diff --git a/qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py b/qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py
index 795fda8be..d87a50a2e 100644
--- a/qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py
+++ b/qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.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
@@ -193,7 +193,7 @@ def fit(
         # training loop
         for step in range(1, self._num_steps + 1):
             # for every step, a random index (determining a random datum) is fixed
-            i = algorithm_globals.random.integers(0, len(y))
+            i = int(algorithm_globals.random.integers(0, len(y)))
 
             value = self._compute_weighted_kernel_sum(i, X, training=True)
 
diff --git a/qiskit_machine_learning/connectors/torch_connector.py b/qiskit_machine_learning/connectors/torch_connector.py
index 241b51e4f..d4c55a822 100644
--- a/qiskit_machine_learning/connectors/torch_connector.py
+++ b/qiskit_machine_learning/connectors/torch_connector.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 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
@@ -237,7 +237,9 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple:  # type: ignore[override]
                     from sparse import COO
 
                     grad_output = grad_output.detach().cpu()
-                    grad_coo = COO(grad_output.indices(), grad_output.values())
+                    grad_coo = COO(
+                        grad_output.indices(), grad_output.values(), shape=grad_output.shape
+                    )
 
                     # Takes gradients from previous layer in backward pass (i.e. later layer in
                     # forward pass) j for each observation i in the batch. Multiplies this with
@@ -280,7 +282,9 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple:  # type: ignore[override]
                     from sparse import COO
 
                     grad_output = grad_output.detach().cpu()
-                    grad_coo = COO(grad_output.indices(), grad_output.values())
+                    grad_coo = COO(
+                        grad_output.indices(), grad_output.values(), shape=grad_output.shape
+                    )
 
                     # Takes gradients from previous layer in backward pass (i.e. later layer in
                     # forward pass) j for each observation i in the batch. Multiplies this with
diff --git a/qiskit_machine_learning/neural_networks/effective_dimension.py b/qiskit_machine_learning/neural_networks/effective_dimension.py
index 1411b9bf8..4b6869a8c 100644
--- a/qiskit_machine_learning/neural_networks/effective_dimension.py
+++ b/qiskit_machine_learning/neural_networks/effective_dimension.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
@@ -13,7 +13,7 @@
 
 import logging
 import time
-from typing import Union, List, Tuple
+from typing import Any, Union, List, Tuple
 
 import numpy as np
 from scipy.special import logsumexp
@@ -136,14 +136,14 @@ def run_monte_carlo(self) -> Tuple[np.ndarray, np.ndarray]:
              outputs: QNN output vector, result of forward passes, of shape
                 ``(num_input_samples * num_weight_samples, output_size)``.
         """
-        grads = np.zeros(
+        grads: Any = np.zeros(
             (
                 self._num_input_samples * self._num_weight_samples,
                 self._model.output_shape[0],
                 self._model.num_weights,
             )
         )
-        outputs = np.zeros(
+        outputs: Any = np.zeros(
             (self._num_input_samples * self._num_weight_samples, self._model.output_shape[0])
         )
 
diff --git a/qiskit_machine_learning/optimizers/adam_amsgrad.py b/qiskit_machine_learning/optimizers/adam_amsgrad.py
index 1fc6c9823..e28dc26ed 100644
--- a/qiskit_machine_learning/optimizers/adam_amsgrad.py
+++ b/qiskit_machine_learning/optimizers/adam_amsgrad.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
@@ -106,10 +106,10 @@ def __init__(
 
         # runtime variables
         self._t = 0  # time steps
-        self._m = np.zeros(1)
-        self._v = np.zeros(1)
+        self._m: Any = np.zeros(1)
+        self._v: Any = np.zeros(1)
         if self._amsgrad:
-            self._v_eff = np.zeros(1)
+            self._v_eff: Any = np.zeros(1)
 
         if self._snapshot_dir is not None:
             file_path = os.path.join(self._snapshot_dir, "adam_params.csv")
diff --git a/qiskit_machine_learning/optimizers/aqgd.py b/qiskit_machine_learning/optimizers/aqgd.py
index 4de3fdfd7..423e5f680 100644
--- a/qiskit_machine_learning/optimizers/aqgd.py
+++ b/qiskit_machine_learning/optimizers/aqgd.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
@@ -161,7 +161,7 @@ def _compute_objective_fn_and_gradient(
         # Evaluate,
         # reshaping to flatten, as expected by objective function
         if self._max_evals_grouped > 1:
-            batches = [
+            batches: Any = [
                 param_sets_to_eval[i : i + self._max_evals_grouped]
                 for i in range(0, len(param_sets_to_eval), self._max_evals_grouped)
             ]