jupyter-execute:: @@ -401,18 +416,17 @@ detects this error. We will compare the results to the over-rotation above. inst_map.add("x", (qubit,), x_under) # do the experiment - underamp_cal = FineXAmplitude(qubit, backend=backend) - underamp_cal.set_transpile_options(inst_map=inst_map) + underamp_exp = FineXAmplitude(qubit, backend=backend) + underamp_exp.set_transpile_options(inst_map=inst_map) - exp_data_under = underamp_cal.run(backend).block_for_results() + exp_data_under = underamp_exp.run(backend).block_for_results() exp_data_under.figure(0) -Similarly to the over-rotation, the under-rotated pulse creates -qubit populations that do not lie on the equator of the Bloch sphere. -However, compared to the ping-pong pattern of the over rotated pulse, -the under rotated pulse produces an inverted ping-pong pattern. -This allows us to determine not only the magnitude of the rotation error -but also its sign. +Similarly to the over-rotation, the under-rotated pulse creates qubit populations that +do not lie on the equator of the Bloch sphere. However, compared to the ping-pong +pattern of the over rotated pulse, the under rotated pulse produces an inverted +ping-pong pattern. This allows us to determine not only the magnitude of the rotation +error but also its sign. .. jupyter-execute:: @@ -432,27 +446,20 @@ Calibrating a :math:`\pi`/2 :math:`X` pulse ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Now we apply the same principles to a different example using the calibration version of -a Fine Amplitude experiment. -The amplitude of the :math:`SX` gate, which is an :math:`X` pulse with half the amplitude, -is calibrated with the :class:`.FineSXAmplitudeCal` experiment. -Unlike the :class:`.FineSXAmplitude` experiment, the :class:`.FineSXAmplitudeCal` experiment -does not require other gates than the :math:`SX` gate since the number of repetitions -can be chosen such that the ideal population is always on the equator of the -Bloch sphere. -To demonstrate the :class:`.FineSXAmplitudeCal` experiment, we now create a :math:`SX` pulse by -dividing the amplitude of the X pulse by two. -We expect that this pulse might have a small rotation error which we want to correct. +a Fine Amplitude experiment. The amplitude of the :math:`SX` gate, which is an :math:`X` +pulse with half the amplitude, is calibrated with the :class:`.FineSXAmplitudeCal` +experiment. Unlike the :class:`.FineSXAmplitude` experiment, the +:class:`.FineSXAmplitudeCal` experiment does not require other gates than the :math:`SX` +gate since the number of repetitions can be chosen such that the ideal population is +always on the equator of the Bloch sphere. To demonstrate the +:class:`.FineSXAmplitudeCal` experiment, we create a :math:`SX` pulse by dividing the +amplitude of the X pulse by two. We expect that this pulse might have a small rotation +error which we want to correct. .. jupyter-execute:: from qiskit_experiments.library import FineSXAmplitudeCal - # build sx_pulse with the default x_pulse from defaults and add it to the InstructionScheduleMap - sx_pulse = pulse.Drag(x_pulse.duration, 0.5*x_pulse.amp, x_pulse.sigma, x_pulse.beta, name="SXp_d0") - with pulse.build(name='sx') as sched: - pulse.play(sx_pulse,d0) - inst_map.add("sx", (qubit,), sched) - amp_cal = FineSXAmplitudeCal([qubit], cals, backend=backend, schedule_name="sx") amp_cal.circuits()[4].draw(output="mpl") @@ -460,53 +467,26 @@ Let's run the calibration experiment: .. jupyter-execute:: - amp_cal.set_transpile_options(inst_map=inst_map) exp_data_x90p = amp_cal.run().block_for_results() exp_data_x90p.figure(0) -From the analysis result, we can see that there is a small rotation error. - -.. jupyter-execute:: - - # check how much more the given sx_pulse makes over or under roatation - print(exp_data_x90p.analysis_results("d_theta")) - target_angle = np.pi / 2 - dtheta = exp_data_x90p.analysis_results("d_theta").value.nominal_value - scale = target_angle / (target_angle + dtheta) - print(f"The ideal angle is {target_angle:.2f} rad. We measured a deviation of {dtheta:.3f} rad.") - print(f"Thus, scale the {sx_pulse.amp:.4f} pulse amplitude by {scale:.3f} to obtain {sx_pulse.amp*scale:.5f}.") - -Let's change the amplitude of the SX pulse by a factor :math:`\pi/2 / (\pi/2 + d\theta)` -to turn it into a sharp :math:`\pi/2` rotation. +Observe, once again, that the calibrations have automatically been updated. .. jupyter-execute:: - pulse_amp = sx_pulse.amp*scale - - with pulse.build(backend=backend, name="sx") as sx_new: - pulse.play(pulse.Drag(x_pulse.duration, pulse_amp, x_pulse.sigma, x_pulse.beta), d0) - - inst_map.add("sx", (qubit,), sx_new) - inst_map.get('sx',(qubit,)) - - # do the experiment - data_x90p = amp_cal.run().block_for_results() - data_x90p.figure(0) - -You can now see that the correction to the pulse amplitude has allowed us -to improve our :math:`SX` gate as shown by the analysis result below. + pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] .. jupyter-execute:: - # check the dtheta - print(data_x90p.analysis_results("d_theta")) + cals.get_schedule("sx", qubit) -Observe, once again, that the calibrations have automatically been updated. +If we run the experiment again, we expect to see that the updated calibrated gate will +have a smaller :math:`d\theta` error: .. jupyter-execute:: - pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()], parameters="amp"))[columns_to_show] - + exp_data_x90p_rerun = amp_cal.run().block_for_results() + exp_data_x90p_rerun.figure(0) See also -------- diff --git a/qiskit_experiments/visualization/__init__.py b/qiskit_experiments/visualization/__init__.py index 5e49aa1e85..682f77bcb1 100644 --- a/qiskit_experiments/visualization/__init__.py +++ b/qiskit_experiments/visualization/__init__.py @@ -16,13 +16,13 @@ .. currentmodule:: qiskit_experiments.visualization -Visualization provides plotting functionality for creating figures from experiment and -analysis results. This includes plotter and drawer classes to plot data in -class:`.CurveAnalysis` and its subclasses. Plotters inherit from :class:`BasePlotter` -and define a type of figure that may be generated from experiment or analysis data. For -example, the results from :class:`CurveAnalysis` --- or any other experiment where -results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted -using the :class:`CurvePlotter` class, which plots X-Y-like values. +The visualization module provides plotting functionality for creating figures from +experiment and analysis results. This includes plotter and drawer classes to plot data +in :class:`.CurveAnalysis` and its subclasses. Plotters inherit from +:class:`BasePlotter` and define a type of figure that may be generated from experiment +or analysis data. For example, the results from :class:`CurveAnalysis`---or any other +experiment where results are plotted against a single parameter (i.e., :math:`x`)---can +be plotted using the :class:`CurvePlotter` class, which plots X-Y-like values. These plotter classes act as a bridge (from the common bridge pattern in software development) between analysis classes (or even users) and plotting backends such as From 534f2843493c94ec76a093d75cc9305023ed5324 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sun, 5 Mar 2023 01:58:13 -0500 Subject: [PATCH 44/64] added experiment icons and copy button --- docs/_static/custom.css | 27 --------------------------- docs/conf.py | 19 +++++++++++++++++-- docs/tutorials/custom_experiment.rst | 2 +- requirements-dev.txt | 5 +++-- 4 files changed, 21 insertions(+), 32 deletions(-) delete mode 100644 docs/_static/custom.css diff --git a/docs/_static/custom.css b/docs/_static/custom.css deleted file mode 100644 index 3f981b6581..0000000000 --- a/docs/_static/custom.css +++ /dev/null @@ -1,27 +0,0 @@ -.toggle .header { - display: block; - clear: both; - background-color: #785EF0; - color: #f9f9f9; - height: 40px; - padding-top: 10px; - padding-left: 5px; - margin-bottom: 20px; -} - -.toggle .header:before { - float: left; - content: "▶ "; - font-size: 20px; - -} - -.toggle .header.open:before { - float: left; - content: "▼ "; - font-size: 20px; -} - -.toggle{ - background: #FBFBFB; -} diff --git a/docs/conf.py b/docs/conf.py index ece071b60c..24d287916a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -31,6 +31,7 @@ sys.path.append(os.path.abspath("./_ext")) sys.path.append(os.path.abspath("..")) + """ Sphinx documentation builder """ @@ -65,6 +66,7 @@ "sphinx.ext.mathjax", "sphinx.ext.viewcode", "sphinx.ext.extlinks", + "sphinx_copybutton", "jupyter_sphinx", "sphinx_autodoc_typehints", "reno.sphinxext", @@ -78,13 +80,26 @@ "jupyter-execute-checkenv", ] templates_path = ["_templates"] -html_css_files = ["custom.css", "gallery.css"] +html_css_files = ["gallery.css"] nbsphinx_timeout = 360 nbsphinx_execute = os.getenv("QISKIT_DOCS_BUILD_TUTORIALS", "never") nbsphinx_widgets_path = "" exclude_patterns = ["_build", "**.ipynb_checkpoints"] -nbsphinx_thumbnails = {} + +# Thumbnails for experiment guides from output images +nbsphinx_thumbnails = { + "guides/quantum_volume": "_images/quantum_volume_2_0.png", + "guides/readout_mitigation": "_images/readout_mitigation_4_0.png", + "guides/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", + "guides/restless_measurements": "_images/restless_shots.png", + "guides/state_tomography": "_images/state_tomography_3_0.png", + "guides/t1_characterization": "_images/t1_characterization_0_0.png", + "guides/t2ramsey_characterization": "_images/t2ramsey_characterization_4_0.png", + "guides/tphi_characterization": "_images/tphi_characterization_8_0.png", + "guides/t2hahn_characterization": "_images/t2hahn_characterization_5_0.png", +} + # Add `data keys` and `style parameters` alias. Needed for `expected_*_data_keys` methods in # visualization module and `default_style` method in `PlotStyle` respectively. diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index 0a0e03147b..cbc6f8c5c1 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -262,7 +262,7 @@ adding a measurement at the end only to the measurement qubits. Metadata contain the classical measurement register and the applied Pauli, and the each of the circuits to tell the analysis class how to restore the original results. To make restoration easier, we store Paulis in the -:class:`x symplectic form ` in ``metadata["rm_frame"]`` +:class:`x symplectic form ` in ``metadata["rm_sig"]`` so we know whether to apply a bit flip to each bit of the result (the phase is not important for our purposes). diff --git a/requirements-dev.txt b/requirements-dev.txt index 0da406279d..56de64bdd0 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -5,7 +5,7 @@ pylint==2.7.1 jinja2==3.0.3 sphinx~=5.0 jupyter-sphinx>=0.4.0 -qiskit-sphinx-theme>=1.10.2 +qiskit-sphinx-theme~=1.10 sphinx-autodoc-typehints<=1.20.2 sphinx-design==0.3.0 pygments>=2.4 @@ -19,4 +19,5 @@ cvxpy>=1.1.15 pylatexenc # Pin `importlib-metadata` because of a bug relating to version 5.0.0. See #931 for more. importlib-metadata==4.13.0;python_version<'3.8' -scikit-learn \ No newline at end of file +scikit-learn +sphinx-copybutton From 6479a20fe771bea497d503549ce9f0b1a753f273 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Sun, 5 Mar 2023 03:15:25 -0500 Subject: [PATCH 45/64] restored path --- docs/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 24d287916a..bc734f5526 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -79,6 +79,8 @@ "autodoc_visualization", "jupyter-execute-checkenv", ] + +html_static_path = ["_static"] templates_path = ["_templates"] html_css_files = ["gallery.css"] From fd381a62dcd67985d6b91c2304ad8194c5f33503 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 6 Mar 2023 09:17:21 -0500 Subject: [PATCH 46/64] fix attribute warnings Co-authored-by: Will Shanks --- docs/_templates/autosummary/class.rst | 4 +--- qiskit_experiments/data_processing/data_action.py | 12 ++++++------ .../visualization/plotters/base_plotter.py | 5 +++-- 3 files changed, 10 insertions(+), 11 deletions(-) diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst index e4d661a008..cb865ca173 100644 --- a/docs/_templates/autosummary/class.rst +++ b/docs/_templates/autosummary/class.rst @@ -20,9 +20,7 @@ .. autosummary:: :toctree: ../stubs/ {% for item in all_attributes %} - {%- if not item.startswith('_') %} - {{ name }}.{{ item }} - {%- endif -%} + .. autoattribute:: {{ item }} {%- endfor %} {% endif %} {% endblock %} diff --git a/qiskit_experiments/data_processing/data_action.py b/qiskit_experiments/data_processing/data_action.py index a6491f2d1e..1c17388772 100644 --- a/qiskit_experiments/data_processing/data_action.py +++ b/qiskit_experiments/data_processing/data_action.py @@ -105,14 +105,14 @@ class TrainableDataAction(DataAction): .. note:: The parameters of trainable nodes computed during training should be listed - in the class method :meth:`._default_parameters`. These parameters - are initialized at construction time and serialized together with the - constructor arguments. All parameters defined in - :meth:`._default_parameters` should be assigned a `None` value to - indicate that the node has not been trained. + in the class method :meth:`.TrainableDataAction._default_parameters`. + These parameters are initialized at construction time and serialized + together with the constructor arguments. All parameters defined in + :meth:`.TrainableDataAction._default_parameters` should be assigned a + `None` value to indicate that the node has not been trained. Parameter values can be updated with the :meth:`.set_parameters` method - and refer to using the :meth:`.parameters` method. + and refer to using the :meth:`.TrainableDataAction.parameters` method. This is required to correctly JSON serialize and deserialize a trainable node with parameters set during training. """ diff --git a/qiskit_experiments/visualization/plotters/base_plotter.py b/qiskit_experiments/visualization/plotters/base_plotter.py index 7eb70bd424..c337adc2e1 100644 --- a/qiskit_experiments/visualization/plotters/base_plotter.py +++ b/qiskit_experiments/visualization/plotters/base_plotter.py @@ -122,6 +122,9 @@ class BasePlotter(ABC): # plotter.drawer.figure_options.unknown_variable # Raises an error as it # does not exist in # `drawer.figure_options`. + + Attributes: + drawer (BaseDrawer): The drawer to use when plotting. """ def __init__(self, drawer: BaseDrawer): @@ -145,9 +148,7 @@ def __init__(self, drawer: BaseDrawer): # Figure options that have changed, for serialization. self._set_figure_options = set() - # The drawer backend to use for plotting. Docstring provided as drawer is not a @property. self.drawer: BaseDrawer = drawer - """The drawer to use when plotting.""" @property def supplementary_data(self) -> Dict[str, Any]: From edee743dd398cd0da78b26169b9d7a8c28594483 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Mon, 6 Mar 2023 15:17:43 -0500 Subject: [PATCH 47/64] updated contributor guidelines Finished updating new guidelines for docs, which are now fully contained in `GUIDELINES.md` and linked to from `CONTRIBUTING.md`. Also changed the `tutorial` heading in class docstrings to `guide`. --- .github/ISSUE_TEMPLATE/NEW_EXPERIMENT.md | 4 +- CONTRIBUTING.md | 367 +++------------- docs/GUIDELINES.md | 409 ++++++++++++++++-- docs/_ext/custom_styles/formatter.py | 6 +- docs/_ext/custom_styles/styles.py | 10 +- docs/guides/t1_characterization.rst | 4 +- docs/guides/t2hahn_characterization.rst | 27 +- .../library/calibration/rough_drag_cal.py | 2 +- .../characterization/cr_hamiltonian.py | 2 +- .../library/characterization/drag.py | 2 +- .../characterization/fine_amplitude.py | 2 +- .../library/characterization/rabi.py | 2 +- .../library/characterization/t2hahn.py | 2 +- .../library/characterization/t2ramsey.py | 2 +- .../library/characterization/tphi.py | 2 +- 15 files changed, 467 insertions(+), 376 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/NEW_EXPERIMENT.md b/.github/ISSUE_TEMPLATE/NEW_EXPERIMENT.md index 25f5a66516..d86e989221 100644 --- a/.github/ISSUE_TEMPLATE/NEW_EXPERIMENT.md +++ b/.github/ISSUE_TEMPLATE/NEW_EXPERIMENT.md @@ -17,7 +17,7 @@ your proposal. --> ## General details ### Experiment name - + ### Experiment type @@ -86,7 +86,7 @@ experiment data is displayed correctly in the results DB webpage - [ ] Add unit testing for the experiment and analysis classes. If needed implement a mock-backend for your experiment Include in your testing running the experiment in the context of `ParallelExperiment` - [ ] Write API docs for all your API methods. Follow the guideline [here](https://github.com/Qiskit/qiskit-experiments/blob/main/CONTRIBUTING.md) -- [ ] Write a tutorial for your experiment. Follow the guideline [here](https://github.com/Qiskit/qiskit-experiments/blob/main/docs/tutorials/GUIDELINES.md) +- [ ] Write a user guide for your experiment. Follow the guideline [here](https://github.com/Qiskit/qiskit-experiments/blob/main/docs/GUIDELINES.md) - [ ] Add a new release note. Follow the guideline [here](https://github.com/Qiskit/qiskit-experiments/blob/main/CONTRIBUTING.md#adding-a-new-release-note) - [ ] Ask for a final review for the implementation, documentation and testing - [ ] Celebrate! diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index efdcfa4976..0419621ff0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,33 +1,25 @@ # Contributing Guide To contribute to Qiskit Experiments, first read the overall [Qiskit project contributing -guidelines](https://qiskit.org/documentation/contributing_to_qiskit.html). +guidelines](https://qiskit.org/documentation/contributing_to_qiskit.html). In addition +to the general guidelines, the specific guidelines for contributing to Qiskit +Experiments are documented below. -## Contributing to Qiskit Experiments - -In addition to the general guidelines, the specific guidelines for contributing to -Qiskit Experiments are documented below. - -### Contents +Contents: - [Contributing Guide](#contributing-guide) - - [Contributing to Qiskit Experiments](#contributing-to-qiskit-experiments) - - [Contents](#contents) - [Proposing a new experiment](#proposing-a-new-experiment) - [Choosing an issue to work on](#choosing-an-issue-to-work-on) - [Pull request checklist](#pull-request-checklist) - - [Code style](#code-style) - [Testing your code](#testing-your-code) - [STDOUT/STDERR and logging capture](#stdoutstderr-and-logging-capture) + - [Code style](#code-style) - [Changelog generation](#changelog-generation) - [Release notes](#release-notes) - [Adding a new release note](#adding-a-new-release-note) - [Linking to issues](#linking-to-issues) - [Generating release notes](#generating-release-notes) - [Documentation](#documentation) - - [Experiment class documentation](#experiment-class-documentation) - - [Analysis class documentation](#analysis-class-documentation) - - [Populating the table of contents](#populating-the-table-of-contents) - [Updating the documentation](#updating-the-documentation) - [Building documentation locally](#building-documentation-locally) - [Adding deprecation warnings](#adding-deprecation-warnings) @@ -43,20 +35,24 @@ or equivalent source, with a use case that is of interest to the Qiskit and quan experimentalist community. If there is an experiment you would like to see added, you can propose it by creating a -[new experiment proposal issue](https://github.com/Qiskit/qiskit-experiments/issues/new?assignees=&labels=enhancement&template=NEW_EXPERIMENT.md&title=) in GitHub. The issue template will ask you to fill in -details about the experiment type, protocol, analysis, and implementation, which will -give us the necessary information to decide whether the experiment is feasible to -implement and useful to include in our package library. +[new experiment proposal +issue](https://github.com/Qiskit/qiskit-experiments/issues/new?assignees=&labels=enhancement&template=NEW_EXPERIMENT.md&title=) +in GitHub. The issue template will ask you to fill in details about the experiment type, +protocol, analysis, and implementation, which will give us the necessary information to +decide whether the experiment is feasible to implement and useful to include in our +package library. ### Choosing an issue to work on We use the following labels to help non-maintainers find issues best suited to their interests and experience level: -* [good first issue](https://github.com/Qiskit/qiskit-experiments/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) +* [good first + issue](https://github.com/Qiskit/qiskit-experiments/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) - these issues are typically the simplest available to work on, perfect for newcomers. They should already be fully scoped, with a clear approach outlined in the descriptions. -* [help wanted](https://github.com/Qiskit/qiskit-experiments/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) +* [help + wanted](https://github.com/Qiskit/qiskit-experiments/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) - these issues are generally more complex than good first issues. They typically cover work that core maintainers don't currently have capacity to implement and may require more investigation/discussion. These are a great option for experienced contributors @@ -68,9 +64,9 @@ When submitting a pull request for review, please ensure that: 1. The code follows the code style of the project and successfully passes the tests. 2. The API documentation has been updated accordingly. -3. You have updated the relevant documentation or written new docs. In case the PR needs to be - merged without delay (e.g. for a high priority fix), open an issue for updating or - adding the documentation later. +3. You have updated the relevant documentation or written new docs. In case the PR needs + to be merged without delay (e.g. for a high priority fix), open an issue for updating + or adding the documentation later. 4. You've added tests that cover the changes you've made, if relevant. 5. If your change has an end user facing impact (new feature, deprecation, removal, etc.), you've added or updated a reno release note for that change and tagged the PR @@ -78,24 +74,6 @@ When submitting a pull request for review, please ensure that: The sections below go into more detail on the guidelines for each point. -### Code style - -The qiskit-experiments repository uses `black` for code formatting and style and -`pylint` for linting. You can run these checks locally with - -``` -tox -elint -``` - -If there is a code formatting issue identified by black you can just run ``black`` -locally to fix this (or ``tox -eblack`` which will install it and run it). - -Because `pylint` analysis can be slow, there is also a `tox -elint-incr` target, which -only applies `pylint` to files which have changed from the source github. On rare -occasions this will miss some issues that would have been caught by checking the -complete source tree, but makes up for this by being much faster (and those rare -oversights will still be caught by the CI after you open a pull request). - ### Testing your code It is important to verify that your code changes don't break any existing tests and that @@ -114,8 +92,8 @@ specific python version such as 3.10: `tox -epy310`. If you just want to run a subset of tests you can pass a selection regex to the test runner. For example, if you want to run all tests that have "dag" in the test id you can -run: `tox -- dag`. You can pass arguments directly to the test runner after the -bare `--`. To see all the options on test selection you can refer to the stestr manual: +run: `tox -- dag`. You can pass arguments directly to the test runner after the bare +`--`. To see all the options on test selection you can refer to the stestr manual: https://stestr.readthedocs.io/en/stable/MANUAL.html#test-selection If you want to run a single test module, test class, or individual test method you can @@ -147,12 +125,30 @@ to the tests run so output can be associated with the test case it originated fr However, if you run tests with `stestr` outside of these mechanisms, by default the streams are not captured. To enable stream capture, just set the `QISKIT_TEST_CAPTURE_STREAMS` env variable to `1`. If this environment variable is set -outside of running with `stestr`, the streams (STDOUT, STDERR, and logging) will still be -captured but **not** displayed in the test runners output. If you are using the stdlib -unittest runner, a similar result can be accomplished by using the +outside of running with `stestr`, the streams (STDOUT, STDERR, and logging) will still +be captured but **not** displayed in the test runners output. If you are using the +stdlib unittest runner, a similar result can be accomplished by using the [`--buffer`](https://docs.python.org/3/library/unittest.html#command-line-options) option (e.g. `python -m unittest discover --buffer ./test/python`). +### Code style + +The qiskit-experiments repository uses `black` for code formatting and style and +`pylint` for linting. You can run these checks locally with + +``` +tox -elint +``` + +If there is a code formatting issue identified by black you can just run ``black`` +locally to fix this (or ``tox -eblack`` which will install it and run it). + +Because `pylint` analysis can be slow, there is also a `tox -elint-incr` target, which +only applies `pylint` to files which have changed from the source github. On rare +occasions this will miss some issues that would have been caught by checking the +complete source tree, but makes up for this by being much faster (and those rare +oversights will still be caught by the CI after you open a pull request). + ### Changelog generation The changelog is automatically generated as part of the release process automation. This @@ -163,8 +159,8 @@ merge) and checks if that PR had a `Changelog:` label on it. If there is a label add the git commit message summary line from the git log for the release to the changelog. -If there are multiple `Changelog:` tags on a PR, the git commit message summary line from -the git log will be used for each changelog category tagged. +If there are multiple `Changelog:` tags on a PR, the git commit message summary line +from the git log will be used for each changelog category tagged. The current categories for each label are as follows: @@ -280,7 +276,7 @@ example you would write a release note with a link to issue 12345 as: ```yaml fixes: - | - Fixes a race condition in the function ``foo()``. Refer to + Fixed a race condition in the function ``foo()``. Refer to `#12345 ` for more details. ``` @@ -303,269 +299,21 @@ At release time, ``reno report`` is used to generate the release notes for the r and the output will be submitted as a pull request to the documentation repository's [release notes file]( https://github.com/Qiskit/qiskit-experiments/blob/main/docs/release_notes.rst). + ### Documentation The [Qiskit Experiments documentation](https://qiskit.org/documentation/experiments/) is -rendered from experiment and analysis class docstrings into HTML files. We provide a -special syntax and macros as [Sphinx](https://www.sphinx-doc.org/en/master/) extensions -to format these docstrings. If you implement a new experiment or analysis or update how -an existing one functions, you should use following style so that the documentation is -formatted in the same manner throughout our experiment library. You can use standard -[reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html) -directives along with our syntax. - -#### Experiment class documentation - -You should complete or update the class documentation and method documentation for -`_default_experiment_options`. You can use several predefined sections for the class docstring. - -```buildoutcfg - """One line simple summary of this experiment. - - You can add more information after line feed. The first line will be shown in an - automatically generated table of contents on the module's top page. - This text block is not shown so you can keep the table clean. - - You can use following sections. The text within a section should be indented. - - # section: overview - Overview of the experiment. This information SHOULD be provided for every experiment. - This section covers technical aspect of experiment and explains how the experiment works. - - A diagram of typical quantum circuit that the experiment generates may help readers - to grasp the behavior of this experiment. - - # section: analysis_ref - You MUST provide a reference to the default analysis class in the base class. - This section is recursively referred by child classes if not explicitly given there. - Note that this is NOT reference nor import path of the class. - You should write the pass to the docstring, i.e. - - :py:class:`~qiskit_experiments.framework.BaseAnalysis` - - # section: warning - If user must take special care when using the experiment (e.g. API is not stabilized) - you should clarify in this section. - - # section: note - Optional. This comment is shown in a box so that the message is stood out. - - # section: example - Optional. You can write code example here. For example, - - .. code-block:: python - - exp = MyExperiment(qubits=[0, 1], backend=backend) - exp.run() - - This is effective especially when your experiment has complicated options. - - # section: reference - Optional. You can write reference to article or external website. - To write a reference to an arXiv work, you can use convenient macro. - - .. ref_arxiv:: Auth2020a 21xx.01xxx - - This collects the latest article information from web and automatically - generates a nicely formatted citation from the arXiv ID. - - For referring to the website, - - .. ref_website:: Qiskit Experiment Github, https://github.com/Qiskit/qiskit-experiments - - you can use the above macro, where you can provide a string for the hyperlink and - the destination location separated by single comma. - - # section: tutorial - Optional. Link to documentation of this experiment if one exists. - - # section: see_also - Optional. You can list relevant experiment or module. - Here you cannot write any comments. - You just need to list absolute paths to relevant API documents, i.e. - - qiskit_experiments.framework.BaseExperiment - qiskit_experiments.framework.BaseAnalysis - """ -``` - -You also need to provide the experiment option description in the `_default_experiment_options` method -if you add new options. This description will be automatically propagated through child classes, -so you don't need to manually copy documentation. -Of course, you can override documentation in the child class if it behaves differently there. - -```buildoutcfg - """Default experiment options. - - Experiment Options: - opt1 (int): Description of opt1. - opt2 (float): Description of opt2. - opt3 (List[SomeClass]): Description of opt3. - """ -``` - -Note that you should use the [Google docstring style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). -Numpy or other docstring styles cannot be parsed by our Sphinx extension, -and the section header should be named `Experiment Options` (NOT `Args`). -Since this is a private method, any other documentation besides option descriptions -are not rendered in the HTML documentation. Documentation for options are -automatically formatted and inserted into the class documentation. - -#### Analysis class documentation - -You can use the same syntax and section headers for the analysis class documentation. In addition, you can use extra sections, `fit_model` and `fit_parameters`, if needed. - -```buildoutcfg - """One line simple summary of this analysis. - - # section: overview - Overview of this analysis. - - # section: fit_model - Optional. If this analysis fits something, probably it is worth describing - the fit model. You can use math mode where latex commands are available. - - .. math:: - - F(x) = a\exp(x) + b - - It is recommended to omit `*` symbols for multiplication (looks ugly in math mode), - and you should carefully choose the parameter name so that symbols matches with - variable names shown in analysis results. You can write symbol :math:`a` here too. - - # section: fit_parameters - Optional. Description for fit parameters in the model. - You can also write how initial guess is generated and how fit bound is determined. - - defpar a: - desc: Amplitude. - init_guess: This is how :math:`a` is generated. No line feed. - bounds: [-1, 1] - - defpar b: - desc: Offset. - init_guess: This is how :math:`b` is generated. No line feed. - bounds: (0, 1] - - The defpar syntax is parsed and formatted nicely. - """ -``` - -You also need to provide a description for analysis class options in the -`_default_options` method. - -```buildoutcfg - """Default analysis options. - - Analysis Options: - opt1 (int): Description of opt1. - opt2 (float): Description of opt2. - opt3 (List[SomeClass]): Description of opt3. - """ -``` - -This is the same syntax with experiment options in the experiment class. -Note that header should be named `Analysis Options` to be parsed correctly. - -#### Populating the table of contents - -After you complete documentation of your classes, you must add documentation to the -toctree so that it can be rendered as the API documentation. In Qiskit Experiments, we -have a separate tables of contents for each experiment module (e.g. [characterization -experiments](https://qiskit.org/documentation/experiments/apidocs/mod_characterization.html)) -and for the [entire -library](https://qiskit.org/documentation/experiments/apidocs/library.html). Thus we -should add document to the tree of a particular module and then reference it to the -entire module. - -As an example, when writing the characterization experiment and analysis, first add your -documentation to the table of contents of the module: - -```buildoutcfg -qiskit_experiments/library/characterization/__init__.py - """ - .. currentmodule:: qiskit_experiments.library.characterization - - Experiments - =========== - .. autosummary:: - :toctree: ../stubs/ - :template: autosummary/experiment.rst - - MyExperiment1 - MyExperiment2 - - Analysis - ======== - - .. autosummary:: - :toctree: ../stubs/ - :template: autosummary/analysis.rst - - ... - """ - - from my_experiment import MyExperiment1, MyExperiment2 - from my_analysis import MyAnalysis -``` - -Note that there are different stylesheets, `experiment.rst` and `analysis.rst`, for the -experiment class and analysis class, respectively. Take care to place your documentation -under the correct stylesheet, otherwise it may not be rendered properly. Then the table -for the entire library should be written like this: - -```buildoutcfg -qiskit_experiments/library/__init__.py - - """ - .. currentmodule:: qiskit_experiments.library - - Characterization Experiments - ============================ - .. autosummary:: - :toctree: ../stubs/ - :template: autosummary/experiment.rst - - ~characterization.MyExperiment1 - ~characterization.MyExperiment2 - """ - - from .characterization import MyExperiment1, MyExperiment2 - from . import characterization -``` - -Here the reference start with `~`. We only add experiment classes to the table of the -entire library. +rendered from `.rst` files as well as experiment and analysis class docstrings into HTML +files. #### Updating the documentation Any change that would affect existing documentation, or a new feature that requires a -documentation, should be updated correspondingly. Before updating, review the -[existing documentation](https://qiskit.org/documentation/experiments) for their style and content, and read the [documentation guidelines](docs/GUIDELINES.md) -for further details. +documentation, should be updated correspondingly. Before updating, review the [existing +documentation](https://qiskit.org/documentation/experiments) for their style and +content, and read the [documentation guidelines](docs/GUIDELINES.md) for further +details. -Documentation is written in reStructuredText format and then built into Jupyter notebooks. -Code cells can be written using `jupyter-execute` blocks, which will be automatically -executed, with both code and output shown to the user: - - .. jupyter-execute:: - - # write Python code here - -Your code should use the appropriate mock backend to show what expected experiment -results might look like for the user. To instantiate a mock backend without exposing it -to the user, use the `:hide-code:` and `:hide-output:` directives: - - .. jupyter-execute:: - :hide-code: - :hide-output: - - from qiskit.test.ibmq_mock import mock_get_backend - backend = mock_get_backend('FakeLima') - -To display a block without actually executing the code, use the `.. jupyter-input::` -and `.. jupyter-output::` directives. To ignore an error from a Jupyter cell block, -use the `:raises:` directive. #### Building documentation locally To check what the rendered html output of the API documentation, tutorials, and release @@ -577,18 +325,25 @@ This will build all the documentation into `docs/_build/html`. The main page `index.html` will link to the relevant pages in the subdirectories, or you can navigate manually: +* `tutorials/`: Contains the built tutorials. +* `howtos/`: Contains the built how-tos. +* `guides/`: Contains the built experiment guides. * `apidocs/`: Contains the API docs automatically compiled from module docstrings. -* `tutorials/`: Contains the executed tutorials built from `.rst` files. * `release_notes.html`: Contains the release notes. -To build release notes and API docs without building the Jupyter cells in the `.rst` -files under `tutorials/`, which is a relatively slow process, you can run +If you encounter a build error involving `config-inited`, you need to be in the root of +the qiskit-experiments git repository then run `git remote add upstream +https://github.com/Qiskit/qiskit-experiments` and `git fetch upstream` before building. + +To build release notes and API docs without executing the Jupyter cells in the `.rst` +files, which is a relatively slow process, you can run tox -edocsnorst instead. ### Adding deprecation warnings + Qiskit Experiments is part of Qiskit and, therefore, the [Qiskit Deprecation Policy](https://qiskit.org/documentation/contributing_to_qiskit.html#deprecation-policy) fully applies here. We have a deprecation decorator for showing deprecation warnings. To diff --git a/docs/GUIDELINES.md b/docs/GUIDELINES.md index 6258932471..28b36080fc 100644 --- a/docs/GUIDELINES.md +++ b/docs/GUIDELINES.md @@ -1,26 +1,96 @@ # Guidelines for writing documentation Read the [contributing guidelines](CONTRIBUTING.md) before proceeding. + +Contents: +- [Guidelines for writing documentation](#guidelines-for-writing-documentation) + - [Introduction](#introduction) + - [General formatting guidelines](#general-formatting-guidelines) + - [Tutorials](#tutorials) + - [How-to recipes](#how-to-recipes) + - [Experiment guides](#experiment-guides) + - [API documentation](#api-documentation) + - [Experiment class documentation](#experiment-class-documentation) + - [Analysis class documentation](#analysis-class-documentation) + - [Populating the table of contents](#populating-the-table-of-contents) + ## Introduction -Qiskit Experiments Documentation is split into four sections: +Qiskit Experiments documentation is split into four sections: + +- Tutorials for learning the package from the ground up +- How-to recipes for solving specific problems +- Experiment guides for information on specific experiments +- API reference for technical documentation + +All documentation is written in reStructuredText format and then built into formatted +text by Sphinx. Code cells can be written using `jupyter-execute` blocks, which will be +automatically executed, with both code and output shown to the user: + + .. jupyter-execute:: + + # write Python code here + +Your code should use the appropriate mock backend to show what expected experiment +results might look like for the user. To instantiate a mock backend without exposing it +to the user, use the `:hide-code:` and `:hide-output:` directives: + + .. jupyter-execute:: + :hide-code: + :hide-output: -- tutorials for learning the package from the ground up -- how-tos for solving specific problems -- guides for information on specific experiments -- API reference for technical documentation on + from qiskit.test.ibmq_mock import mock_get_backend + backend = mock_get_backend('FakeLima') + +To display a block without actually executing the code, use the `.. jupyter-input::` and +`.. jupyter-output::` directives. To ignore an error from a Jupyter cell block, use the +`:raises:` directive. To see more options, consult the [Jupyter Sphinx documentation](https://jupyter-sphinx.readthedocs.io/en/latest/). + +### General formatting guidelines + +* For experiments, documentation title should be just the name of the experiment. Use + regular capitalization. +* Use headers, subheaders, subsubheaders etc. for hierarchical text organization. No + need to number the headers +* Use present progressive for subtitles, such as "Saving experiment data to the + database" instead of "Save experiment data to the database" +* Use math notation as much as possible (e.g. use $\frac{\pi}{2}$ instead of pi-half or + pi/2) +* Use device names as shown in the IBM Quantum Services dashboard, e.g. `ibmq_lima` + instead of IBMQ Lima +* put identifier names (e.g. osc_freq) in code blocks using double backticks, i.e. `osc_freq` Below we provide templates and guidelines for each of these types of documentation. -### How-to recipe +### Tutorials + +The learning tutorials are for users who are familiar with Python and Qiskit and new to +the Qiskit Experiments package. Here are what to keep in mind when writing and updating +tutorials: -The title of a how-to should clearly describe what problem it's solving. It should be an action that follows "How to". The text itself has up to four sections, but only the first two are required: +- Whenever possible, external resources should be linked to. For example, classes and + methods in Qiskit should be linked +- If you make changes to the basic API shown in the tutorials, it's important to update + them. Consider adding a special note for major recent changes to inform users who may + be used to the old usage pattern. -- Problem: This section should describe the user problem that your recipe is providing a direct solution for in second person. This should ideally be a one-liner so that users can quickly scan it and see if it’s relevant to what they’re trying to do. -- Solution: This section should describe possible solutions for the problem with code snippets and text before and after that describe what is needed to run the code, as well as what it generates and how this solves the problem. +### How-to recipes -- Discussion: This section can go into detail on when this kind of problem can arise, caveats to running the code, and any related. +The title of a how-to should clearly describe what problem it's solving. It should be an +action that follows "How to". The text itself has up to four sections, but only the +first two are required: + +- Problem: This section should describe the user problem that your recipe is providing a + direct solution for in second person. This should ideally be a one-liner so that users + can quickly scan it and see if it’s relevant to what they’re trying to do. + +- Solution: This section should describe possible solutions for the problem with code + snippets and text before and after that describe what is needed to run the code, as + well as what it generates and how this solves the problem. + +- Discussion: This section can go into detail on when this kind of problem can