Migrate the library to Qiskit 2.0

.pylintdict

@@ -70,6 +70,8 @@ cerezo
 chernoff
 choi
 chuang
+cinds
+circ
 clbit
 clbits
 clopper
@@ -78,7 +80,9 @@ codebase
 codec
 coeffs
 colin
+coles
 combinatorial
+computable
 concha
 config
 configs
@@ -335,6 +339,7 @@ msg
 multiclass
 multinomial
 multioutput
+multipartite
 mxd
 mypy
 nabla
@@ -357,6 +362,7 @@ nfevs
 nft
 nielsen
 njev
+nlocal
 nlopt
 nn
 noancilla
@@ -450,6 +456,7 @@ qgans
 qgt
 qgt's
 qgts
+qinds
 qiskit
 qiskit's
 qn
@@ -494,6 +501,7 @@ RuntimeError
 rx
 ry
 rz
+qpy
 samplerqnn
 sanjiv
 sashank
@@ -511,7 +519,7 @@ scipy
 sdg
 seealso
 semidefinite
-sep 
+sep
 seperate
 seperable
 serializable

README.md

@@ -42,7 +42,7 @@ The Qiskit Machine Learning framework aims to be:
 ### Kernel-based methods
 
 The [`FidelityQuantumKernel`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.FidelityQuantumKernel) 
-class uses the [`Fidelity`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.state_fidelities.BaseStateFidelity.html)) 
+class uses the [`Fidelity`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.state_fidelities.BaseStateFidelity.html) 
 algorithm. It computes kernel matrices for datasets and can be combined with a Quantum Support Vector Classifier ([`QSVC`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html#qiskit_machine_learning.algorithms.QSVC)) 
 or a Quantum Support Vector Regressor ([`QSVR`](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVR.html#qiskit_machine_learning.algorithms.QSVR)) 
 to solve classification or regression problems respectively. It is also compatible with classical kernel-based machine learning algorithms.

qiskit_machine_learning/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 2025.
 #
 # 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
@@ -41,6 +41,7 @@
    kernels
    neural_networks
    optimizers
+   primitives
    state_fidelities
    utils
 

qiskit_machine_learning/algorithm_job.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # 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
@@ -29,17 +29,7 @@ def submit(self) -> None:
         """
         Submit the job for execution.
 
-        For V1 primitives, Qiskit ``PrimitiveJob`` subclassed JobV1 and defined ``submit()``.
-        ``PrimitiveJob`` was updated for V2 primitives, no longer subclasses ``JobV1``, and
-        now has a private ``_submit()`` method, with ``submit()`` being deprecated as of
-        Qiskit version 0.46. This maintains the ``submit()`` for ``AlgorithmJob`` here as
-        it's called in many places for such a job. An alternative could be to make
-        0.46 the required minimum version and alter all algorithm's call sites to use
-        ``_submit()`` and make this an empty class again as it once was. For now this
-        way maintains compatibility with the current min version of 0.44.
+        Since the library has been migrated to Qiskit v2.1, it is no longer necessary to
+        keep the :meth:``JobV1.submit()`` for the exception handling.
         """
-        # TODO: Considering changing this in the future - see above docstring.
-        try:
-            super()._submit()
-        except AttributeError:
-            super().submit()  # pylint: disable=no-member
+        super()._submit()

qiskit_machine_learning/algorithms/classifiers/__init__.py

@@ -13,8 +13,8 @@
 """Classifiers Package"""
 
 from .neural_network_classifier import NeuralNetworkClassifier
-from .qsvc import QSVC
 from .pegasos_qsvc import PegasosQSVC
+from .qsvc import QSVC
 from .vqc import VQC
 
 __all__ = ["NeuralNetworkClassifier", "QSVC", "PegasosQSVC", "VQC"]

qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py

@@ -15,7 +15,6 @@
 
 import logging
 from datetime import datetime
-from typing import Dict
 import warnings
 
 import numpy as np
@@ -99,7 +98,10 @@ def __init__(
                 raise ValueError("'quantum_kernel' has to be None to use a precomputed kernel")
         else:
             if quantum_kernel is None:
-                msg = "No quantum kernel is provided, SamplerV1 based quantum kernel will be used."
+                msg = (
+                    "No quantum kernel is provided, SamplerV2 based fidelity quantum kernel "
+                    + "will be used."
+                )
                 warnings.warn(msg, QiskitMachineLearningWarning, stacklevel=2)
                 quantum_kernel = FidelityQuantumKernel()
 
@@ -115,11 +117,11 @@ def __init__(
             raise ValueError(f"C has to be a positive number, found {C}.")
 
         # these are the parameters being fit and are needed for prediction
-        self._alphas: Dict[int, int] | None = None
+        self._alphas: dict[int, int] | None = None
         self._x_train: np.ndarray | None = None
         self._n_samples: int | None = None
         self._y_train: np.ndarray | None = None
-        self._label_map: Dict[int, int] | None = None
+        self._label_map: dict[int, int] | None = None
         self._label_pos: int | None = None
         self._label_neg: int | None = None
 

qiskit_machine_learning/algorithms/classifiers/qsvc.py

@@ -13,7 +13,6 @@
 """Quantum Support Vector Classifier"""
 
 import warnings
-from typing import Optional
 
 from sklearn.svm import SVC
 
@@ -54,7 +53,7 @@ class QSVC(SVC, SerializableModelMixin):
 
     """
 
-    def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
+    def __init__(self, *, quantum_kernel: BaseKernel | None = None, **kwargs):
         """
         Args:
             quantum_kernel: A quantum kernel to be used for classification.
@@ -72,7 +71,7 @@ def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
             # if we don't delete, then this value clashes with our quantum kernel
             del kwargs["kernel"]
         if quantum_kernel is None:
-            msg = "No quantum kernel is provided, SamplerV1 based quantum kernel will be used."
+            msg = "No quantum kernel is provided, SamplerV2 based fidelity quantum kernel will be used."
             warnings.warn(msg, QiskitMachineLearningWarning, stacklevel=2)
         self._quantum_kernel = quantum_kernel if quantum_kernel else FidelityQuantumKernel()
 

qiskit_machine_learning/algorithms/classifiers/vqc.py

@@ -12,19 +12,18 @@
 """An implementation of variational quantum classifier."""
 
 from __future__ import annotations
+
 from typing import Callable
 
 import numpy as np
-
 from qiskit import QuantumCircuit
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 from qiskit.transpiler.passmanager import BasePassManager
 
 from ...neural_networks import SamplerQNN
-from ...optimizers import Optimizer, OptimizerResult, Minimizer
+from ...optimizers import Minimizer, Optimizer, OptimizerResult
 from ...utils import derive_num_qubits_feature_map_ansatz
 from ...utils.loss_functions import Loss
-
 from .neural_network_classifier import NeuralNetworkClassifier
 
 
@@ -58,7 +57,7 @@ def __init__(
         initial_point: np.ndarray | None = None,
         callback: Callable[[np.ndarray, float], None] | None = None,
         *,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,  # change: BaseSampler is migrated to BaseSamplerV2
         interpret: Callable[[int], int | tuple[int, ...]] | None = None,
         output_shape: int | None = None,
         pass_manager: BasePassManager | None = None,
@@ -108,7 +107,7 @@ def __init__(
         """
 
         num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-            num_qubits, feature_map, ansatz, use_methods=True
+            num_qubits, feature_map, ansatz
         )
 
         if output_shape is None: