Migrate QGT and QFI from qiskit-algorithms

.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

@@ -46,20 +46,20 @@ def entanglement_concentration_data(
     | tuple[list[Statevector], np.ndarray, list[Statevector], np.ndarray, np.ndarray]
 ):
     r"""
-    Generates a dataset that comprises of Quantum States with two different
-    amounts of Concentration Of Entanglement (CE) and their corresponding class labels.
+    Generates a dataset that comprises Quantum States with two different
+    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
+    on fully separable input states according to the procedure outlined in [1]. Pre-trained
+    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"
+    argument. Next, based on the ``mode`` argument, two pre-trained circuits, "A" and "B"
     are used for generating datapoints.
 
 
     CE can be interpreted as a measure of correlation between the different qubits.
     The ``mode`` argument supports two options. ``"easy"`` gives datapoints with high CE
-    difference hence being easy to seperate. ``"hard"`` mode gives closer CE values.
+    difference, hence being easy to separate. ``"hard"`` mode gives closer CE values.
     The user's classifiers can be benchmarked against these modes for their ability to
     separate the data into two classes based on CE.
 
@@ -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
@@ -71,6 +71,8 @@
 from .base.sampler_gradient_result import SamplerGradientResult
 from .spsa.spsa_estimator_gradient import SPSAEstimatorGradient
 from .spsa.spsa_sampler_gradient import SPSASamplerGradient
+from .lin_comb.lin_comb_qgt import LinCombQGT
+from .qfi import QFI
 
 __all__ = [
     "BaseEstimatorGradient",
@@ -84,4 +86,6 @@
     "SamplerGradientResult",
     "SPSAEstimatorGradient",
     "SPSASamplerGradient",
+    "LinCombQGT",
+    "QFI",
 ]