arise, + caveats to running the code, and any related. - See also: Links to other relevant documentation or resources. @@ -62,36 +132,299 @@ See also ``` - ### Experiment guides The main goal of `qiskit-experiment` experiment guides is to serve as user guides for -the various package components such as the characterization and calibration -experiments. To this end, each documentation should cover the main (if not all) use-cases -of the documented functionality, including code examples and expected outputs. -Another objective of the documentation is to provide the user with basic background -on each experiment method. Hence a good practice would -be to have in the beginning of the documentation a short background explanation, -preferably 1 or 2 paragraphs long which includes the main literature references -as well as a link to the relevant chapter in the Qiskit textbook, if available. See for example the -[Randomized Benchmarking](randomized_benchmarking.ipynb) documentation. - -Below are more concrete guidelines pertaining to various documentation aspects: - -## Formatting guidelines -* For experiments, documentation title should be just the name of the experiment. Use regular capitalization. -* For sub titles of how-to steps - use present progressive. E.e. "Saving exp data to the DB" (instead of "Save exp data to the DB") -* Use math notation as much as possible (e.g. use $\frac{\pi}{2}$ instead of pi-half or pi/2) -* Use headers, subheaders, subsubheaders etc. for hierarchical text organization. No need to number the headers -* Use device names as shown in the IBM Quantum Services dashboard, e.g. ibmq_lima instead of IBMQ Lima -* put identifier names (e.g. osc_freq) in code blocks using backticks, i.e. `osc_freq` - -## Content guidelines - -* First section should be a general explanation on the topic. Put 2-3 most relevant references (papers and Qiskit textbook) -* Cover the common use-cases of the documented functionality (e.g. experiment) -* For each use-case, provide an example output, such as console printings and plot figures -* Cover all the required and common params (e.g. experiment and analysis options) -* For an experiment documentation, cover using the experiment in a composite experiment setting +the various package components such as the characterization and calibration experiments. +To this end, each guide should introduce the cover the main (if not all) use-cases of +the documented functionality, including code examples and expected outputs. Another +objective of the documentation is to provide the user with basic background on each +experiment method. Hence a good practice would be to have in the beginning of the +documentation a short background explanation, preferably 1 or 2 paragraphs long which +includes the main literature references as well as a link to the relevant chapter in the +Qiskit textbook, if available. The common use cases of the experiment should be covered +with a code example and example outputs by printing relevant analysis results and plot +figures. Required and common parameters, such as experiment and analysis options, shoud +be covered. + +See the [Randomized Benchmarking](docs/guides/randomized_benchmarking.rst) guide for an +example. Here is a simple template for a guide: + +``` +New Experiment +============== + +Here the experiment is introduced, and any background info needed to understand it is +ideally provided to the level of someone who has taken a background course in quantum +computing. References are provided to the original paper where the experiment was +described, if relevant, and to good resources for understanding it. + +Running new experiment +---------------------- + +Here caveats about the specific implementation of the experiment in this package are +discussed and sample code is provided. Because information on the general inputs and +outputs of an experiment will be covered in the tutorials, there’s no need to repeat +information that applies to all experiments. + +.. jupyter-execute:: + + # Sample code that runs the experiment is shown here. + +Choosing good parameters +~~~~~~~~~~~~~~~~~~~~~~~~ + +If there are specific considerations when running the experiment that you want to +highlight, this is a good place to discuss them. + +Advanced usage +-------------- + +You may want to highlight advanced usage or ways to improve performance that will be of +interest to experimentalists and researchers. For example, for the T1 experiment, one +such section might discuss the optimal way of choosing delay lengths to obtain the +most information about T1, in scenarios where T1 is roughly known versus scenarios +where nearly nothing is known. Papers should be cited where relevant. + +See also +-------- + +Links to relevant experiment classes in the API docs should be provided here. +``` + +### API documentation + +API documentation is automatically generated from docstrings. If you implement a new +experiment or analysis or update how an existing one functions, you should use following +style so that the documentation is formatted in the same manner throughout our +experiment library. You can use standard +[reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html) +directives along with our syntax. + +#### Experiment class documentation + +There are several predefined sections for the class docstring. + +```buildoutcfg + """One line simple summary of this experiment in the format of "An experiment that + measures [parameter]". + + You can add more information after line feed. The first line will be shown in an + automatically generated table of contents on the module's top page. + This text block is not shown so you can keep the table clean. + + You can use following sections. The text within a section should be indented. + + # section: overview + + Overview of the experiment. This information SHOULD be provided for every experiment. + This section covers technical aspect of experiment and explains how the experiment works. + + A diagram of typical quantum circuit that the experiment generates may help readers + to grasp the behavior of this experiment. + + # section: analysis_ref + + You MUST provide a reference to the default analysis class in the base class. + This section is recursively referred by child classes if not explicitly given there. + The format should be a Sphinx cross-reference to the class, such as + + :class:`~qiskit_experiments.framework.BaseAnalysis` + + # section: warning + If user must take special care when using the experiment (e.g. API is not stabilized) + you should clarify in this section. + + # section: note + Optional. This comment is shown in a box so that the message is stood out. + + # section: example + Optional. You can write code example here. For example, + + .. code-block:: python + + exp = MyExperiment(qubits=[0, 1], backend=backend) + exp.run() + + This is effective especially when your experiment has complicated options. + + # section: reference + Optional. You can write reference to article or external website. + To write a reference to an arXiv work, you can use convenient macro. + + .. ref_arxiv:: Auth2020a 21xx.01xxx + + This collects the latest article information from web and automatically + generates a nicely formatted citation from the arXiv ID. + + For referring to the website, + + .. ref_website:: Qiskit Experiment Github, https://github.com/Qiskit/qiskit-experiments + + you can use the above macro, where you can provide a string for the hyperlink and + the destination location separated by single comma. + + # section: guide + Optional. Link to user guide of this experiment if one exists. + + # section: see_also + Optional. You can list relevant experiment or module. + Here you cannot write any comments. + You just need to list absolute paths to relevant API documents, i.e. + + qiskit_experiments.framework.BaseExperiment + qiskit_experiments.framework.BaseAnalysis + """ +``` + +You also need to provide the experiment option description in the +`_default_experiment_options` method if you add new options. This description will be +automatically propagated through child classes, so you don't need to manually copy +documentation. Of course, you can override documentation in the child class if it +behaves differently there. + +```buildoutcfg + """Default experiment options. + + Experiment Options: + opt1 (int): Description of opt1. + opt2 (float): Description of opt2. + opt3 (List[SomeClass]): Description of opt3. + """ +``` + +Note that you should use the [Google docstring +style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). +Numpy or other docstring styles cannot be parsed by our Sphinx extension, and the +section header should be named `Experiment Options` (NOT `Args`). Since this is a +private method, any other documentation besides option descriptions are not rendered in +the HTML documentation. Documentation for options are automatically formatted and +inserted into the class documentation. + +#### Analysis class documentation + +You can use the same syntax and section headers for the analysis class documentation. In +addition, you can use extra sections, `fit_model` and `fit_parameters`, if needed. + +```buildoutcfg + """One line simple summary of this analysis. + + # section: overview + Overview of this analysis. + + # section: fit_model + Optional. If this analysis fits something, probably it is worth describing + the fit model. You can use math mode where latex commands are available. + + .. math:: + + F(x) = a\exp(x) + b + + It is recommended to omit `*` symbols for multiplication (looks ugly in math mode), + and you should carefully choose the parameter name so that symbols matches with + variable names shown in analysis results. You can write symbol :math:`a` here too. + + # section: fit_parameters + Optional. Description for fit parameters in the model. + You can also write how initial guess is generated and how fit bound is determined. + + defpar a: + desc: Amplitude. + init_guess: This is how :math:`a` is generated. No line feed. + bounds: [-1, 1] + + defpar b: + desc: Offset. + init_guess: This is how :math:`b` is generated. No line feed. + bounds: (0, 1] + + The defpar syntax is parsed and formatted nicely. + """ +``` + +You also need to provide a description for analysis class options in the +`_default_options` method. + +```buildoutcfg + """Default analysis options. + + Analysis Options: + opt1 (int): Description of opt1. + opt2 (float): Description of opt2. + opt3 (List[SomeClass]): Description of opt3. + """ +``` + +This is the same syntax with experiment options in the experiment class. Note that +header should be named `Analysis Options` to be parsed correctly. + +#### Populating the table of contents + +After you complete documentation of your classes, you must add documentation to the +toctree so that it can be rendered as the API documentation. In Qiskit Experiments, we +have a separate tables of contents for each experiment module (e.g. [characterization +experiments](https://qiskit.org/documentation/experiments/apidocs/mod_characterization.html)) +and for the [entire +library](https://qiskit.org/documentation/experiments/apidocs/library.html). Thus we +should add document to the tree of a particular module and then reference it to the +entire module. + +As an example, when writing the characterization experiment and analysis, first add your +documentation to the table of contents of the module: + +```buildoutcfg +qiskit_experiments/library/characterization/__init__.py + """ + .. currentmodule:: qiskit_experiments.library.characterization + + Experiments + =========== + .. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + MyExperiment1 + MyExperiment2 + + Analysis + ======== + + .. autosummary:: + :toctree: ../stubs/ + :template: autosummary/analysis.rst + + ... + """ + + from my_experiment import MyExperiment1, MyExperiment2 + from my_analysis import MyAnalysis +``` + +Note that there are different stylesheets, `experiment.rst` and `analysis.rst`, for the +experiment class and analysis class, respectively. Take care to place your documentation +under the correct stylesheet, otherwise it may not be rendered properly. Then the table +for the entire library should be written like this: + +```buildoutcfg +qiskit_experiments/library/__init__.py + + """ + .. currentmodule:: qiskit_experiments.library + + Characterization Experiments + ============================ + .. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ~characterization.MyExperiment1 + ~characterization.MyExperiment2 + """ + + from .characterization import MyExperiment1, MyExperiment2 + from . import characterization +``` +Here the reference start with `~`. We only add experiment classes to the table of the +entire library. diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index 111a75e80f..d6555a1363 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -90,9 +90,9 @@ def format_see_also(self, lines: List[str]) -> List[str]: return format_lines @_check_no_indent - def format_tutorial(self, lines: List[str]) -> List[str]: - """Format tutorial section.""" - format_lines = [".. rubric:: Tutorials", ""] + def format_guide(self, lines: List[str]) -> List[str]: + """Format user guide section.""" + format_lines = [".. rubric:: User Guide", ""] format_lines.extend(lines) format_lines.append("") diff --git a/docs/_ext/custom_styles/styles.py b/docs/_ext/custom_styles/styles.py index 3b37c45c89..eb1b994881 100644 --- a/docs/_ext/custom_styles/styles.py +++ b/docs/_ext/custom_styles/styles.py @@ -161,7 +161,7 @@ class ExperimentDocstring(QiskitExperimentDocstring): "warning": load_standard_section, "overview": load_standard_section, "reference": load_standard_section, - "tutorial": load_standard_section, + "guide": load_standard_section, "analysis_ref": load_standard_section, "experiment_opts": None, "transpiler_opts": None, @@ -266,7 +266,7 @@ class AnalysisDocstring(QiskitExperimentDocstring): "fit_model": load_standard_section, "fit_parameters": load_fit_parameters, "reference": load_standard_section, - "tutorial": load_standard_section, + "guide": load_standard_section, "analysis_opts": None, "example": load_standard_section, "note": load_standard_section, @@ -322,9 +322,9 @@ class VisualizationDocstring(QiskitExperimentDocstring): "warning": load_standard_section, "overview": load_standard_section, "reference": load_standard_section, - "tutorial": load_standard_section, - "opts": None, # For standard options - "figure_opts": None, # For figure options + "guide": load_standard_section, + "opts": None, # For standard options + "figure_opts": None, # For figure options "example": load_standard_section, "note": load_standard_section, "see_also": load_standard_section, diff --git a/docs/guides/t1_characterization.rst b/docs/guides/t1_characterization.rst index 396257645f..b71df500f0 100644 --- a/docs/guides/t1_characterization.rst +++ b/docs/guides/t1_characterization.rst @@ -2,7 +2,7 @@ T1 Characterization =================== Background -=========== +---------- In a :math:`T_1` experiment, we measure an excited qubit after a delay. Due to decoherence processes (e.g. amplitude damping channel), it is @@ -80,7 +80,7 @@ for qubit 0. :math:`T_1` experiments with kerneled measurement -================================================= +------------------------------------------------- :math:`T_1` experiments can also be done with kerneled measurements. If we set the run option `meas_level=MeasLevel.KERNELED`, the job diff --git a/docs/guides/t2hahn_characterization.rst b/docs/guides/t2hahn_characterization.rst index 3a5449d209..49a4473a25 100644 --- a/docs/guides/t2hahn_characterization.rst +++ b/docs/guides/t2hahn_characterization.rst @@ -26,12 +26,11 @@ Decoherence Time The decoherence time is the time taken for off-diagonal components of the density matrix to fall to approximately 37% (:math:`\frac{1}{e}`). For -:math:`t\gg T_2`, the qubit statistics behave like a random bit. It gets -the value of ``0`` with probability of :math:`p` and the value of ``1`` -with probability of :math:`1-p`. +:math:`t\gg T_2`, the qubit statistics behave like a random bit, with +value 0 with probability of :math:`p` and value 1 with probability :math:`1-p`. Since the qubit is exposed to other types of noise (like T1), we are -using :math:`Rx(\pi)` pulses for decoupling and to solve our inaccuracy +using :math:`R_x(\pi)` pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation. .. jupyter-execute:: @@ -42,17 +41,21 @@ for the qubit frequency estimation. The circuit used for an experiment with :math:`N` echoes comprises the following components: - 1. :math:`Rx\left(\frac{\pi}{2} \right)` gate - 2. :math:`N` times echo sequence: - (a) :math:`Delay \left(t_{0} \right)` gate - (b) :math:`Rx \left(\pi \right)` gate - (c) :math:`Delay \left(t_{0} \right)` gate - 3. :math:`Rx \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the number of echoes) - 4. Measurement gate +#. :math:`R_x\left(\frac{\pi}{2} \right)` gate +#. :math:`N` times echo sequence: + + #. :math:`Delay \left(t_{0} \right)` gate + #. :math:`R_x \left(\pi \right)` gate + #. :math:`Delay \left(t_{0} \right)` gate + +#. :math:`R_x \left(\pm \frac{\pi}{2} \right)` gate (sign depends on the number of echoes) +#. Measurement gate + +| The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the -echo gate (:math:`Rx(\pi)`) for each echo, the angle after the delay +echo gate (:math:`R_x(\pi)`) for each echo, the angle after the delay gates will be :math:`\theta_{new} = \theta_{old} + \pi`. After waiting the same delay time, the angle will be approximately :math:`0` or :math:`\pi`. By varying the extension of the delays, we get a series of diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py index cbebf46e4e..c9cdf1363b 100644 --- a/qiskit_experiments/library/calibration/rough_drag_cal.py +++ b/qiskit_experiments/library/calibration/rough_drag_cal.py @@ -33,7 +33,7 @@ class RoughDragCal(BaseCalibrationExperiment, RoughDrag): # section: see_also :class:`.RoughDrag` - # section: tutorial + # section: guide :ref:`DRAG Calibration` """ diff --git a/qiskit_experiments/library/characterization/cr_hamiltonian.py b/qiskit_experiments/library/characterization/cr_hamiltonian.py index e641f609a6..c07516bda6 100644 --- a/qiskit_experiments/library/characterization/cr_hamiltonian.py +++ b/qiskit_experiments/library/characterization/cr_hamiltonian.py @@ -122,7 +122,7 @@ class CrossResonanceHamiltonian(BaseExperiment): # section: reference .. ref_arxiv:: 1 1603.04821 - # section: tutorial + # section: guide .. ref_website:: Qiskit Textbook 6.7, https://qiskit.org/textbook/ch-quantum-hardware/hamiltonian-tomography.html """ diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index fa28f402b0..4f4762a5f7 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -70,7 +70,7 @@ class RoughDrag(BaseExperiment, RestlessMixin): .. ref_arxiv:: 2 0901.0534 .. ref_arxiv:: 3 1509.05470 - # section: tutorial + # section: guide :ref:`DRAG Calibration` """ diff --git a/qiskit_experiments/library/characterization/fine_amplitude.py b/qiskit_experiments/library/characterization/fine_amplitude.py index cc88cf4811..668d48c287 100644 --- a/qiskit_experiments/library/characterization/fine_amplitude.py +++ b/qiskit_experiments/library/characterization/fine_amplitude.py @@ -85,7 +85,7 @@ class FineAmplitude(BaseExperiment, RestlessMixin): # section: reference .. ref_arxiv:: 1 1504.06597 - # section: tutorial + # section: guide :ref:`fine-amplitude-cal` """ diff --git a/qiskit_experiments/library/characterization/rabi.py b/qiskit_experiments/library/characterization/rabi.py index c8dc2196f7..13ac8e0aff 100644 --- a/qiskit_experiments/library/characterization/rabi.py +++ b/qiskit_experiments/library/characterization/rabi.py @@ -47,7 +47,7 @@ class Rabi(BaseExperiment, RestlessMixin): The user provides his own schedule for the Rabi at initialization which must have one free parameter, i.e. the amplitude to scan and a drive channel which matches the qubit. - # section: tutorial + # section: guide :ref:`Rabi Calibration` See also `Qiskit Textbook Date: Mon, 6 Mar 2023 18:57:04 -0500 Subject: [PATCH 48/64] Updated index Made images clickable --- docs/index.rst | 30 +++++++++++++----------------- 1 file changed, 13 insertions(+), 17 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index aa2586371c..020289c3ce 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -19,11 +19,10 @@ We've divided up the documentation into four sections with different purposes: .. grid:: 2 :gutter: 5 - .. grid-item-card:: - :img-top: _static/tutorials.png + .. grid-item-card:: Tutorials - Tutorials - ^^^^^^^^^ + .. image:: _static/tutorials.png + :target: tutorials/index.html These step-by-step tutorials teach the fundamentals of the package and are suitable for getting started. You'll find in these tutorials: @@ -40,12 +39,11 @@ We've divided up the documentation into four sections with different purposes: To the learning tutorials - .. grid-item-card:: - :img-top: _static/howtos.png - - How-Tos - ^^^^^^^ + .. grid-item-card:: How-Tos + .. image:: _static/howtos.png + :target: howtos/index.html + These individual how-to recipes provide short and direct solutions to some commonly asked questions for Qiskit Experiments users. You'll find in these recipes: @@ -60,11 +58,10 @@ We've divided up the documentation into four sections with different purposes: To the how-to recipes - .. grid-item-card:: - :img-top: _static/guides.png + .. grid-item-card:: Experiment Guides - Experiment Guides - ^^^^^^^^^^^^^^^^^ + .. image:: _static/guides.png + :target: guides/index.html These are in-depth guides to key experiments in the package, describing their background, principle, and how to run them in Qiskit Experiments. You'll find in these guides: @@ -81,11 +78,10 @@ We've divided up the documentation into four sections with different purposes: To the experiment guides - .. grid-item-card:: - :img-top: _static/api.png + .. grid-item-card:: API Reference - API Reference - ^^^^^^^^^^^^^ + .. image:: _static/api.png + :target: apidocs/index.html This is a detailed description of every module, method, and function in Qiskit Experiments and how to use them, suitable for those working closely From 505f7795a4ddd97b1e613e86229c4131ae6874b9 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 7 Mar 2023 16:03:59 -0500 Subject: [PATCH 49/64] Reorganized guides and fixes Co-authored-by: Will Shanks Guides are now split into three categories. The file to override jupyter-execute has been renamed. The reno newline warning has been fixed. --- .github/ISSUE_TEMPLATE/FEATURE_REQUEST.md | 2 +- docs/GUIDELINES.md | 3 +- ...-checkenv.py => jupyter_execute_custom.py} | 2 +- docs/conf.py | 23 ++++++------ .../{ => benchmarking}/quantum_volume.rst | 0 .../randomized_benchmarking.rst | 19 +++++----- .../{ => benchmarking}/state_tomography.rst | 33 +++++++++--------- .../t1.rst} | 0 .../t2hahn.rst} | 22 ++++++------ .../t2ramsey.rst} | 0 .../tphi.rst} | 20 +++++------ docs/guides/index.rst | 32 +++++++++++++++-- .../{ => measurement}/readout_mitigation.rst | 4 +-- .../restless_measurements.rst | 0 .../{ => measurement}/restless_shots.png | Bin docs/howtos/index.rst | 5 ++- docs/index.rst | 4 +-- docs/tutorials/curve_analysis.rst | 22 ++++++------ docs/tutorials/data_processor.rst | 2 +- docs/tutorials/intro.rst | 1 + .../library/characterization/t1.py | 7 ++-- .../library/characterization/t2hahn.py | 2 +- .../library/characterization/t2ramsey.py | 2 +- .../library/characterization/tphi.py | 2 +- .../notes/tomography-b091ce13d6983bc1.yaml | 2 +- 25 files changed, 120 insertions(+), 89 deletions(-) rename docs/_ext/{jupyter-execute-checkenv.py => jupyter_execute_custom.py} (93%) rename docs/guides/{ => benchmarking}/quantum_volume.rst (100%) rename docs/guides/{ => benchmarking}/randomized_benchmarking.rst (94%) rename docs/guides/{ => benchmarking}/state_tomography.rst (85%) rename docs/guides/{t1_characterization.rst => characterization/t1.rst} (100%) rename docs/guides/{t2hahn_characterization.rst => characterization/t2hahn.rst} (92%) rename docs/guides/{t2ramsey_characterization.rst => characterization/t2ramsey.rst} (100%) rename docs/guides/{tphi_characterization.rst => characterization/tphi.rst} (80%) rename docs/guides/{ => measurement}/readout_mitigation.rst (97%) rename docs/guides/{ => measurement}/restless_measurements.rst (100%) rename docs/guides/{ => measurement}/restless_shots.png (100%) diff --git a/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md b/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md index 3061c05382..9710fd41de 100644 --- a/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md +++ b/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md @@ -10,4 +10,4 @@ assignees: '' -### What is the expected behavior? +### Suggested feature diff --git a/docs/GUIDELINES.md b/docs/GUIDELINES.md index 28b36080fc..f3ad03f1d2 100644 --- a/docs/GUIDELINES.md +++ b/docs/GUIDELINES.md @@ -147,7 +147,8 @@ with a code example and example outputs by printing relevant analysis results an figures. Required and common parameters, such as experiment and analysis options, shoud be covered. -See the [Randomized Benchmarking](docs/guides/randomized_benchmarking.rst) guide for an +See the [Randomized Benchmarking](https://qiskit.org/documentation/experiments/guides/benchmarking/randomized_benchmarking.html) +guide and its [source code](docs/guides/benchmarking/randomized_benchmarking.rst) for an example. Here is a simple template for a guide: ``` diff --git a/docs/_ext/jupyter-execute-checkenv.py b/docs/_ext/jupyter_execute_custom.py similarity index 93% rename from docs/_ext/jupyter-execute-checkenv.py rename to docs/_ext/jupyter_execute_custom.py index fe123115b5..2f52812955 100644 --- a/docs/_ext/jupyter-execute-checkenv.py +++ b/docs/_ext/jupyter_execute_custom.py @@ -11,7 +11,7 @@ # that they have been altered from the originals. """ -Directive to skip build of tutorial cells when indicated by the environment. +Customizations of :mod:`jupyter-sphinx`. """ from jupyter_sphinx import JupyterCell from sphinx.application import Sphinx diff --git a/docs/conf.py b/docs/conf.py index bc734f5526..1455f5a105 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -77,7 +77,7 @@ "autodoc_experiment", "autodoc_analysis", "autodoc_visualization", - "jupyter-execute-checkenv", + "jupyter_execute_custom", ] html_static_path = ["_static"] @@ -90,19 +90,20 @@ exclude_patterns = ["_build", "**.ipynb_checkpoints"] # Thumbnails for experiment guides from output images +# These should ideally be automatically generated using a custom macro to specify +# chosen cells for thumbnails, like the nbsphinx-gallery tag nbsphinx_thumbnails = { - "guides/quantum_volume": "_images/quantum_volume_2_0.png", - "guides/readout_mitigation": "_images/readout_mitigation_4_0.png", - "guides/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", - "guides/restless_measurements": "_images/restless_shots.png", - "guides/state_tomography": "_images/state_tomography_3_0.png", - "guides/t1_characterization": "_images/t1_characterization_0_0.png", - "guides/t2ramsey_characterization": "_images/t2ramsey_characterization_4_0.png", - "guides/tphi_characterization": "_images/tphi_characterization_8_0.png", - "guides/t2hahn_characterization": "_images/t2hahn_characterization_5_0.png", + "guides/benchmarking/quantum_volume": "_images/quantum_volume_2_0.png", + "guides/measurement/readout_mitigation": "_images/readout_mitigation_4_0.png", + "guides/benchmarking/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", + "guides/measurement/restless_measurements": "_images/restless_shots.png", + "guides/benchmarking/state_tomography": "_images/state_tomography_3_0.png", + "guides/characterization/t1": "_images/t1_characterization_0_0.png", + "guides/characterization/t2ramsey": "_images/t2ramsey_characterization_4_0.png", + "guides/characterization/tphi": "_images/tphi_characterization_8_0.png", + "guides/characterization/t2hahn": "_images/t2hahn_characterization_5_0.png", } - # Add `data keys` and `style parameters` alias. Needed for `expected_*_data_keys` methods in # visualization module and `default_style` method in `PlotStyle` respectively. napoleon_custom_sections = [("data keys", "params_style"), ("style parameters", "params_style")] diff --git a/docs/guides/quantum_volume.rst b/docs/guides/benchmarking/quantum_volume.rst similarity index 100% rename from docs/guides/quantum_volume.rst rename to docs/guides/benchmarking/quantum_volume.rst diff --git a/docs/guides/randomized_benchmarking.rst b/docs/guides/benchmarking/randomized_benchmarking.rst similarity index 94% rename from docs/guides/randomized_benchmarking.rst rename to docs/guides/benchmarking/randomized_benchmarking.rst index 1610cfcaf2..4616378e50 100644 --- a/docs/guides/randomized_benchmarking.rst +++ b/docs/guides/benchmarking/randomized_benchmarking.rst @@ -1,16 +1,15 @@ Randomized Benchmarking ======================= -Randomized benchmarking (RB) is a popular protocol for characterizing the -error rate of quantum processors. An RB experiment consists of the generation of -random Clifford circuits on the given qubits such that the unitary -computed by the circuits is the identity. After running the circuits, -the number of shots resulting in an error (i.e. an output different than -the ground state) are counted, and from this data one can infer error -estimates for the quantum device, by calculating the Error Per Clifford. -See the `Qiskit -Textbook `__ -for an explanation on the RB method, which is based on Ref. [1, 2]. +Randomized benchmarking (RB) is a popular protocol for characterizing the error rate of +quantum processors. An RB experiment consists of the generation of random Clifford +circuits on the given qubits such that the unitary computed by the circuits is the +identity. After running the circuits, the number of shots resulting in an error (i.e. an +output different from the ground state) are counted, and from this data one can infer +error estimates for the quantum device, by calculating the Error Per Clifford. See the +`Qiskit Textbook +`__ for an +explanation on the RB method, which is based on Ref. [1, 2]. .. jupyter-execute:: diff --git a/docs/guides/state_tomography.rst b/docs/guides/benchmarking/state_tomography.rst similarity index 85% rename from docs/guides/state_tomography.rst rename to docs/guides/benchmarking/state_tomography.rst index 89ec10a121..0b86616258 100644 --- a/docs/guides/state_tomography.rst +++ b/docs/guides/benchmarking/state_tomography.rst @@ -19,7 +19,7 @@ State Tomography Experiment To run a state tomography experiment, we initialize the experiment with a circuit to prepare the state to be measured. We can also pass in an -:class:`~qiskit.quantum_info.Operator`, or a :class:`~qiskit.quantum_info.Statevector` +:class:`~qiskit.quantum_info.Operator` or a :class:`~qiskit.quantum_info.Statevector` to describe the preparation circuit. .. jupyter-execute:: @@ -79,24 +79,25 @@ Additional state metadata ^^^^^^^^^^^^^^^^^^^^^^^^^ Additional data is stored in the tomography under the -``"state_metadata"`` field. This includes - ``eigvals``: the eigenvalues -of the fitted state - ``trace``: the trace of the fitted state - -``positive``: Whether the eigenvalues are all non-negative - -``positive_delta``: the deviation from positivity given by 1-norm of -negative eigenvalues. - -If trace rescaling was performed this dictionary will also contain a -``raw_trace`` field containing the trace before rescaling. Futhermore, -if the state was rescaled to be positive or trace 1 an additional field -``raw_eigvals`` will contain the state eigenvalues before rescaling was -performed. +``"state_metadata"`` field. This includes + +- ``eigvals``: the eigenvalues of the fitted state +- ``trace``: the trace of the fitted state +- ``positive``: Whether the eigenvalues are all non-negative +- ``positive_delta``: the deviation from positivity given by 1-norm of negative + eigenvalues. + +If trace rescaling was performed this dictionary will also contain a ``raw_trace`` field +containing the trace before rescaling. Futhermore, if the state was rescaled to be +positive or trace 1 an additional field ``raw_eigvals`` will contain the state +eigenvalues before rescaling was performed. .. jupyter-execute:: state_result.extra -To see the effect of rescaling we can perform a “bad” fit with very low -counts +To see the effect of rescaling, we can perform a “bad” fit with very low +counts: .. jupyter-execute:: @@ -124,8 +125,8 @@ Gambetta, G Smith, Phys. Rev. Lett. 108, 070502 (2012), `open access `__. There are several other fitters are included (See API documentation for -details). For example if ``cvxpy`` is installed we can use the -``cvxpy_gaussian_lstsq`` fitter which allows constraining the fit to be +details). For example, if ``cvxpy`` is installed we can use the +:func:`~.cvxpy_gaussian_lstsq` fitter, which allows constraining the fit to be PSD without requiring rescaling. .. jupyter-execute:: diff --git a/docs/guides/t1_characterization.rst b/docs/guides/characterization/t1.rst similarity index 100% rename from docs/guides/t1_characterization.rst rename to docs/guides/characterization/t1.rst diff --git a/docs/guides/t2hahn_characterization.rst b/docs/guides/characterization/t2hahn.rst similarity index 92% rename from docs/guides/t2hahn_characterization.rst rename to docs/guides/characterization/t2hahn.rst index 49a4473a25..97f9d225d4 100644 --- a/docs/guides/t2hahn_characterization.rst +++ b/docs/guides/characterization/t2hahn.rst @@ -1,19 +1,17 @@ -T2 Hahn Characterization (CPMG) -=============================== +T2 Hahn Characterization +======================== The purpose of the :math:`T_2` Hahn Echo experiment is to determine the :math:`T_2` qubit property. -In this experiment, we would like to get a more precise estimate of the -qubit’s decay time. :math:`T_2` represents the amount of time required -for a single qubit's Bloch vector projection on the XY plane to fall to -approximately 37% (:math:`\frac{1}{e}`) of its initial amplitude. In the -Ramsey Experiment, we were introduced to the term detuning frequency (The -difference between the frequency used for the control rotation, and the -precise frequency). Hahn Echo experiment and the CPMG sequence are -experiments to estimate :math:`T_2` which are robust to the detuning -frequency. The decay in amplitude causes the probability function to -take the following form: +In this experiment, we would like to get a more precise estimate of the qubit’s decay +time. :math:`T_2` represents the amount of time required for a single qubit's Bloch +vector projection on the XY plane to fall to approximately 37% (:math:`\frac{1}{e}`) of +its initial amplitude. Hahn Echo experiment and the Carr-Purcell-Meiboom-Gill (CPMG) +sequence are experiments to estimate :math:`T_2` which are robust to the detuning +frequency, or the difference between the qubit frequency and the pulse frequency of the +applied rotation. The decay in amplitude causes the probability function to take the +following form: .. math:: f(t) = A \cdot e^{-\frac{t}{T_2}}+ B diff --git a/docs/guides/t2ramsey_characterization.rst b/docs/guides/characterization/t2ramsey.rst similarity index 100% rename from docs/guides/t2ramsey_characterization.rst rename to docs/guides/characterization/t2ramsey.rst diff --git a/docs/guides/tphi_characterization.rst b/docs/guides/characterization/tphi.rst similarity index 80% rename from docs/guides/tphi_characterization.rst rename to docs/guides/characterization/tphi.rst index ce90045ed3..5ace9ff5f6 100644 --- a/docs/guides/tphi_characterization.rst +++ b/docs/guides/characterization/tphi.rst @@ -1,17 +1,15 @@ Tφ Characterization =================== -:math:`\Gamma_\varphi` is defined as the rate of pure dephasing or -depolarization in the :math:`x - y` plane. We compute -:math:`\Gamma_\varphi` by computing :math:`\Gamma_2*`, the transverse -relaxation rate, and subtracting :math:`\Gamma_1`, the longitudinal -relaxation rate. The pure dephasing time is defined by -:math:`T_\varphi = 1/\Gamma_\varphi`. Or more precisely, -:math:`1/T_\varphi = 1/T_{2*} - 1/2T_1` - -We therefore create a composite experiment consisting of a :math:`T_1` -experiment and a :math:`T_2*` experiment. From the results of these two, -we compute the results for :math:`T_\varphi.` +:math:`\Gamma_\varphi` is defined as the rate of pure dephasing or depolarization in the +:math:`x - y` plane. We compute :math:`\Gamma_\varphi` by computing :math:`\Gamma_2*`, +the transverse relaxation rate, and subtracting :math:`\Gamma_1`, the longitudinal +relaxation rate. The pure dephasing time is defined by :math:`T_\varphi = +1/\Gamma_\varphi`. Or more precisely, :math:`1/T_\varphi = 1/T_{2*} - 1/2T_1` + +We therefore create a composite experiment consisting of a :math:`T_1` experiment and a +:math:`T_2*` experiment. From the results of these two, we compute the results for +:math:`T_\varphi.` .. jupyter-execute:: diff --git a/docs/guides/index.rst b/docs/guides/index.rst index e7d6e4259b..ba0fa25a46 100644 --- a/docs/guides/index.rst +++ b/docs/guides/index.rst @@ -2,14 +2,40 @@ Experiment Guides ##################### -These experiment guides are in-depth dives into individual experiments, their operational principles, -and how to run them in Qiskit Experiments. +These experiment guides are in-depth dives into individual experiments, their +operational principles, and how to run them in Qiskit Experiments. +Benchmarking Experiments +------------------------ + +These experiments measure your device performance according to a set of defined +metrics, such as the space-time volume of circuits that can be successfully executed. + +.. nbgallery:: + :glob: + + benchmarking/* + +Qubit Characterization Experiments +---------------------------------- + +These experiment measure specific properties of a qubit. + +.. nbgallery:: + :glob: + + characterization/* + +Measurement-Related Experiments +------------------------------- + +These experiments postprocess on measurement results to improve some aspect of a +quantum circuit, such as readout fidelity or run time. .. nbgallery:: :glob: - * + measurement/* .. Hiding - Indices and tables diff --git a/docs/guides/readout_mitigation.rst b/docs/guides/measurement/readout_mitigation.rst similarity index 97% rename from docs/guides/readout_mitigation.rst rename to docs/guides/measurement/readout_mitigation.rst index cfe5e1b613..6c7eab475d 100644 --- a/docs/guides/readout_mitigation.rst +++ b/docs/guides/measurement/readout_mitigation.rst @@ -20,12 +20,12 @@ the tensor product of :math:`n` :math:`2 \times 2` matrices, one for each qubit, making it practical to store the assignment matrix in implicit form, by storing the individual :math:`2 \times 2` assignment matrices. The corresponding class in Qiskit is the -:class:`~qiskit.result.LocalReadoutMitigator` in Qiskit Terra. +:class:`~qiskit.result.LocalReadoutMitigator`. A *Correlated readout mitigator* uses the full :math:`2^n \times 2^n` assignment matrix, meaning it can only be used for small values of :math:`n`. The corresponding class in Qiskit is the -:class:`~qiskit.result.CorrelatedReadoutMitigator` in Qiskit Terra. +:class:`~qiskit.result.CorrelatedReadoutMitigator`. This notebook demonstrates the usage of both the local and correlated experiments to generate the corresponding mitigators. diff --git a/docs/guides/restless_measurements.rst b/docs/guides/measurement/restless_measurements.rst similarity index 100% rename from docs/guides/restless_measurements.rst rename to docs/guides/measurement/restless_measurements.rst diff --git a/docs/guides/restless_shots.png b/docs/guides/measurement/restless_shots.png similarity index 100% rename from docs/guides/restless_shots.png rename to docs/guides/measurement/restless_shots.png diff --git a/docs/howtos/index.rst b/docs/howtos/index.rst index dae6225d43..8f86897150 100644 --- a/docs/howtos/index.rst +++ b/docs/howtos/index.rst @@ -14,7 +14,10 @@ with :ref:`the basics ` of the package before using these recipes. * | -| + +If there are recipes on solving specific problems that you'd like to see added, please +`file an issue on GitHub `_. + | diff --git a/docs/index.rst b/docs/index.rst index 020289c3ce..308c91f15b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -66,8 +66,8 @@ We've divided up the documentation into four sections with different purposes: These are in-depth guides to key experiments in the package, describing their background, principle, and how to run them in Qiskit Experiments. You'll find in these guides: - * How to analyze 1- and 2-qubit errors in :doc:`randomized benchmarking ` - * How to calculate the speedup from using :doc:`restless measurements ` + * How to analyze 1- and 2-qubit errors in :doc:`randomized benchmarking ` + * How to calculate the speedup from using :doc:`restless measurements ` +++ diff --git a/docs/tutorials/curve_analysis.rst b/docs/tutorials/curve_analysis.rst index 9b20d3582e..2ff337fbe1 100644 --- a/docs/tutorials/curve_analysis.rst +++ b/docs/tutorials/curve_analysis.rst @@ -25,7 +25,7 @@ and curve analysis sorts the experiment results based on the circuit metadata. This is an example of showing the abstract data structure of typical curve analysis experiment: -.. code-block:: none +.. jupyter-input:: :emphasize-lines: 1,10,19 "experiment" @@ -118,7 +118,7 @@ Basically the Qiskit curve analysis delegates the core fitting functionality to You can intuitively write the definition of model, as shown below: -.. code-block:: python3 +.. jupyter-input:: import lmfit @@ -135,7 +135,7 @@ consists of three parameters (``amp``, ``alpha``, ``base``), and ``exp`` indicat a universal function in Python's math module. Alternatively, you can take a callable to define the model object. -.. code-block:: python3 +.. jupyter-input:: import lmfit import numpy as np @@ -145,7 +145,7 @@ Alternatively, you can take a callable to define the model object. models = [lmfit.Model(func=exp_decay)] -See `LMFIT`_ documentation for detailed user guide. They also provide preset models. +See the `LMFIT`_ documentation for detailed user guide. They also provide preset models. If the :class:`.CurveAnalysis` is instantiated with multiple models, it internally builds a cost function to simultaneously minimize the residuals of @@ -155,7 +155,7 @@ in the analysis result, and potentially in your experiment database as a fit res Here is another example how to implement multi-objective optimization task: -.. code-block:: python3 +.. jupyter-input:: import lmfit @@ -184,7 +184,7 @@ Here one should expect the experiment data will have two classes of data with me By using this model, one can flexibly set up your fit model. Here is another example: -.. code-block:: python3 +.. jupyter-input:: import lmfit @@ -216,7 +216,7 @@ the parameter names in the analysis option ``fixed_parameters``. This feature is useful especially when you want to define a subclass of a particular analysis class. -.. code-block:: python3 +.. jupyter-input:: class AnalysisA(CurveAnalysis): @@ -241,7 +241,7 @@ a particular analysis class. The parameter specified in ``fixed_parameters`` is excluded from the fitting. This code will give you identical fit model to the one defined in the following class: -.. code-block:: python3 +.. jupyter-input:: class AnalysisB(CurveAnalysis): @@ -350,7 +350,7 @@ The :class:`.FitOptions` class implements convenient method :meth:`set_if_empty` conflict with user provided values, i.e. user provided values have higher priority, thus systematically generated values cannot override user values. -.. code-block:: python3 +.. jupyter-input:: def _generate_fit_guesses(self, user_opt, curve_data): @@ -401,7 +401,7 @@ If you want to create an analysis result entry for the particular parameter, you can override the analysis options ``result_parameters``. By using :class:`ParameterRepr` representation, you can rename the parameter in the entry. -.. code-block:: python3 +.. jupyter-input:: from qiskit_experiments.curve_analysis import ParameterRepr @@ -419,7 +419,7 @@ Not only returning the fit parameters, you can also compute new quantities by combining multiple fit parameters. This can be done by overriding the :meth:`_create_analysis_results` method. -.. code-block:: python3 +.. jupyter-input:: from qiskit_experiments.framework import AnalysisResultData diff --git a/docs/tutorials/data_processor.rst b/docs/tutorials/data_processor.rst index 651bae61f1..6d45a01af8 100644 --- a/docs/tutorials/data_processor.rst +++ b/docs/tutorials/data_processor.rst @@ -212,4 +212,4 @@ PRX Quantum 2, 020324 (2021). https://arxiv.org/abs/2010.06576 See also -------- -Experiment guide: :doc:`/guides/restless_measurements` +Experiment guide: :doc:`/guides/measurement/restless_measurements` diff --git a/docs/tutorials/intro.rst b/docs/tutorials/intro.rst index 0eee1d41bd..5886ab2665 100644 --- a/docs/tutorials/intro.rst +++ b/docs/tutorials/intro.rst @@ -40,6 +40,7 @@ to process the data, fit it to specified models, and plot the results, respectiv .. figure:: images/experimentarch.png :width: 400 :align: center + :class: no-scaled-link Experiments start with an ``Experiment`` class, which instantiates the circuits that diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index 9ae0dddf1d..635cf0c205 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -28,7 +28,7 @@ class T1(BaseExperiment): # section: overview - Design and analyze experiments for estimating T\ :sub:`1` relaxation time of the qubit. + Design and analyze experiments for estimating :math:`T_1` relaxation time of the qubit. Each experiment consists of the following steps: @@ -36,7 +36,7 @@ class T1(BaseExperiment): wait different time intervals, then measure the qubit. 