Fix spelling and CI checks

Author: edoaltamura

.pylintdict

@@ -77,8 +77,10 @@ cobyla
 codebase
 codec
 coeffs
+coles
 colin
 combinatorial
+computable
 concha
 config
 configs
@@ -335,6 +337,7 @@ msg
 multiclass
 multinomial
 multioutput
+multipartite
 mxd
 mypy
 nabla
@@ -511,7 +514,7 @@ scipy
 sdg
 seealso
 semidefinite
-sep 
+sep
 seperate
 seperable
 serializable

qiskit_machine_learning/datasets/entanglement_concentration.py

@@ -50,7 +50,7 @@ def entanglement_concentration_data(
     amounts of Concentration Of Entanglement (CE) and their corresponding class labels.
     These states are generated by the effect of two different pre-trained ansatz
     on fully seperable input states according to the procedure outlined in [1]. Pre-trained
-    data in courtesy of L Schatzki et el [3]. The datapoints can be fully separated using
+    data in courtesy of L Schatzki et al [3]. The datapoints can be fully separated using
     the SWAP test outlined in [2]. First, input states are randomly generated from a
     uniform distribution, using a sampling method determined by the ``sampling_method``
     argument. Next, based on the ``mode`` argument, two pre-trained circuits "A" and "B"
@@ -152,21 +152,21 @@ def entanglement_concentration_data(
         raise ValueError("Invalid sampling method. Must be 'isotropic' or 'cardinal'")
     if sampling_method == "cardinal" and n_points >= (6**n):
         raise ValueError(
-            """Cardinal Sampling cannot generate a large number of unique 
-            datapoints due to the limited number of combinations possible. 
+            """Cardinal Sampling cannot generate a large number of unique
+            datapoints due to the limited number of combinations possible.
             Try "isotropic" sampling method"""
         )
     if formatting not in {"statevector", "ndarray"}:
         raise ValueError(
-            """Formatting must be "statevector" or "ndarray". Please check for 
+            """Formatting must be "statevector" or "ndarray". Please check for
             case sensitivity."""
         )
 
     # Warnings
     if sampling_method == "cardinal" and n_points > (3**n):
         warnings.warn(
-            """Cardinal Sampling for large number of samples is not recommended 
-            and can lead to an arbitrarily large generation time due to 
+            """Cardinal Sampling for large number of samples is not recommended
+            and can lead to an arbitrarily large generation time due to
             repeating datapoints. Try "isotropic" sampling method""",
             UserWarning,
         )
@@ -244,7 +244,7 @@ def _assign_parameters(
     expected = 3 * depth * n_qubits
     if len(weights) != expected:
         raise ValueError(
-            """Parameter mismatch – please reinstall the latest 'qiskit-machine-learning' 
+            """Parameter mismatch – please reinstall the latest 'qiskit-machine-learning'
             package (or update the model files).""",
         )
 

qiskit_machine_learning/gradients/__init__.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

qiskit_machine_learning/gradients/base/base_qgt.py

@@ -93,11 +93,9 @@ def __init__(
             precision: Precision to be used by the underlying Estimator. If provided, this number
                 takes precedence over the default precision of the primitive. If None, the default
                 precision of the primitive is used.
-            transpiler: An optional object with a `run` method allowing to transpile the circuits
+            pass_manager: An optional object with a `run` method allowing to transpile the circuits
                 that are run when using this algorithm. If set to `None`, these won't be
                 transpiled.
-            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
-                method as keyword arguments.
         """
         self._estimator: BaseEstimatorV2 = estimator
         self._precision = precision
@@ -107,7 +105,9 @@ def __init__(
         self._gradient_circuit_cache: dict[tuple, GradientCircuit] = {}
 
         self._pass_manager = pass_manager
-        self._pass_manager_options = pass_manager_options if pass_manager_options is not None else {}
+        self._pass_manager_options = (
+            pass_manager_options if pass_manager_options is not None else {}
+        )
 