2. Backend execution: actually running the circuits on the device (or simulator). - 3. Analysis of results: deduction of T\ :sub:`1`\ , based on the outcomes, + 3. Analysis of results: deduction of :math:`T_1`, based on the outcomes, by fitting to an exponential curve. | @@ -44,6 +44,9 @@ class T1(BaseExperiment): # section: analysis_ref :class:`.T1Analysis` + # section: guide + :doc:`/guides/characterization/t1` + """ @classmethod diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py index 628aff4ce5..bf77a51dfd 100644 --- a/qiskit_experiments/library/characterization/t2hahn.py +++ b/qiskit_experiments/library/characterization/t2hahn.py @@ -54,7 +54,7 @@ class T2Hahn(BaseExperiment): The circuits are run on the device or on a simulator backend. # section: guide - :doc:`/guides/t2hahn_characterization` + :doc:`/guides/characterization/t2hahn` # section: analysis_ref :class:`T2HahnAnalysis` diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py index f5e66c11bd..8098d26eba 100644 --- a/qiskit_experiments/library/characterization/t2ramsey.py +++ b/qiskit_experiments/library/characterization/t2ramsey.py @@ -54,7 +54,7 @@ class T2Ramsey(BaseExperiment): The circuits are run on the device or on a simulator backend. # section: guide - :doc:`/guides/t2ramsey_characterization` + :doc:`/guides/characterization/t2ramsey` # section: analysis_ref :class:`T2RamseyAnalysis` diff --git a/qiskit_experiments/library/characterization/tphi.py b/qiskit_experiments/library/characterization/tphi.py index 2661d05c9b..7fd6365e17 100644 --- a/qiskit_experiments/library/characterization/tphi.py +++ b/qiskit_experiments/library/characterization/tphi.py @@ -49,7 +49,7 @@ class Tphi(BatchExperiment): .. ref_arxiv:: 1 1904.06560v5 # section: guide - :doc:`/guides/tphi_characterization` + :doc:`/guides/characterization/tphi` """ def set_experiment_options(self, **fields): diff --git a/releasenotes/notes/tomography-b091ce13d6983bc1.yaml b/releasenotes/notes/tomography-b091ce13d6983bc1.yaml index 1cd2dd6e7c..c42789dde3 100644 --- a/releasenotes/notes/tomography-b091ce13d6983bc1.yaml +++ b/releasenotes/notes/tomography-b091ce13d6983bc1.yaml @@ -90,7 +90,7 @@ upgrade: deprecations: - | Renames the ``qubits``, ``measurement_qubits``, and ``preparation_qubits`` - init kwargs of :class:`~.StateTomography`, + init kwargs of :class:`~.StateTomography`, :class:`~.ProcessTomography`, and :class:`~.TomographyExperiment` have been deprecated. They have been replaced with kwargs ``physical_qubits``, ``measurement_indices`` and ``preparation_indices`` respectively. The From 0e492df4c41dd2b76cc4d38abffabe69606e46b0 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 7 Mar 2023 17:10:57 -0500 Subject: [PATCH 50/64] Fixed sphinx-build parallelization Parallelization has been turned on for custom Sphinx modules. Also rewrote the T1 summary and fixed image links on the experiment guide page. Co-Authored-By: Will Shanks --- docs/_ext/autodoc_analysis.py | 1 + docs/_ext/autodoc_experiment.py | 1 + docs/_ext/autodoc_visualization.py | 1 + docs/_ext/autoref.py | 2 ++ docs/_ext/jupyter_execute_custom.py | 1 + docs/conf.py | 8 ++++---- .../library/characterization/t1.py | 17 +++++------------ 7 files changed, 15 insertions(+), 16 deletions(-) diff --git a/docs/_ext/autodoc_analysis.py b/docs/_ext/autodoc_analysis.py index ded49695c6..259ce8c0fd 100644 --- a/docs/_ext/autodoc_analysis.py +++ b/docs/_ext/autodoc_analysis.py @@ -63,3 +63,4 @@ def setup(app: Sphinx): existing_documenter = app.registry.documenters.get(AnalysisDocumenter.objtype) if existing_documenter is None or not issubclass(existing_documenter, AnalysisDocumenter): app.add_autodocumenter(AnalysisDocumenter, override=True) + return {"parallel_read_safe": True} diff --git a/docs/_ext/autodoc_experiment.py b/docs/_ext/autodoc_experiment.py index 978b52dc24..9d74f026d3 100644 --- a/docs/_ext/autodoc_experiment.py +++ b/docs/_ext/autodoc_experiment.py @@ -79,3 +79,4 @@ def setup(app: Sphinx): existing_documenter = app.registry.documenters.get(ExperimentDocumenter.objtype) if existing_documenter is None or not issubclass(existing_documenter, ExperimentDocumenter): app.add_autodocumenter(ExperimentDocumenter, override=True) + return {"parallel_read_safe": True} diff --git a/docs/_ext/autodoc_visualization.py b/docs/_ext/autodoc_visualization.py index 938c11dcc9..203944fd05 100644 --- a/docs/_ext/autodoc_visualization.py +++ b/docs/_ext/autodoc_visualization.py @@ -97,3 +97,4 @@ def setup(app: Sphinx): existing_documenter = app.registry.documenters.get(DrawerDocumenter.objtype) if existing_documenter is None or not issubclass(existing_documenter, DrawerDocumenter): app.add_autodocumenter(DrawerDocumenter, override=True) + return {"parallel_read_safe": True} diff --git a/docs/_ext/autoref.py b/docs/_ext/autoref.py index 6a4c42a4ae..4d303d0246 100644 --- a/docs/_ext/autoref.py +++ b/docs/_ext/autoref.py @@ -103,3 +103,5 @@ def run(self): def setup(app: Sphinx): app.add_directive("ref_arxiv", Arxiv) app.add_directive("ref_website", WebSite) + + return {"parallel_read_safe": True} diff --git a/docs/_ext/jupyter_execute_custom.py b/docs/_ext/jupyter_execute_custom.py index 2f52812955..8c8b3d40f0 100644 --- a/docs/_ext/jupyter_execute_custom.py +++ b/docs/_ext/jupyter_execute_custom.py @@ -33,3 +33,4 @@ def run(self): def setup(app: Sphinx): app.add_directive("jupyter-execute", JupyterCellCheckEnv, override=True) + return {"parallel_read_safe": True} diff --git a/docs/conf.py b/docs/conf.py index 1455f5a105..be9ad19b48 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -98,10 +98,10 @@ "guides/benchmarking/randomized_benchmarking": "_images/randomized_benchmarking_3_1.png", "guides/measurement/restless_measurements": "_images/restless_shots.png", "guides/benchmarking/state_tomography": "_images/state_tomography_3_0.png", - "guides/characterization/t1": "_images/t1_characterization_0_0.png", - "guides/characterization/t2ramsey": "_images/t2ramsey_characterization_4_0.png", - "guides/characterization/tphi": "_images/tphi_characterization_8_0.png", - "guides/characterization/t2hahn": "_images/t2hahn_characterization_5_0.png", + "guides/characterization/t1": "_images/t1_0_0.png", + "guides/characterization/t2ramsey": "_images/t2ramsey_4_0.png", + "guides/characterization/tphi": "_images/tphi_8_0.png", + "guides/characterization/t2hahn": "_images/t2hahn_5_0.png", } # Add `data keys` and `style parameters` alias. Needed for `expected_*_data_keys` methods in diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index 635cf0c205..139abac5b7 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -28,18 +28,11 @@ class T1(BaseExperiment): # section: overview - Design and analyze experiments for estimating :math:`T_1` relaxation time of the qubit. - - Each experiment consists of the following steps: - - 1. Circuits generation: the circuits set the qubit in the excited state, - wait different time intervals, then measure the qubit. - 2. Backend execution: actually running the circuits on the device - (or simulator). - 3. Analysis of results: deduction of :math:`T_1`, based on the outcomes, - by fitting to an exponential curve. - - | + This experiment estimates the :math:`T_1` relaxation time of the qubit by + generating a series of circuits that excite the qubit then wait for different + intervals before measurement. The resulting data of excited population versus + wait time is fitted to an exponential curve to obtain an estimate for + :math:`T_1`. # section: analysis_ref :class:`.T1Analysis` From 90c78de5f9b018009b25833a7f6b4b2d787bbe6e Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 7 Mar 2023 21:01:02 -0500 Subject: [PATCH 51/64] Fix rebase mistakes Some files got messed up after the rebase, so this commit is to fix problems after comparing with a backup branch. --- docs/guides/characterization/t1.rst | 12 +-- docs/guides/t1.rst | 161 ---------------------------- test/test_composite.py | 106 ------------------ tox.ini | 5 +- 4 files changed, 6 insertions(+), 278 deletions(-) delete mode 100644 docs/guides/t1.rst delete mode 100644 test/test_composite.py diff --git a/docs/guides/characterization/t1.rst b/docs/guides/characterization/t1.rst index b71df500f0..962d82e769 100644 --- a/docs/guides/characterization/t1.rst +++ b/docs/guides/characterization/t1.rst @@ -5,7 +5,7 @@ Background ---------- In a :math:`T_1` experiment, we measure an excited qubit after a delay. -Due to decoherence processes (e.g. amplitude damping channel), it is +Due to decoherence processes (e.g. the amplitude damping channel), it is possible that, at the time of measurement, after the delay, the qubit will not be excited anymore. The larger the delay time is, the more likely is the qubit to fall to the ground state. The goal of the @@ -63,7 +63,7 @@ for qubit 0. delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) # Create an experiment for qubit 0 # with the specified time intervals - exp = T1(qubit=0, delays=delays) + exp = T1(physical_qubits=[0], delays=delays) # Set scheduling method so circuit is scheduled for delay noise simulation exp.set_transpile_options(scheduling_method='asap') @@ -77,14 +77,12 @@ for qubit 0. print(result) - - :math:`T_1` experiments with kerneled measurement ------------------------------------------------- :math:`T_1` experiments can also be done with kerneled measurements. -If we set the run option `meas_level=MeasLevel.KERNELED`, the job -will not discriminate the data and will not label it. In the T1 experiment, +If we set the run option ``meas_level=MeasLevel.KERNELED``, the job +will not discriminate the IQ data and will not label it. In the T1 experiment, since we know that :math:`P(1|t=0)=1`, we will add a circuit with delay=0, and another circuit with a very large delay. In this configuration we know that the data starts from a point [I,Q] that is close to a logical value '1' and ends at a point [I,Q] @@ -122,7 +120,7 @@ that is close to a logical value '0'. ) # Creating a T1 experiment - expT1_kerneled = T1(0, delays) + expT1_kerneled = T1([0], delays) expT1_kerneled.analysis = T1KerneledAnalysis() expT1_kerneled.analysis.set_options(p0={"amp": 1, "tau": t1[0] + t1_estimated_shift, "base": 0}) diff --git a/docs/guides/t1.rst b/docs/guides/t1.rst deleted file mode 100644 index b882969ebc..0000000000 --- a/docs/guides/t1.rst +++ /dev/null @@ -1,161 +0,0 @@ -The `T_1` experiment -======================== - -In a :math:`T_1` experiment, we measure an excited qubit after a delay. -Due to decoherence processes (e.g. amplitude damping channel), it is -possible that, at the time of measurement, after the delay, the qubit -will not be excited anymore. The larger the delay time is, the more -likely is the qubit to fall to the ground state. The goal of the -experiment is to characterize the decay rate of the qubit towards the -ground state. - -We start by fixing a delay time :math:`t` and a number of shots -:math:`s`. Then, by repeating :math:`s` times the procedure of exciting -the qubit, waiting, and measuring, we estimate the probability to -measure :math:`|1\rangle` after the delay. We repeat this process for a -set of delay times, resulting in a set of probability estimates. - -In the absence of state preparation and measurement errors, the -probability to measure \|1> after time :math:`t` is :math:`e^{-t/T_1}`, -for a constant :math:`T_1` (the coherence time), which is our target -number. Since state preparation and measurement errors do exist, the -qubit’s decay towards the ground state assumes the form -:math:`Ae^{-t/T_1} + B`, for parameters :math:`A, T_1`, and :math:`B`, -which we deduce form the probability estimates. To this end, the -:math:`T_1` experiment internally calls the ``curve_fit`` method of -``scipy.optimize``. - -The following code demonstrates a basic run of a :math:`T_1` experiment -for qubit 0. - -.. jupyter-execute:: - - import numpy as np - from qiskit.qobj.utils import MeasLevel - from qiskit_experiments.framework import ParallelExperiment - from qiskit_experiments.library import T1 - from qiskit_experiments.library.characterization.analysis.t1_analysis import T1KerneledAnalysis - - # A T1 simulator - from qiskit.providers.fake_provider import FakeVigo - from qiskit_aer import AerSimulator - from qiskit_aer.noise import NoiseModel - - # A kerneled data simulator - from qiskit_experiments.test.mock_iq_backend import MockIQBackend - from qiskit_experiments.test.mock_iq_helpers import MockIQT1Helper - - # Create a pure relaxation noise model for AerSimulator - noise_model = NoiseModel.from_backend( - FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False - ) - - # Create a fake backend simulator - backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model) - - # Look up target T1 of qubit-0 from device properties - qubit0_t1 = backend.properties().t1(0) - - # Time intervals to wait before measurement - delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) - # Create an experiment for qubit 0 - # with the specified time intervals - exp = T1(physical_qubits=[0], delays=delays) - - # Set scheduling method so circuit is scheduled for delay noise simulation - exp.set_transpile_options(scheduling_method='asap') - - # Run the experiment circuits and analyze the result - exp_data = exp.run(backend=backend, seed_simulator=101).block_for_results() - - # Print the result - display(exp_data.figure(0)) - for result in exp_data.analysis_results(): - print(result) - - -Parallel :math:`T_1` experiments on multiple qubits ---------------------------------------------------- - -To measure :math:`T_1` of multiple qubits in the same experiment, we -create a parallel experiment: - -.. jupyter-execute:: - - # Create a parallel T1 experiment - parallel_exp = ParallelExperiment([T1(physical_qubits=[i], delays=delays) for i in range(2)]) - parallel_exp.set_transpile_options(scheduling_method='asap') - parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() - - # View result data - for result in parallel_data.analysis_results(): - print(result) - - - -:math:`T_1` experiments with kerneled measurement ---------------------------------------------------- -:math:`T_1` experiments can also be done with kerneled measurements. -If we set the run option `meas_level=MeasLevel.KERNELED`, the job -will not discriminate the data and will not label it. In the T1 experiment, -since we know that :math:`P(1|t=0)=1`, we will add a circuit with delay=0, -and another circuit with a very large delay. In this configuration we know that the data starts from -a point [I,Q] that is close to a logical value '1' and ends at a point [I,Q] -that is close to a logical value '0'. - - -.. jupyter-execute:: - - # Experiment - ns = 1e-9 - mu = 1e-6 - - # qubit properties - t1 = [45 * mu, 45 * mu] - t2 = [value/2 for value in t1] - - # we will guess that our guess is 10% off the exact value of t1 for qubit 0. - t1_estimated_shift = t1[0]/10 - - # We use log space for the delays because of the noise properties - delays = np.logspace(1, 11, num=23, base=np.exp(1)) - delays *= ns - - # Adding circuits with delay=0 and long delays so the centers in the IQ plane won't be misplaced. - # Without this, the fitting can provide wrong results. - delays = np.insert(delays, 0, 0) - delays = np.append(delays, [t1[0]*3]) - - num_qubits = 2 - num_shots = 2048 - - backend = MockIQBackend( - MockIQT1Helper(t1=t1, iq_cluster_centers=[((-5.0, -4.0), (-5.0, 4.0)), ((3.0, 1.0), (5.0, -3.0))] - , iq_cluster_width=[1.0, 2.0]) - ) - - # Creating a T1 experiment - expT1_kerneled = T1([0], delays) - expT1_kerneled.analysis = T1KerneledAnalysis() - expT1_kerneled.analysis.set_options(p0={"amp": 1, "tau": t1[0] + t1_estimated_shift, "base": 0}) - - # Running the experiment - expdataT1_kerneled = expT1_kerneled.run(backend=backend, meas_return="avg", - meas_level=MeasLevel.KERNELED, - shots=num_shots).block_for_results() - - # Displaying results - display(expdataT1_kerneled.figure(0)) - for result in expdataT1_kerneled.analysis_results(): - print(result) - -Designing the optimal `T_1` experiment --------------------------------------- - -Given a limited number of shots :math:`t`, what is the optimal selection of delay times -and the number of shots for each? - -.. jupyter-execute:: - - import qiskit.tools.jupyter - %qiskit_copyright diff --git a/test/test_composite.py b/test/test_composite.py deleted file mode 100644 index 3d66e845f8..0000000000 --- a/test/test_composite.py +++ /dev/null @@ -1,106 +0,0 @@ -# This code is part of Qiskit. -# -# (C) Copyright IBM 2021. -# -# This code is licensed under the Apache License, Version 2.0. You may -# obtain a copy of this license in the LICENSE.txt file in the root directory -# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. -# -# Any modifications or derivative works of this code must retain this -# copyright notice, and modified files need to carry a notice indicating -# that they have been altered from the originals. - -"""Class to test composite experiments.""" - -from typing import Optional - -from qiskit.providers import Backend -from qiskit.providers.backend import BackendV1 -from qiskit.providers.options import Options -from qiskit.providers.models import QasmBackendConfiguration -from qiskit.result import Result -from qiskit.test import QiskitTestCase - -from qiskit_experiments.test.mock_job import MockJob -from qiskit_experiments.base_experiment import BaseExperiment -from qiskit_experiments.base_analysis import BaseAnalysis -from qiskit_experiments.composite.parallel_experiment import ParallelExperiment - - -class DummyAnalysis(BaseAnalysis): - """ - Dummy analysis class for test purposes only. - """ - - def _run_analysis(self, experiment_data, **options): - return [], None - - -class DummyExperiment(BaseExperiment): - """ - Dummy experiment class for test purposes only. - """ - - __analysis_class__ = DummyAnalysis - - def circuits(self, backend: Optional[Backend] = None): - return [] - - -class DummyBackend(BackendV1): - """ - Dummy backend for test purposes only. - """ - - def __init__(self): - configuration = QasmBackendConfiguration( - backend_name="dummy_backend", - backend_version="0", - n_qubits=int(1e6), - basis_gates=["barrier", "x", "delay", "measure"], - gates=[], - local=True, - simulator=True, - conditional=False, - open_pulse=False, - memory=False, - max_shots=int(1e6), - coupling_map=None, - ) - super().__init__(configuration) - - @classmethod - def _default_options(cls): - return Options() - - def run(self, run_input, **options): - result = { - "backend_name": "Dummmy backend", - "backend_version": "0", - "qobj_id": 0, - "job_id": 0, - "success": True, - "results": [], - } - return MockJob(backend=self, result=Result.from_dict(result)) - - -class TestComposite(QiskitTestCase): - """ - Test composite experiment behavior. - """ - - def test_parallel_options(self): - """ - Test parallel experiments overriding sub-experiment options. - """ - - exp0 = DummyExperiment(0) - exp2 = DummyExperiment(2) - exp2.set_run_options(shots=2000) - - par_exp = ParallelExperiment([exp0, exp2]) - with self.assertWarnsRegex( - Warning, "Sub-experiment run options are overridden by composite experiment settings." - ): - par_exp.run(DummyBackend()) diff --git a/tox.ini b/tox.ini index 66faeb7444..331e325721 100644 --- a/tox.ini +++ b/tox.ini @@ -9,9 +9,7 @@ install_command = pip install -U {opts} {packages} setenv = VIRTUAL_ENV={envdir} QISKIT_SUPPRESS_PACKAGING_WARNINGS=Y -deps = - git+https://github.com/jupyter/jupyter-sphinx - -r{toxinidir}/requirements-dev.txt +deps = -r{toxinidir}/requirements-dev.txt passenv = OMP_NUM_THREADS QISKIT_PARALLEL @@ -26,7 +24,6 @@ setenv = VIRTUAL_ENV={envdir} QISKIT_SUPPRESS_PACKAGING_WARNINGS=Y deps = - git+https://github.com/jupyter/jupyter-sphinx git+https://github.com/Qiskit/qiskit-terra -r{toxinidir}/requirements-dev.txt passenv = From fd208751378330b746d0d4c09c4b2eaea8b0eb95 Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Tue, 7 Mar 2023 22:46:05 -0500 Subject: [PATCH 52/64] Reverted unnecessary changes The earliest_version flag may have been causing CI release note builds to behave incorrectly while local builds worked. Also regularized some capitalization and minor text fixes. --- docs/_ext/custom_styles/formatter.py | 4 ++-- docs/conf.py | 2 +- .../calibration_management/basis_gate_library.py | 2 +- .../calibration_management/calibrations.py | 4 ++-- qiskit_experiments/curve_analysis/curve_fit.py | 2 +- qiskit_experiments/curve_analysis/data_processing.py | 2 +- .../curve_analysis/visualization/curves.py | 2 +- qiskit_experiments/data_processing/data_processor.py | 2 +- .../data_processing/processor_library.py | 4 ++-- .../data_processing/sklearn_discriminators.py | 4 ++-- qiskit_experiments/framework/base_analysis.py | 6 +++--- qiskit_experiments/framework/base_experiment.py | 6 +++--- .../framework/composite/composite_analysis.py | 2 +- .../framework/composite/composite_experiment.py | 2 +- qiskit_experiments/framework/configs.py | 4 ++-- qiskit_experiments/framework/experiment_data.py | 2 +- qiskit_experiments/framework/json.py | 4 ++-- qiskit_experiments/framework/restless_mixin.py | 10 +++++----- .../library/calibration/rough_frequency.py | 4 ++-- .../analysis/correlated_readout_error_analysis.py | 4 ++-- .../characterization/correlated_readout_error.py | 2 +- qiskit_experiments/library/characterization/drag.py | 6 +++--- .../library/characterization/local_readout_error.py | 2 +- .../library/characterization/resonator_spectroscopy.py | 2 +- .../library/characterization/spectroscopy.py | 2 +- qiskit_experiments/library/characterization/t1.py | 2 +- .../library/randomized_benchmarking/clifford_utils.py | 8 ++++---- .../library/randomized_benchmarking/rb_experiment.py | 2 +- .../library/randomized_benchmarking/rb_utils.py | 2 +- .../library/tomography/basis/local_basis.py | 4 ++-- .../library/tomography/fitters/cvxpy_utils.py | 6 +++--- .../library/tomography/tomography_experiment.py | 2 +- qiskit_experiments/test/mock_iq_helpers.py | 2 +- qiskit_experiments/test/t2hahn_backend.py | 2 +- .../visualization/drawers/base_drawer.py | 4 ++-- .../visualization/plotters/base_plotter.py | 2 +- qiskit_experiments/visualization/utils.py | 8 ++++---- releasenotes/config.yaml | 1 + 38 files changed, 66 insertions(+), 65 deletions(-) diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index d6555a1363..bc6cad4376 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -145,7 +145,7 @@ def format_transpiler_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Transpiler Options", "", - "This option can be set by :py:meth:`set_transpile_options` method.", + "This option can be set by the :py:meth:`set_transpile_options` method.", "", ] format_lines.extend(lines) @@ -159,7 +159,7 @@ def format_run_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Backend Run Options", "", - "This option can be set by :py:meth:`set_run_options` method.", + "This option can be set by the :py:meth:`set_run_options` method.", "", ] format_lines.extend(lines) diff --git a/docs/conf.py b/docs/conf.py index be9ad19b48..7fc1b7f70c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -118,7 +118,7 @@ # Autodoc # ----------------------------------------------------------------------------- -autodoc_default_options = {"inherited-members": None, "private-members": True} +autodoc_default_options = {"inherited-members": None} # If true, figures, tables and code-blocks are automatically numbered if they diff --git a/qiskit_experiments/calibration_management/basis_gate_library.py b/qiskit_experiments/calibration_management/basis_gate_library.py index 713066f5f2..20f37026bf 100644 --- a/qiskit_experiments/calibration_management/basis_gate_library.py +++ b/qiskit_experiments/calibration_management/basis_gate_library.py @@ -219,7 +219,7 @@ def __init__( default_values: Default values for the parameters this dictionary can contain the following keys: "duration", "amp", "β", and "σ". If "σ" is not provided this library will take one fourth of the pulse duration as default value. - link_parameters: if set to True then the amplitude and DRAG parameters of the + link_parameters: If set to True then the amplitude and DRAG parameters of the X and Y gates will be linked as well as those of the SX and SY gates. """ self._link_parameters = link_parameters diff --git a/qiskit_experiments/calibration_management/calibrations.py b/qiskit_experiments/calibration_management/calibrations.py index 127729b73d..8a91ce6182 100644 --- a/qiskit_experiments/calibration_management/calibrations.py +++ b/qiskit_experiments/calibration_management/calibrations.py @@ -616,7 +616,7 @@ def get_template( The registered template schedule. Raises: - CalibrationError: if no template schedule for the given schedule name and qubits + CalibrationError: If no template schedule for the given schedule name and qubits was registered. """ key = ScheduleKey(schedule_name, self._to_tuple(qubits)) @@ -1367,7 +1367,7 @@ def save( default so that when saving to csv all values will be saved. Raises: - CalibrationError: if the files exist and overwrite is not set to True. + CalibrationError: If the files exist and overwrite is not set to True. """ warnings.warn("Schedules are only saved in text format. They cannot be re-loaded.") diff --git a/qiskit_experiments/curve_analysis/curve_fit.py b/qiskit_experiments/curve_analysis/curve_fit.py index 9cceeb78bb..a8c1459232 100644 --- a/qiskit_experiments/curve_analysis/curve_fit.py +++ b/qiskit_experiments/curve_analysis/curve_fit.py @@ -219,7 +219,7 @@ def multi_curve_fit( ``xrange`` the range of xdata values used for fit. Raises: - AnalysisError: if the number of degrees of freedom of the fit is + AnalysisError: If the number of degrees of freedom of the fit is less than 1, or the curve fitting fails. .. note:: diff --git a/qiskit_experiments/curve_analysis/data_processing.py b/qiskit_experiments/curve_analysis/data_processing.py index b0c4365e6f..e191d667c9 100644 --- a/qiskit_experiments/curve_analysis/data_processing.py +++ b/qiskit_experiments/curve_analysis/data_processing.py @@ -92,7 +92,7 @@ def mean_xy_data( duplicated x value entries. Raises: - QiskitError: if "ivw" method is used without providing a sigma. + QiskitError: If "ivw" method is used without providing a sigma. """ x_means = np.unique(xdata, axis=0) y_means = np.zeros(x_means.size) diff --git a/qiskit_experiments/curve_analysis/visualization/curves.py b/qiskit_experiments/curve_analysis/visualization/curves.py index d03e8d1dcf..9985958e15 100644 --- a/qiskit_experiments/curve_analysis/visualization/curves.py +++ b/qiskit_experiments/curve_analysis/visualization/curves.py @@ -55,7 +55,7 @@ def plot_curve_fit( matplotlib.axes.Axes: the matplotlib axes containing the plot. Raises: - ImportError: if matplotlib is not installed. + ImportError: If matplotlib is not installed. """ if ax is None: ax = get_non_gui_ax() diff --git a/qiskit_experiments/data_processing/data_processor.py b/qiskit_experiments/data_processing/data_processor.py index d222ecc5b5..73a90d5f97 100644 --- a/qiskit_experiments/data_processing/data_processor.py +++ b/qiskit_experiments/data_processing/data_processor.py @@ -143,7 +143,7 @@ def _call_internal( Args: data: The data, typically from ExperimentData.data(...), that needs to be processed. This dict or list of dicts also contains the metadata of each experiment. - with_history: if True the history is returned otherwise it is not. + with_history: If True the history is returned otherwise it is not. history_nodes: The nodes, specified by index in the data processing chain, to include in the history. If None is given then all nodes will be included in the history. diff --git a/qiskit_experiments/data_processing/processor_library.py b/qiskit_experiments/data_processing/processor_library.py index 0cc4443bb9..2c5da80196 100644 --- a/qiskit_experiments/data_processing/processor_library.py +++ b/qiskit_experiments/data_processing/processor_library.py @@ -44,7 +44,7 @@ def get_kerneled_processor( the corresponding job. Raises: - DataProcessorError: if the wrong dimensionality reduction for kerneled data + DataProcessorError: If the wrong dimensionality reduction for kerneled data is specified. """ @@ -104,7 +104,7 @@ def get_processor(experiment_data: ExperimentData, analysis_options: Options) -> classified data if it was not given in the analysis options. Raises: - DataProcessorError: if the measurement level is not supported. + DataProcessorError: If the measurement level is not supported. """ metadata = experiment_data.metadata if "job_metadata" in metadata: diff --git a/qiskit_experiments/data_processing/sklearn_discriminators.py b/qiskit_experiments/data_processing/sklearn_discriminators.py index 49d3072004..24fca162ad 100644 --- a/qiskit_experiments/data_processing/sklearn_discriminators.py +++ b/qiskit_experiments/data_processing/sklearn_discriminators.py @@ -38,7 +38,7 @@ def __init__(self, lda: "LinearDiscriminantAnalysis"): untrained discriminator. Raises: - DataProcessorError: if SKlearn could not be imported. + DataProcessorError: If SKlearn could not be imported. """ self._lda = lda self.attributes = [ @@ -112,7 +112,7 @@ def __init__(self, qda: "QuadraticDiscriminantAnalysis"): untrained discriminator. Raises: - DataProcessorError: if SKlearn could not be imported. + DataProcessorError: If SKlearn could not be imported. """ self._qda = qda self.attributes = [ diff --git a/qiskit_experiments/framework/base_analysis.py b/qiskit_experiments/framework/base_analysis.py index 08d8de0129..bc2a9a9b55 100644 --- a/qiskit_experiments/framework/base_analysis.py +++ b/qiskit_experiments/framework/base_analysis.py @@ -118,7 +118,7 @@ def run( Args: experiment_data: the experiment data to analyze. - replace_results: if True clear any existing analysis results and + replace_results: If True clear any existing analysis results and figures in the experiment data and replace with new results. See note for additional information. options: additional analysis options. See class documentation for @@ -128,7 +128,7 @@ def run( An experiment data object containing the analysis results and figures. Raises: - QiskitError: if experiment_data container is not valid for analysis. + QiskitError: If experiment_data container is not valid for analysis. .. note:: **Updating Results** @@ -230,7 +230,7 @@ def _run_analysis( is a list of any figures for the experiment. Raises: - AnalysisError: if the analysis fails. + AnalysisError: If the analysis fails. """ # NOTE: passing kwarg options to _run_analysis should be removed once pass diff --git a/qiskit_experiments/framework/base_experiment.py b/qiskit_experiments/framework/base_experiment.py index b79602db27..f261ff0066 100644 --- a/qiskit_experiments/framework/base_experiment.py +++ b/qiskit_experiments/framework/base_experiment.py @@ -51,7 +51,7 @@ def __init__( experiment_type: Optional, the experiment type string. Raises: - QiskitError: if qubits contains duplicates. + QiskitError: If qubits contains duplicates. """ # Experiment identification metadata self._type = experiment_type if experiment_type else type(self).__name__ @@ -212,7 +212,7 @@ def run( The experiment data object. Raises: - QiskitError: if experiment is run with an incompatible existing + QiskitError: If experiment is run with an incompatible existing ExperimentData container. """ @@ -377,7 +377,7 @@ def set_transpile_options(self, **fields): fields: The fields to update the options Raises: - QiskitError: if `initial_layout` is one of the fields. + QiskitError: If `initial_layout` is one of the fields. """ if "initial_layout" in fields: raise QiskitError( diff --git a/qiskit_experiments/framework/composite/composite_analysis.py b/qiskit_experiments/framework/composite/composite_analysis.py index 6336800865..2860449b11 100644 --- a/qiskit_experiments/framework/composite/composite_analysis.py +++ b/qiskit_experiments/framework/composite/composite_analysis.py @@ -144,7 +144,7 @@ def _component_experiment_data(self, experiment_data: ExperimentData) -> List[Ex component experiment. Raises: - AnalysisError: if the component experiment data cannot be extracted. + AnalysisError: If the component experiment data cannot be extracted. """ if not self._flatten_results: # Retrieve child data for component experiments for updating diff --git a/qiskit_experiments/framework/composite/composite_experiment.py b/qiskit_experiments/framework/composite/composite_experiment.py index a7c7fad109..babf505b9a 100644 --- a/qiskit_experiments/framework/composite/composite_experiment.py +++ b/qiskit_experiments/framework/composite/composite_experiment.py @@ -54,7 +54,7 @@ def __init__( supplied experiments. Raises: - QiskitError: if the provided analysis class is not a CompositeAnalysis + QiskitError: If the provided analysis class is not a CompositeAnalysis instance. """ self._experiments = experiments diff --git a/qiskit_experiments/framework/configs.py b/qiskit_experiments/framework/configs.py index 07e84c037f..d1941b50c8 100644 --- a/qiskit_experiments/framework/configs.py +++ b/qiskit_experiments/framework/configs.py @@ -48,7 +48,7 @@ def experiment(self): BaseExperiment: The experiment reconstructed from the config. Raises: - QiskitError: if the experiment class is not stored, + QiskitError: If the experiment class is not stored, was not successful deserialized, or reconstruction of the experiment fails. """ @@ -99,7 +99,7 @@ def analysis(self): BaseAnalysis: The analysis reconstructed from the config. Raises: - QiskitError: if the analysis class is not stored, + QiskitError: If the analysis class is not stored, was not successful deserialized, or reconstruction of the analysis class fails. """ diff --git a/qiskit_experiments/framework/experiment_data.py b/qiskit_experiments/framework/experiment_data.py index a423bbe1cd..1d831b0d9e 100644 --- a/qiskit_experiments/framework/experiment_data.py +++ b/qiskit_experiments/framework/experiment_data.py @@ -1856,7 +1856,7 @@ def child_data( The requested single or list of child experiment data. Raises: - QiskitError: if the index or ID of the child experiment data + QiskitError: If the index or ID of the child experiment data cannot be found. """ if index is None: diff --git a/qiskit_experiments/framework/json.py b/qiskit_experiments/framework/json.py index 6f7b091da3..01e44f8dcd 100644 --- a/qiskit_experiments/framework/json.py +++ b/qiskit_experiments/framework/json.py @@ -194,7 +194,7 @@ def _decode_and_deserialize(value: Dict, deserializer: Callable, name: Optional[ Deserialized data. Raises: - ValueError: if deserialization fails. + ValueError: If deserialization fails. """ try: with io.BytesIO() as buff: @@ -294,7 +294,7 @@ def _serialize_object(obj: Any, settings: Optional[Dict] = None, safe_float: boo Args: obj: The object to be serialized. settings: Optional, settings for reconstructing the object from kwargs. - safe_float: if True check float values for NaN, inf and -inf + safe_float: If True check float values for NaN, inf and -inf and cast to strings during serialization. Returns: diff --git a/qiskit_experiments/framework/restless_mixin.py b/qiskit_experiments/framework/restless_mixin.py index 6386d261e8..42666e8d56 100644 --- a/qiskit_experiments/framework/restless_mixin.py +++ b/qiskit_experiments/framework/restless_mixin.py @@ -85,12 +85,12 @@ def enable_restless( be logged as restless measurements may have a large amount of noise. Raises: - DataProcessorError: if the attribute rep_delay_range is not defined for the backend. - DataProcessorError: if a data processor has already been set but + DataProcessorError: If the attribute rep_delay_range is not defined for the backend. + DataProcessorError: If a data processor has already been set but override_processor_by_restless is True. - DataProcessorError: if the experiment analysis does not have the data_processor + DataProcessorError: If the experiment analysis does not have the data_processor option. - DataProcessorError: if the rep_delay is equal to or greater than the + DataProcessorError: If the rep_delay is equal to or greater than the T1 time of one of the physical qubits in the experiment and the flag ``ignore_t1_check`` is False. """ @@ -195,7 +195,7 @@ def _t1_check(self, rep_delay: float) -> bool: True if the repetition delay is smaller than the qubit T1 times. Raises: - DataProcessorError: if the T1 values are not defined for the qubits of + DataProcessorError: If the T1 values are not defined for the qubits of the used backend. """ diff --git a/qiskit_experiments/library/calibration/rough_frequency.py b/qiskit_experiments/library/calibration/rough_frequency.py index 6d61b75368..4902b02e49 100644 --- a/qiskit_experiments/library/calibration/rough_frequency.py +++ b/qiskit_experiments/library/calibration/rough_frequency.py @@ -58,7 +58,7 @@ def __init__( qubit frequency in the backend. Raises: - QiskitError: if there are less than three frequency shifts. + QiskitError: If there are less than three frequency shifts. """ super().__init__( @@ -107,7 +107,7 @@ def __init__( qubit frequency in the backend. Raises: - QiskitError: if there are less than three frequency shifts. + QiskitError: If there are less than three frequency shifts. """ super().__init__( diff --git a/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py b/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py index faf650d823..6e9f31b239 100644 --- a/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/correlated_readout_error_analysis.py @@ -103,9 +103,9 @@ def _assignment_matrix_visualization( The generated plot of the assignment matrix Raises: - QiskitError: if _cal_matrices was not set. + QiskitError: If _cal_matrices was not set. - ImportError: if matplotlib was not installed. + ImportError: If matplotlib was not installed. """ diff --git a/qiskit_experiments/library/characterization/correlated_readout_error.py b/qiskit_experiments/library/characterization/correlated_readout_error.py index 466277bfda..d9ed961803 100644 --- a/qiskit_experiments/library/characterization/correlated_readout_error.py +++ b/qiskit_experiments/library/characterization/correlated_readout_error.py @@ -93,7 +93,7 @@ def __init__( backend: Optional, the backend to characterize. Raises: - QiskitError: if args are not valid. + QiskitError: If args are not valid. """ if physical_qubits is None: if backend is None: diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py index 4f4762a5f7..7f4b60d709 100644 --- a/qiskit_experiments/library/characterization/drag.py +++ b/qiskit_experiments/library/characterization/drag.py @@ -113,7 +113,7 @@ def __init__( backend: Optional, the backend to run the experiment on. Raises: - QiskitError: if the schedule does not have a free parameter. + QiskitError: If the schedule does not have a free parameter. """ # Create analysis in finalize to reflect user change to reps @@ -141,13 +141,13 @@ def circuits(self) -> List[QuantumCircuit]: circuits: The circuits that will run the Drag calibration. Raises: - QiskitError: if the number of different repetition series is not three. + QiskitError: If the number of different repetition series is not three. """ schedule = self.experiment_options.schedule beta = next(iter(schedule.parameters)) - # Note: if the pulse has a reserved name, e.g. x, which does not have parameters + # Note: If the pulse has a reserved name, e.g. x, which does not have parameters # then we cannot directly call the gate x and attach a schedule to it. Doing so # would results in QObj errors. drag_gate = Gate(name="Drag(" + schedule.name + ")", num_qubits=1, params=[beta]) diff --git a/qiskit_experiments/library/characterization/local_readout_error.py b/qiskit_experiments/library/characterization/local_readout_error.py index a26bb68f87..be875e1995 100644 --- a/qiskit_experiments/library/characterization/local_readout_error.py +++ b/qiskit_experiments/library/characterization/local_readout_error.py @@ -82,7 +82,7 @@ def __init__( backend: Optional, the backend to characterize. Raises: - QiskitError: if args are not valid. + QiskitError: If args are not valid. """ if physical_qubits is None: if backend is None: diff --git a/qiskit_experiments/library/characterization/resonator_spectroscopy.py b/qiskit_experiments/library/characterization/resonator_spectroscopy.py index a54e695681..8f863e9e93 100644 --- a/qiskit_experiments/library/characterization/resonator_spectroscopy.py +++ b/qiskit_experiments/library/characterization/resonator_spectroscopy.py @@ -177,7 +177,7 @@ def __init__( experiment_options: Key word arguments used to set the experiment options. Raises: - QiskitError: if no frequencies are given and absolute frequencies are desired and + QiskitError: If no frequencies are given and absolute frequencies are desired and no backend is given. """ analysis = ResonatorSpectroscopyAnalysis() diff --git a/qiskit_experiments/library/characterization/spectroscopy.py b/qiskit_experiments/library/characterization/spectroscopy.py index 2eae5f5b73..21a9bf6f7c 100644 --- a/qiskit_experiments/library/characterization/spectroscopy.py +++ b/qiskit_experiments/library/characterization/spectroscopy.py @@ -84,7 +84,7 @@ def __init__( experiment_options: Key word arguments used to set the experiment options. Raises: - QiskitError: if there are less than three frequency shifts. + QiskitError: If there are less than three frequency shifts. """ analysis = analysis or ResonanceAnalysis() diff --git a/qiskit_experiments/library/characterization/t1.py b/qiskit_experiments/library/characterization/t1.py index 139abac5b7..43711a27ce 100644 --- a/qiskit_experiments/library/characterization/t1.py +++ b/qiskit_experiments/library/characterization/t1.py @@ -70,7 +70,7 @@ def __init__( backend: Optional, the backend to run the experiment on. Raises: - ValueError: if the number of delays is smaller than 3 + ValueError: If the number of delays is smaller than 3 """ # Initialize base experiment super().