     @property
     def derivative_type(self) -> DerivativeType:

qiskit_machine_learning/gradients/lin_comb/lin_comb_qgt.py

@@ -16,7 +16,6 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from typing import Any
 
 import numpy as np
 
@@ -269,7 +268,7 @@ def _run_unique(
         # Compute the QGT
         qgts = []
         partial_sum_n = 0
-        for phase_fix, n, m in zip(phase_fixes, all_n, all_m):
+        for phase_fix, n, m in zip(phase_fixes, all_n, all_m):  # type: ignore
             qgt = np.zeros((m, m), dtype="complex")
             # Compute the first term in the QGT
             if self.derivative_type == DerivativeType.COMPLEX:
@@ -311,7 +310,7 @@ def _run_unique(
         else:
             for i, (precision_, result) in enumerate(zip(precision, results)):
                 if precision_ is None:
-                    precision[i] = results[i].metadata["target_precision"]
+                    precision[i] = results[i].metadata["target_precision"]  # type: ignore
 
         return QGTResult(
             qgts=qgts, derivative_type=self.derivative_type, metadata=metadata, precision=precision

qiskit_machine_learning/gradients/utils.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

test/gradients/test_qgt.py

@@ -24,7 +24,7 @@
 from qiskit.circuit.library import real_amplitudes
 from qiskit.primitives import StatevectorEstimator
 
-from qiskit_algorithms.gradients import DerivativeType, LinCombQGT, ReverseQGT
+from qiskit_machine_learning.gradients import DerivativeType, LinCombQGT
 
 from .logging_primitives import LoggingEstimator
 
@@ -37,10 +37,10 @@ def setUp(self):
         super().setUp()
         self.estimator = StatevectorEstimator()
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_derivative_type(self, qgt_type):
         """Test QGT derivative_type"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args, derivative_type=DerivativeType.REAL)
 
         a, b = Parameter("a"), Parameter("b")
@@ -76,10 +76,10 @@ def test_qgt_derivative_type(self, qgt_type):
                 qgt_result = qgt.run([qc], [param]).result().qgts
                 np.testing.assert_allclose(qgt_result[0], correct_values[i], atol=1e-3)
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_phase_fix(self, qgt_type):
         """Test the phase-fix argument in a QGT calculation"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args, phase_fix=False)
 
         # create the circuit
@@ -116,10 +116,10 @@ def test_qgt_phase_fix(self, qgt_type):
                 qgt_result = qgt.run([qc], [param]).result().qgts
                 np.testing.assert_allclose(qgt_result[0], correct_values[i], atol=1e-3)
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_coefficients(self, qgt_type):
         """Test the derivative option of QGT"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args, derivative_type=DerivativeType.REAL)
 
         qc = real_amplitudes(num_qubits=2, reps=1)
@@ -154,10 +154,10 @@ def test_qgt_coefficients(self, qgt_type):
             qgt_result = qgt.run([qc], [param]).result().qgts
             np.testing.assert_allclose(qgt_result[0], correct_values[i], atol=1e-3)
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_parameters(self, qgt_type):
         """Test the QGT with specified parameters"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args, derivative_type=DerivativeType.REAL)
 
         a = Parameter("a")
@@ -199,10 +199,10 @@ def test_qgt_parameters(self, qgt_type):
                 qgt_result = qgt.run([qc2], [param_values], [param]).result().qgts
                 np.testing.assert_allclose(qgt_result[0], expected[i], atol=1e-3)
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_multi_arguments(self, qgt_type):
         """Test the QGT for multiple arguments"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args, derivative_type=DerivativeType.REAL)
 
         a = Parameter("a")
@@ -221,10 +221,10 @@ def test_qgt_multi_arguments(self, qgt_type):
         for i, _ in enumerate(param_list):
             np.testing.assert_allclose(qgt_results[i], correct_values[i], atol=1e-3)
 
-    @data(LinCombQGT, ReverseQGT)
+    @data(LinCombQGT)
     def test_qgt_validation(self, qgt_type):
         """Test estimator QGT's validation"""
-        args = () if qgt_type == ReverseQGT else (self.estimator,)
+        args = ()
         qgt = qgt_type(*args)
 
         a = Parameter("a")