__init__(physical_qubits, analysis=T1Analysis(), backend=backend) diff --git a/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py b/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py index 99a1edd9c7..a63b91459c 100644 --- a/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py +++ b/qiskit_experiments/library/randomized_benchmarking/clifford_utils.py @@ -375,8 +375,8 @@ def _num_from_1q_gate(op: Instruction) -> int: An integer representing a Clifford consisting of a single operation. Raises: - QiskitError: if the input instruction is not a Clifford instruction. - QiskitError: if rz is given with a angle that is not Clifford. + QiskitError: If the input instruction is not a Clifford instruction. + QiskitError: If rz is given with a angle that is not Clifford. """ if op.name in {"delay", "barrier"}: return 0 @@ -454,8 +454,8 @@ def _num_from_2q_gate( An integer representing a Clifford consisting of a single operation. Raises: - QiskitError: if the input instruction is not a Clifford instruction. - QiskitError: if rz is given with a angle that is not Clifford. + QiskitError: If the input instruction is not a Clifford instruction. + QiskitError: If rz is given with a angle that is not Clifford. """ if op.name in {"delay", "barrier"}: return 0 diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py index 1007543ff6..3daf4e2038 100644 --- a/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py +++ b/qiskit_experiments/library/randomized_benchmarking/rb_experiment.py @@ -104,7 +104,7 @@ def __init__( The default is False. Raises: - QiskitError: if any invalid argument is supplied. + QiskitError: If any invalid argument is supplied. """ # Initialize base experiment super().__init__(physical_qubits, analysis=RBAnalysis(), backend=backend) diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_utils.py b/qiskit_experiments/library/randomized_benchmarking/rb_utils.py index b86c77be2c..cae8a1d858 100644 --- a/qiskit_experiments/library/randomized_benchmarking/rb_utils.py +++ b/qiskit_experiments/library/randomized_benchmarking/rb_utils.py @@ -34,7 +34,7 @@ def coherence_limit(nQ=2, T1_list=None, T2_list=None, gatelen=0.1): Returns: float: coherence limited error per gate. Raises: - ValueError: if there are invalid inputs + ValueError: If there are invalid inputs """ # pylint: disable = invalid-name diff --git a/qiskit_experiments/library/tomography/basis/local_basis.py b/qiskit_experiments/library/tomography/basis/local_basis.py index 47abde58ba..ae921b7e21 100644 --- a/qiskit_experiments/library/tomography/basis/local_basis.py +++ b/qiskit_experiments/library/tomography/basis/local_basis.py @@ -59,7 +59,7 @@ def __init__( qubits not specified in this dict. Raises: - QiskitError: if input states or instructions are not valid, or no + QiskitError: If input states or instructions are not valid, or no instructions or states are provided. """ if instructions is None and default_states is None and qubit_states is None: @@ -283,7 +283,7 @@ def __init__( not specified in this dict. Raises: - QiskitError: if the input instructions or POVMs are not valid, or if no + QiskitError: If the input instructions or POVMs are not valid, or if no instructions or POVMs are provided. """ if instructions is None and default_povms is None and qubit_povms is None: diff --git a/qiskit_experiments/library/tomography/fitters/cvxpy_utils.py b/qiskit_experiments/library/tomography/fitters/cvxpy_utils.py index 3b03bba2ad..2e32bfe6b9 100644 --- a/qiskit_experiments/library/tomography/fitters/cvxpy_utils.py +++ b/qiskit_experiments/library/tomography/fitters/cvxpy_utils.py @@ -184,7 +184,7 @@ def trace_constraint( A list of constraints on the real and imaginary parts. Raises: - TypeError: if input variables are not valid. + TypeError: If input variables are not valid. """ if isinstance(mat_r, (list, tuple)): arg_r = cvxpy.sum(mat_r) @@ -225,7 +225,7 @@ def partial_trace_constaint( A list of constraints on the real and imaginary parts. Raises: - TypeError: if input variables are not valid. + TypeError: If input variables are not valid. """ sdim = mat_r.shape[0] output_dim = constraint.shape[0] @@ -258,7 +258,7 @@ def trace_preserving_constaint( A list of constraints on the real and imaginary parts. Raises: - TypeError: if input variables are not valid. + TypeError: If input variables are not valid. """ if isinstance(mat_r, (tuple, list)): sdim = mat_r[0].shape[0] diff --git a/qiskit_experiments/library/tomography/tomography_experiment.py b/qiskit_experiments/library/tomography/tomography_experiment.py index 277c4e6eb0..ff96ae4aad 100644 --- a/qiskit_experiments/library/tomography/tomography_experiment.py +++ b/qiskit_experiments/library/tomography/tomography_experiment.py @@ -100,7 +100,7 @@ def __init__( instance will be set. Raises: - QiskitError: if input params are invalid. + QiskitError: If input params are invalid. """ # Initialize BaseExperiment if physical_qubits is None: diff --git a/qiskit_experiments/test/mock_iq_helpers.py b/qiskit_experiments/test/mock_iq_helpers.py index 9ff0593493..8b626aaf50 100644 --- a/qiskit_experiments/test/mock_iq_helpers.py +++ b/qiskit_experiments/test/mock_iq_helpers.py @@ -494,7 +494,7 @@ def __init__( are different centers for different logical values of the qubit. iq_cluster_width: A list of standard deviation values for the sampling of each qubit. Raises: - ValueError: if probability value is not valid. + ValueError: If probability value is not valid. """ super().__init__(iq_cluster_centers, iq_cluster_width) if max_probability + offset_probability > 1: diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py index 80918f9a69..8d93cfa85f 100644 --- a/qiskit_experiments/test/t2hahn_backend.py +++ b/qiskit_experiments/test/t2hahn_backend.py @@ -183,7 +183,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict: dict: The state of the qubit after operating the gate. Raises: - QiskitError: if angle is not ±π/2 or ±π. Those are the only supported angles. + QiskitError: If angle is not ±π/2 or ±π. Those are the only supported angles. """ if qubit_state["XY plane"]: diff --git a/qiskit_experiments/visualization/drawers/base_drawer.py b/qiskit_experiments/visualization/drawers/base_drawer.py index 499f93092f..e5b7a5e962 100644 --- a/qiskit_experiments/visualization/drawers/base_drawer.py +++ b/qiskit_experiments/visualization/drawers/base_drawer.py @@ -247,7 +247,7 @@ def set_options(self, **fields): fields: The fields to update the options Raises: - AttributeError: if an unknown options is encountered. + AttributeError: If an unknown options is encountered. """ for field in fields: if not hasattr(self._options, field): @@ -265,7 +265,7 @@ def set_figure_options(self, **fields): fields: The fields to update the figure options Raises: - AttributeError: if an unknown figure option is encountered. + AttributeError: If an unknown figure option is encountered. """ for field in fields: if not hasattr(self._figure_options, field): diff --git a/qiskit_experiments/visualization/plotters/base_plotter.py b/qiskit_experiments/visualization/plotters/base_plotter.py index c337adc2e1..a069bd1371 100644 --- a/qiskit_experiments/visualization/plotters/base_plotter.py +++ b/qiskit_experiments/visualization/plotters/base_plotter.py @@ -475,7 +475,7 @@ def set_options(self, **fields): fields: The fields to update in options. Raises: - AttributeError: if an unknown option is encountered. + AttributeError: If an unknown option is encountered. """ for field in fields: if not hasattr(self._options, field): diff --git a/qiskit_experiments/visualization/utils.py b/qiskit_experiments/visualization/utils.py index da68534ed8..b832ccafcf 100644 --- a/qiskit_experiments/visualization/utils.py +++ b/qiskit_experiments/visualization/utils.py @@ -94,10 +94,10 @@ def register_data(self, data: Union[List, np.ndarray], dim: Optional[int] = None Defaults to None. Raises: - QiskitError: if the data is not two-dimensional and ``dim`` is not set. - QiskitError: if the data does not contain one-dimensional values when + QiskitError: If the data is not two-dimensional and ``dim`` is not set. + QiskitError: If the data does not contain one-dimensional values when ``dim`` is set. - QiskitError: if ``dim`` is not an index for two-dimensions: i.e., + QiskitError: If ``dim`` is not an index for two-dimensions: i.e., :math:`0\leq{}\text{dim}<2`. """ data = np.asarray(data) @@ -238,7 +238,7 @@ def extent(self) -> ExtentTuple: """An extent array for the registered data, multiplier, and aspect ratio. Raises: - QiskitError: if the resulting extent tuple is not finite. This can occur if + QiskitError: If the resulting extent tuple is not finite. This can occur if no data was registered before calling :meth:`extent`. Returns: diff --git a/releasenotes/config.yaml b/releasenotes/config.yaml index 76062aec91..719160ee1c 100644 --- a/releasenotes/config.yaml +++ b/releasenotes/config.yaml @@ -84,3 +84,4 @@ template: | needs to be worded so that it does not depend on any information only available in another section, such as the prelude. This may mean repeating some details. +earliest_version: 0.1.0 \ No newline at end of file From e8c6b4fb832bbf4520e515762881f0c460a8c97b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 9 Mar 2023 14:32:43 -0500 Subject: [PATCH 53/64] Turn on -W and pin theme dependency to git repo The metadata fix is live on qiskit-sphinx-theme but not released yet, so this commit pins the dependency to the git repo. The `-W` flag is turned on only for the CI build because the mac build still has warnings. Also updated component_experiment and T2/T2* explanations and added some minor style fixes. --- .github/workflows/main.yml | 2 +- docs/_ext/custom_styles/formatter.py | 10 +- docs/guides/characterization/t2hahn.rst | 11 +- docs/guides/characterization/t2ramsey.rst | 29 +-- docs/tutorials/custom_experiment.rst | 14 +- docs/tutorials/getting_started.rst | 233 ++++++++++-------- .../library/characterization/t2hahn.py | 16 +- .../library/characterization/t2ramsey.py | 16 +- releasenotes/config.yaml | 2 +- requirements-dev.txt | 3 +- 10 files changed, 194 insertions(+), 142 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 09a46c01e0..f4c3907a27 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -97,7 +97,7 @@ jobs: python -m pip install -U tox sudo apt-get install -y pandoc graphviz - name: Build Docs - run: tox -edocs + run: tox -edocs -- -W - name: Compress Artifacts run: | mkdir artifacts diff --git a/docs/_ext/custom_styles/formatter.py b/docs/_ext/custom_styles/formatter.py index bc6cad4376..ca606f0522 100644 --- a/docs/_ext/custom_styles/formatter.py +++ b/docs/_ext/custom_styles/formatter.py @@ -117,7 +117,7 @@ def format_experiment_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Experiment Options", "", - "These options can be set by the :py:meth:`set_experiment_options` method.", + "These options can be set by the :meth:`set_experiment_options` method.", "", ] format_lines.extend(lines) @@ -131,7 +131,7 @@ def format_analysis_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Analysis Options", "", - "These options can be set by the :py:meth:`analysis.set_options` method.", + "These options can be set by the :meth:`analysis.set_options` method.", "", ] format_lines.extend(lines) @@ -145,7 +145,7 @@ def format_transpiler_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Transpiler Options", "", - "This option can be set by the :py:meth:`set_transpile_options` method.", + "This option can be set by the :meth:`set_transpile_options` method.", "", ] format_lines.extend(lines) @@ -159,7 +159,7 @@ def format_run_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Backend Run Options", "", - "This option can be set by the :py:meth:`set_run_options` method.", + "This option can be set by the :meth:`set_run_options` method.", "", ] format_lines.extend(lines) @@ -177,7 +177,7 @@ def format_analysis_opts(self, lines: List[str]) -> List[str]: format_lines = [ ".. rubric:: Run Options", "", - "These are the keyword arguments of :py:meth:`run` method.", + "These are the keyword arguments of :meth:`run` method.", "", ] format_lines.extend(lines) diff --git a/docs/guides/characterization/t2hahn.rst b/docs/guides/characterization/t2hahn.rst index 97f9d225d4..2eef6441e1 100644 --- a/docs/guides/characterization/t2hahn.rst +++ b/docs/guides/characterization/t2hahn.rst @@ -7,11 +7,12 @@ The purpose of the :math:`T_2` Hahn Echo experiment is to determine the In this experiment, we would like to get a more precise estimate of the qubit’s decay time. :math:`T_2` represents the amount of time required for a single qubit's Bloch vector projection on the XY plane to fall to approximately 37% (:math:`\frac{1}{e}`) of -its initial amplitude. Hahn Echo experiment and the Carr-Purcell-Meiboom-Gill (CPMG) -sequence are experiments to estimate :math:`T_2` which are robust to the detuning -frequency, or the difference between the qubit frequency and the pulse frequency of the -applied rotation. The decay in amplitude causes the probability function to take the -following form: +its initial amplitude. Unlike :math:`T_2^*`, which is measured by :class:`.T2Ramsey`, +:math:`T_2` is insensitive to inhomogenous broadening. Hahn Echo experiment and the +Carr-Purcell-Meiboom-Gill (CPMG) sequence are experiments to estimate :math:`T_2` which +are robust to the detuning frequency, or the difference between the qubit frequency and +the pulse frequency of the applied rotation. The decay in amplitude causes the +probability function to take the following form: .. math:: f(t) = A \cdot e^{-\frac{t}{T_2}}+ B diff --git a/docs/guides/characterization/t2ramsey.rst b/docs/guides/characterization/t2ramsey.rst index cdf9e6dcba..88529c4b0a 100644 --- a/docs/guides/characterization/t2ramsey.rst +++ b/docs/guides/characterization/t2ramsey.rst @@ -1,20 +1,21 @@ T2 Ramsey Characterization ========================== -The purpose of the :math:`T_2` Ramsey experiment is to determine two of -the qubit's properties: *Ramsey* or *detuning frequency* and -:math:`T_2^\ast`. In this experiment, we would like to get a more precise estimate of the -qubit's frequency given a rough estimate. The difference between the frequency used for the -control rotation pulses and the qubit transition frequency is called the -*detuning frequency*. This part of the experiment is called a *Ramsey -Experiment*. :math:`T_2^\ast` represents the rate of decay toward a -mixed state, when the qubit is initialized to the -:math:`\left|1\right\rangle` state. - -Since the detuning frequency is relatively small, we add a phase gate to -the circuit to enable better measurement. The actual frequency measured -is the sum of the detuning frequency and the user induced *oscillation -frequency* (``osc_freq`` parameter). +The purpose of the :math:`T_2` Ramsey experiment is to determine two of the qubit's +properties: *Ramsey* or *detuning frequency* and :math:`T_2^\ast`. In this experiment, +we would like to get a more precise estimate of the qubit's frequency given a rough +estimate. The difference between the frequency used for the control rotation pulses and +the qubit transition frequency is called the *detuning frequency*. This part of the +experiment is called a *Ramsey Experiment*. :math:`T_2^\ast` represents the rate of +decay toward a mixed state, when the qubit is initialized to the +:math:`\left|1\right\rangle` state. It is the dephasing time or the transverse +relaxation time of the qubit on the Bloch sphere as a result of both energy relaxation +and pure dephasing in the transverse plane. Unlike :math:`T_2`, which is measured by +:class:`.T2Hahn`, :math:`T_2^*` is sensitive to inhomogenous broadening. + +Since the detuning frequency is relatively small, we add a phase gate to the circuit to +enable better measurement. The actual frequency measured is the sum of the detuning +frequency and the user induced *oscillation frequency* (``osc_freq`` parameter). .. jupyter-execute:: diff --git a/docs/tutorials/custom_experiment.rst b/docs/tutorials/custom_experiment.rst index cbc6f8c5c1..6693c72b20 100644 --- a/docs/tutorials/custom_experiment.rst +++ b/docs/tutorials/custom_experiment.rst @@ -206,13 +206,13 @@ be able to set how many repetitions of the circuit to run: return options -Now we write the ``circuits()`` method. We need to take the input circuit in ``self._circuit`` -and add our random Paulis as well as measurement at the end. We use the built-in property -:attr:`~.BaseExperiment.num_qubits` of :class:`~.BaseExperiment` to get the number of qubits -in the experiment. We keep track of the list of qubits and classical -Note that the circuits themselves are always built on qubits `0` to `length of the circuit - 1`, -and not the actual physical qubit indices given in ``physical_qubits``. During experiment -execution, a mapping is performed to place these circuits on the physical layout. +Now we write the ``circuits()`` method. We need to take the input circuit in +``self._circuit`` and add our random Paulis as well as measurement at the end. We use +the built-in property :attr:`~.BaseExperiment.num_qubits` of :class:`~.BaseExperiment` +to get the number of qubits in the experiment. We keep track of the list of qubits and +classical Note that the circuits themselves are always built on qubits `0` to `length of +the circuit - 1`, and not the actual physical qubit indices given in +``physical_qubits``, as discussed in :doc:`getting_started`. .. jupyter-input:: diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 999c6f0134..54c62093a3 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -6,12 +6,11 @@ Installation ============ Qiskit Experiments is built on top of Qiskit, so we recommend that you first install -Qiskit following its :external+qiskit:doc:`installation guide `. -Qiskit Experiments supports the same platforms and Python versions (currently 3.7+) as -Qiskit itself. +Qiskit following its :external+qiskit:doc:`installation guide `. Qiskit +Experiments supports the same platforms and Python versions (currently 3.7+) as Qiskit +itself. -Qiskit Experiments releases can be installed via the Python package manager -``pip``: +Qiskit Experiments releases can be installed via the Python package manager ``pip``: .. jupyter-input:: @@ -24,7 +23,7 @@ install the latest main branch: python -m pip install git+https://github.com/Qiskit/qiskit-experiments.git -If you want to develop the package, you can install Qiskit Experiments from source by +If you want to develop the package, you can install Qiskit Experiments from source by cloning the repository: .. jupyter-input:: @@ -32,25 +31,24 @@ cloning the repository: git clone https://github.com/Qiskit/qiskit-experiments.git python -m pip install -e qiskit-experiments -The ``-e`` option will keep your installed package up to date as you make or pull new +The ``-e`` option will keep your installed package up to date as you make or pull new changes. Running your first experiment ============================= -Let's run a :class:`.T1` Experiment, which estimates the characteristic relaxation -time of a qubit from the excited state to the ground state, also known as :math:`T_1`, by measuring the -excited state population after varying delays. First, we have to import the experiment from the -Qiskit Experiments library: +Let's run a :class:`.T1` Experiment, which estimates the characteristic relaxation time +of a qubit from the excited state to the ground state, also known as :math:`T_1`, by +measuring the excited state population after varying delays. First, we have to import +the experiment from the Qiskit Experiments library: .. jupyter-execute:: from qiskit_experiments.library import T1 -Experiments must be run on a backend. We're going to use a simulator, -:class:`~qiskit.providers.fake_provider.FakeVigo`, for -this example, but you can use any IBM backend, real or simulated, that you can access -through Qiskit. +Experiments must be run on a backend. We're going to use a simulator, +:class:`~qiskit.providers.fake_provider.FakeVigo`, for this example, but you can use any +IBM backend, real or simulated, that you can access through Qiskit. .. jupyter-execute:: @@ -68,11 +66,11 @@ through Qiskit. All experiments require a ``physical_qubits`` parameter as input that specifies which physical qubit or qubits the circuits will be executed on. The qubits must be given as a -Python sequence (usually a tuple or a list). In addition, the :math:`T_1` experiment has a second -required parameter, ``delays``, which is a list of times in seconds at which to measure -the excited state population. In this example, we'll run the :math:`T_1` experiment on qubit 0, -and use the ``t1`` backend property of this qubit to give us a good -estimate for the sweep range of the delays. +Python sequence (usually a tuple or a list). In addition, the :math:`T_1` experiment has +a second required parameter, ``delays``, which is a list of times in seconds at which to +measure the excited state population. In this example, we'll run the :math:`T_1` +experiment on qubit 0, and use the ``t1`` backend property of this qubit to give us a +good estimate for the sweep range of the delays. .. jupyter-execute:: @@ -81,10 +79,10 @@ estimate for the sweep range of the delays. delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5) exp = T1(physical_qubits=(0,), delays=delays) -The circuits encapsulated by the experiment can be accessed using the experiment's -:meth:`~.BaseExperiment.circuits` method, which returns a list of circuits that can -be run on a backend. Let's print the range of delay times we're sweeping over and -draw the first and last circuits for our :math:`T_1` experiment: +The circuits encapsulated by the experiment can be accessed using the experiment's +:meth:`~.BaseExperiment.circuits` method, which returns a list of circuits that can be +run on a backend. Let's print the range of delay times we're sweeping over and draw the +first and last circuits for our :math:`T_1` experiment: .. jupyter-execute:: @@ -100,7 +98,7 @@ As expected, the delay block spans the full range of time values that we specifi The ExperimentData class ======================== -After instantiating the experiment, we run the experiment by calling +After instantiating the experiment, we run the experiment by calling :meth:`~.BaseExperiment.run` with our backend of choice. This transpiles our experiment circuits then packages them into jobs that are run on the backend. @@ -108,44 +106,47 @@ circuits then packages them into jobs that are run on the backend. See the how-tos for :doc:`customizing job splitting ` when running an experiment. -This statement returns the :class:`.ExperimentData` class containing the results of the experiment, -so it's crucial that we assign the output to a data variable. We could have also provided the backend -at the instantiation of the experiment, but specifying the backend at run time -allows us to run the same exact experiment on different backends should we choose to do so. +This statement returns the :class:`.ExperimentData` class containing the results of the +experiment, so it's crucial that we assign the output to a data variable. We could have +also provided the backend at the instantiation of the experiment, but specifying the +backend at run time allows us to run the same exact experiment on different backends +should we choose to do so. .. jupyter-execute:: exp_data = exp.run(backend=backend).block_for_results() -The :meth:`~.ExperimentData.block_for_results` method is optional and is used to block execution -of subsequent code until the experiment has fully completed execution and analysis. If +The :meth:`~.ExperimentData.block_for_results` method is optional and is used to block +execution of subsequent code until the experiment has fully completed execution and +analysis. If .. jupyter-input:: exp_data = exp.run(backend=backend) -is executed instead, the statement will finish running as soon as the jobs are submitted, -but the analysis callback won't populate -``exp_data`` with results until the entire process has finished. In this case, there are -two useful methods in the :class:`.ExperimentData`, :meth:`~.ExperimentData.job_status` -and :meth:`~.ExperimentData.analysis_status`, that return the current status of the job -and analysis, respectively: +is executed instead, the statement will finish running as soon as the jobs are +submitted, but the analysis callback won't populate ``exp_data`` with results until the +entire process has finished. In this case, there are two useful methods in the +:class:`.ExperimentData`, :meth:`~.ExperimentData.job_status` and +:meth:`~.ExperimentData.analysis_status`, that return the current status of the job and +analysis, respectively: .. jupyter-execute:: print(exp_data.job_status()) print(exp_data.analysis_status()) -Once the analysis is complete, figures are retrieved using the :meth:`~.ExperimentData.figure` method. See the -:doc:`visualization module ` tutorial on how to customize figures for an experiment. For our :math:`T_1` -experiment, we have a single figure showing the raw data and fit to the exponential -decay model of the :math:`T_1` experiment: +Once the analysis is complete, figures are retrieved using the +:meth:`~.ExperimentData.figure` method. See the :doc:`visualization module +` tutorial on how to customize figures for an experiment. For our +:math:`T_1` experiment, we have a single figure showing the raw data and fit to the +exponential decay model of the :math:`T_1` experiment: .. jupyter-execute:: display(exp_data.figure(0)) -The fit results and associated parameters are accessed with +The fit results and associated parameters are accessed with :meth:`~.ExperimentData.analysis_results`: .. jupyter-execute:: @@ -153,23 +154,30 @@ The fit results and associated parameters are accessed with for result in exp_data.analysis_results(): print(result) -Results can be indexed numerically or using their name. Each analysis result value is a ``UFloat`` object from the ``uncertainties`` package. -The nominal value and standard deviation of each value can be accessed as follows: +Results can be indexed numerically (starting from 0) or using their name. + +.. note:: + See the :meth:`~.ExperimentData.analysis_results` API documentation for more + advanced usage patterns to access subsets of analysis results. + +Each analysis +result value is a ``UFloat`` object from the ``uncertainties`` package. The nominal +value and standard deviation of each value can be accessed as follows: .. jupyter-execute:: print(exp_data.analysis_results("T1").value.nominal_value) print(exp_data.analysis_results("T1").value.std_dev) -For further documentation on how to work with UFloats, consult the ``uncertainties`` +For further documentation on how to work with UFloats, consult the ``uncertainties`` :external+uncertainties:doc:`user_guide`. -Raw circuit output data and its associated metadata can be accessed with the -:meth:`~.ExperimentData.data` property. Data is indexed by the circuit -it corresponds to. Depending on the measurement level set in the experiment, the raw data -will either be in the key ``counts`` (level 2) or ``memory`` (level 1 IQ data). Circuit metadata -contains information set by the experiment on a circuit-by-circuit basis; ``xval`` -is used by the analysis to extract the x value for each circuit when fitting the data. +Raw circuit output data and its associated metadata can be accessed with the +:meth:`~.ExperimentData.data` property. Data is indexed by the circuit it corresponds +to. Depending on the measurement level set in the experiment, the raw data will either +be in the key ``counts`` (level 2) or ``memory`` (level 1 IQ data). Circuit metadata +contains information set by the experiment on a circuit-by-circuit basis; ``xval`` is +used by the analysis to extract the x value for each circuit when fitting the data. .. jupyter-execute:: @@ -192,14 +200,15 @@ The actual backend jobs that were executed for the experiment can be accessed wi Setting experiment options ========================== -It's often insufficient to run an experiment with only its default options. -There are four types of options one can set for an experiment: +It's often insufficient to run an experiment with only its default options. There are +four types of options one can set for an experiment: Run options ----------- -These options are passed to the experiment's :meth:`~.BaseExperiment.run` method and then to the ``run()`` -method of your specified backend. Any run option that your backend supports can be set: +These options are passed to the experiment's :meth:`~.BaseExperiment.run` method and +then to the ``run()`` method of your specified backend. Any run option that your backend +supports can be set: .. jupyter-input:: @@ -207,8 +216,8 @@ method of your specified backend. Any run option that your backend supports can meas_level=MeasLevel.CLASSIFIED, meas_return="avg") -Consult the documentation of :meth:`qiskit.execute_function` or the run method of -your specific backend type for valid options. +Consult the documentation of :meth:`qiskit.execute_function` or the run method of your +specific backend type for valid options. Transpile options ----------------- @@ -226,68 +235,97 @@ Consult the documentation of :func:`qiskit.compiler.transpile` for valid options Experiment options ------------------ These options are unique to each experiment class. Many experiment options can be set -upon experiment instantiation, but can also be explicitly set via +upon experiment instantiation, but can also be explicitly set via :meth:`~BaseExperiment.set_experiment_options`: .. jupyter-input:: - exp = T1(physical_qubits=(i,), delays=delays) - exp.set_experiment_options(delays=new_delays) + exp = T1(physical_qubits=(i,), delays=delays) + exp.set_experiment_options(delays=new_delays) -Consult the :doc:`API documentation ` for the options of each experiment class. +Consult the :doc:`API documentation ` for the options of each experiment +class. Analysis options ---------------- -These options are unique to each analysis class. Unlike the other options, analyis options -are not directly set via the experiment object -but use instead a method of the associated ``analysis``: +These options are unique to each analysis class. Unlike the other options, analyis +options are not directly set via the experiment object but use instead a method of the +associated ``analysis``: -.. jupyter-input:: +.. jupyter-execute:: - exp = rb.StandardRB(physical_qubits=(0,), - lengths=list(range(1, 300, 30)), - seed=123, - backend=backend) - exp.analysis.set_options(gate_error_ratio=None) + from qiskit_experiments.library import StandardRB -Consult the :doc:`API documentation ` for the options of each + exp = StandardRB(physical_qubits=(0,), + lengths=list(range(1, 300, 30)), + seed=123, + backend=backend) + exp.analysis.set_options(gate_error_ratio=None) + +Consult the :doc:`API documentation ` for the options of each experiment's analysis class. Running experiments on multiple qubits ====================================== -To run experiments across many qubits of the same device, we use **composite experiments**. -A composite experiment is a parent object that contains one or more child -experiments, which may themselves be composite. There are two core types of composite experiments: +To run experiments across many qubits of the same device, we use **composite +experiments**. A composite experiment is a parent object that contains one or more child +experiments, which may themselves be composite. There are two core types of composite +experiments: * **Parallel experiments** run across qubits simultaneously as set by the user. The - circuits of child experiments are combined into new circuits that map circuit gates onto - qubits in parallel. Therefore, the circuits in child experiments *cannot* overlap in the ``physical_qubits`` - parameter. The marginalization of measurement data for analysis of each child experiment is handled automatically. -* **Batch experiments** run consecutively in time. These child circuits *can* overlap in qubits used. + circuits of child experiments are combined into new circuits that map circuit gates + onto qubits in parallel. Therefore, the circuits in child experiments *cannot* overlap + in the ``physical_qubits`` parameter. The marginalization of measurement data for + analysis of each child experiment is handled automatically. +* **Batch experiments** run consecutively in time. These child circuits *can* overlap in + qubits used. -Here's an example of measuring the :math:`T_1` of two qubits on the same device simultaneously -in a parallel experiment: +Using parallel experiments, we can measure the :math:`T_1` of one qubit while doing a +:class:`.StandardRB` experiment on other qubits simultaneously on the same device: .. jupyter-execute:: from qiskit_experiments.framework import ParallelExperiment - parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)]) - parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() + child_exp1 = T1(physical_qubits=(2,), delays=delays) + child_exp2 = StandardRB(physical_qubits=(3,1), lengths=np.arange(1,100,10), num_samples=2) + parallel_exp = ParallelExperiment([child_exp1, child_exp2]) + +Note that when the transpile and run options are set for a composite experiment, the +child experiments's options are also set to the same options recursively. Let's examine +how the parallel experiment is constructed by visualizing child and parent circuits. The +child experiments can be accessed via the +:meth:`~.ParallelExperiment.component_experiment` method, which indexes from zero: + +.. jupyter-execute:: -Note that when the transpile and run options are set for a composite experiment, the child -experiments's options are also set to the same options recursively. Let's examine how the parallel -experiment is constructed by visualizing the circuits: + parallel_exp.component_experiment(0).circuits()[0].draw(output='mpl') + +.. jupyter-execute:: + + parallel_exp.component_experiment(1).circuits()[0].draw(output='mpl') + +The circuits of all experiments assume they're acting on virtual qubits starting from +index 0. In the case of a parallel experiment, the child experiment +circuits are composed together and then reassigned virtual qubit indices: .. jupyter-execute:: parallel_exp.circuits()[0].draw(output='mpl') -We see that the :math:`T_1` circuits on qubits 0 and 1 have been parallelized to run simultaneously. +During experiment transpilation, a mapping is performed to place these circuits on the +physical layout. We can see the effects of transpilation by looking at the transpiled +circuit, which is accessed via the internal method ``_transpiled_circuits()``. Now the +:class:`.T1` experiment is correctly placed on physical qubit 2 and the Randomized +Benchmarking experiment is on physical qubits 3, 1. + +.. jupyter-execute:: + + parallel_exp._transpiled_circuits()[0].draw(output='mpl') -:class:`.ParallelExperiment` and :class:`.BatchExperiment` classes can be nested +:class:`.ParallelExperiment` and :class:`.BatchExperiment` classes can also be nested arbitrarily to make complex composite experiments. .. figure:: ./images/compositeexperiments.png @@ -296,27 +334,30 @@ arbitrarily to make complex composite experiments. Viewing child experiment data ----------------------------- -The experiment data returned from a composite experiment contains -individual analysis results for each child experiment that can be accessed -using :meth:`~.ExperimentData.child_data`. By default, the parent data object does -not contain analysis results. +The experiment data returned from a composite experiment contains individual analysis +results for each child experiment that can be accessed using +:meth:`~.ExperimentData.child_data`. By default, the parent data object does not contain +analysis results. .. jupyter-execute:: + parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() + for i, sub_data in enumerate(parallel_data.child_data()): print("Component experiment",i) display(sub_data.figure(0)) for result in sub_data.analysis_results(): print(result) -If you want the parent data object to contain the analysis results instead, -you can set the ``flatten_results`` flag to true to flatten the results of all component -experiments into one level: +If you want the parent data object to contain the analysis results instead, you can set +the ``flatten_results`` flag to true to flatten the results of all component experiments +into one level: .. jupyter-execute:: - parallel_exp = ParallelExperiment([T1(physical_qubits=(i,), delays=delays) for i in range(2)], - flatten_results=True) + parallel_exp = ParallelExperiment( + [T1(physical_qubits=(i,), delays=delays) for i in range(2)], flatten_results=True + ) parallel_data = parallel_exp.run(backend, seed_simulator=101).block_for_results() for result in parallel_data.analysis_results(): diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py index bf77a51dfd..5ed9db1e7d 100644 --- a/qiskit_experiments/library/characterization/t2hahn.py +++ b/qiskit_experiments/library/characterization/t2hahn.py @@ -26,15 +26,16 @@ class T2Hahn(BaseExperiment): - r"""An experiment to measure the dephasing time using Hahn echos. + r"""An experiment to measure the dephasing time insensitive to inhomogeneous + broadening using Hahn echos. # section: overview - This experiment is used to estimate T2 noise of a single qubit. - - See `Qiskit Textbook `_ for a more detailed explanation on - these properties. + This experiment is used to estimate the :math:`T_2` time of a single qubit. + :math:`T_2` is the dephasing time or the transverse relaxation time of the qubit + on the Bloch sphere as a result of both energy relaxation and pure dephasing in + the transverse plane. Unlike :math:`T_2^*`, which is measured by + :class:`.T2Ramsey`, :math:`T_2` is insensitive to inhomogenous broadening. This experiment consists of a series of circuits of the form @@ -58,6 +59,9 @@ class T2Hahn(BaseExperiment): # section: analysis_ref :class:`T2HahnAnalysis` + + # section: reference + .. ref_arxiv:: 1 1904.06560 """ @classmethod diff --git a/qiskit_experiments/library/characterization/t2ramsey.py b/qiskit_experiments/library/characterization/t2ramsey.py index 8098d26eba..f661c55315 100644 --- a/qiskit_experiments/library/characterization/t2ramsey.py +++ b/qiskit_experiments/library/characterization/t2ramsey.py @@ -27,16 +27,17 @@ class T2Ramsey(BaseExperiment): - r"""An experiment to measure the effective qubit dephasing time and Ramsey frequency. + r"""An experiment to measure the Ramsey frequency and the qubit dephasing time + sensitive to inhomogeneous broadening. # section: overview This experiment is used to estimate two properties for a single qubit: - T2* and Ramsey frequency. - - See `Qiskit Textbook `_ for a more detailed explanation on - these properties. + :math:`T_2^*` and Ramsey frequency. :math:`T_2^*` is the dephasing time + or the transverse relaxation time of the qubit on the Bloch sphere as a result + of both energy relaxation and pure dephasing in the transverse plane. Unlike + :math:`T_2`, which is measured by :class:`.T2Hahn`, :math:`T_2^*` is sensitive + to inhomogenous broadening. This experiment consists of a series of circuits of the form @@ -58,6 +59,9 @@ class T2Ramsey(BaseExperiment): # section: analysis_ref :class:`T2RamseyAnalysis` + + # section: reference + .. ref_arxiv:: 1 1904.06560 """ @classmethod diff --git a/releasenotes/config.yaml b/releasenotes/config.yaml index 719160ee1c..cd18b4b856 100644 --- a/releasenotes/config.yaml +++ b/releasenotes/config.yaml @@ -84,4 +84,4 @@ template: | needs to be worded so that it does not depend on any information only available in another section, such as the prelude. This may mean repeating some details. -earliest_version: 0.1.0 \ No newline at end of file +earliest_version: 0.1.0 diff --git a/requirements-dev.txt b/requirements-dev.txt index 56de64bdd0..42963d3110 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -5,7 +5,8 @@ pylint==2.7.1 jinja2==3.0.3 sphinx~=5.0 jupyter-sphinx>=0.4.0 -qiskit-sphinx-theme~=1.10 +# Temporary qiskit-sphinx-theme git repository dependency until the next release +git+https://github.com/Qiskit/qiskit_sphinx_theme.git sphinx-autodoc-typehints<=1.20.2 sphinx-design==0.3.0 pygments>=2.4 From fe8b9a687f6c39494e4bddcb175a116d36759c9b Mon Sep 17 00:00:00 2001 From: Helena Zhang Date: Thu, 9 Mar 2023 16:09:43 -0500 Subject: [PATCH 54/64] Pin theme version to commit id Also updated the navigation to link to individual tutorials and updated the composite experiment figure to be more readable. --- docs/howtos/cloud_service.rst | 2 +- docs/index.rst | 16 +++++++++++++++- docs/tutorials/getting_started.rst | 13 +++++++------ .../tutorials/images/compositeexperiments.png | Bin 677880 -> 763049 bytes .../framework/restless_mixin.py | 8 ++++---- requirements-dev.txt | 4 ++-- 6 files changed, 29 insertions(+), 14 deletions(-) diff --git a/docs/howtos/cloud_service.rst b/docs/howtos/cloud_service.rst index 8b15605a86..7a32b3c8d6 100644 --- a/docs/howtos/cloud_service.rst +++ b/docs/howtos/cloud_service.rst @@ -191,4 +191,4 @@ See also -------- * `Experiments web interface documentation `__ - + diff --git a/docs/index.rst b/docs/index.rst index 308c91f15b..ddddafd719 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -97,16 +97,30 @@ We've divided up the documentation into four sections with different purposes: To the API reference +.. toctree:: + :hidden: + :caption: Tutorials + + All Tutorials + tutorials/intro + tutorials/getting_started + Calibrations + Data Processor + Curve Analysis + Visualization + Custom Experiments + .. toctree:: :hidden: - tutorials/index howtos/index guides/index apidocs/index release_notes GitHub +| + .. Hiding - Indices and tables :ref:`genindex` :ref:`modindex` diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst index 54c62093a3..c611d28328 100644 --- a/docs/tutorials/getting_started.rst +++ b/docs/tutorials/getting_started.rst @@ -282,8 +282,9 @@ experiments: * **Batch experiments** run consecutively in time. These child circuits *can* overlap in qubits used. -Using parallel experiments, we can measure the :math:`T_1` of one qubit while doing a -:class:`.StandardRB` experiment on other qubits simultaneously on the same device: +Using parallel experiments, we can measure the :math:`T_1` of one qubit while doing a +standard Randomized Benchmarking :class:`.StandardRB` experiment on other qubits +simultaneously on the same device: .. jupyter-execute:: @@ -316,10 +317,10 @@ circuits are composed together and then reassigned virtual qubit indices: parallel_exp.circuits()[0].draw(output='mpl') During experiment transpilation, a mapping is performed to place these circuits on the -physical layout. We can see the effects of transpilation by looking at the transpiled -circuit, which is accessed via the internal method ``_transpiled_circuits()``. Now the -:class:`.T1` experiment is correctly placed on physical qubit 2 and the Randomized -Benchmarking experiment is on physical qubits 3, 1. +physical layout. We can see its effects by looking at the transpiled +circuit, which is accessed via the internal method ``_transpiled_circuits()``. 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