Update black (#173)

Co-authored-by: Elena Peña Tapia <[email protected]>

qiskit_algorithms/amplitude_estimators/ae_utils.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2024.
 #
 # 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
@@ -203,9 +203,11 @@ def pdf_a(x, p, m):
     # 0 and 1, respectively
     pr = np.array(
         [
-            _pdf_a_single_angle(xi, p, m, _alpha) + _pdf_a_single_angle(xi, p, m, _beta)
-            if (xi not in [0, 1])
-            else _pdf_a_single_angle(xi, p, m, _alpha)
+            (
+                _pdf_a_single_angle(xi, p, m, _alpha) + _pdf_a_single_angle(xi, p, m, _beta)
+                if (xi not in [0, 1])
+                else _pdf_a_single_angle(xi, p, m, _alpha)
+            )
             for xi in x
         ]
     ).flatten()

qiskit_algorithms/amplitude_estimators/estimation_problem.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2024.
 #
 # 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
@@ -36,9 +36,9 @@ def __init__(
         state_preparation: QuantumCircuit,
         objective_qubits: int | list[int],
         grover_operator: QuantumCircuit | None = None,
-        post_processing: Callable[[list[float]], list[float]]
-        | Callable[[float], float]
-        | None = None,
+        post_processing: (
+            Callable[[list[float]], list[float]] | Callable[[float], float] | None
+        ) = None,
         is_good_state: Callable[[str], bool] | None = None,
     ) -> None:
         r"""

qiskit_algorithms/gradients/reverse/bind.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2024.
 #
 # 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
@@ -17,6 +17,7 @@
 
 from qiskit.circuit import QuantumCircuit, Parameter
 
+
 # pylint: disable=inconsistent-return-statements
 def bind(
     circuits: QuantumCircuit | Iterable[QuantumCircuit],

qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2024.
 #
 # 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
@@ -266,9 +266,10 @@ def _get_evaluate_energy(
         operator: BaseOperator,
         ansatz: QuantumCircuit,
         return_best_measurement: bool = False,
-    ) -> Callable[[np.ndarray], np.ndarray | float] | tuple[
-        Callable[[np.ndarray], np.ndarray | float], dict[str, Any]
-    ]:
+    ) -> (
+        Callable[[np.ndarray], np.ndarray | float]
+        | tuple[Callable[[np.ndarray], np.ndarray | float], dict[str, Any]]
+    ):
         """Returns a function handle to evaluate the energy at given parameters.
 
         This is the objective function to be passed to the optimizer that is used for evaluation.

qiskit_algorithms/optimizers/aqgd.py

@@ -325,7 +325,7 @@ def minimize(
         logger.info("Initial Params: %s", params)
         epoch = 0
         converged = False
-        for (eta, mom_coeff) in zip(self._eta, self._momenta_coeff):
+        for eta, mom_coeff in zip(self._eta, self._momenta_coeff):
             logger.info("Epoch: %4d | Stepsize: %6.4f | Momentum: %6.4f", epoch, eta, mom_coeff)
 
             sum_max_iters = sum(self._maxiter[0 : epoch + 1])

qiskit_algorithms/optimizers/gradient_descent.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2024.
 #
 # 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
@@ -177,10 +177,9 @@ def grad(x):
     def __init__(
         self,
         maxiter: int = 100,
-        learning_rate: float
-        | list[float]
-        | np.ndarray
-        | Callable[[], Generator[float, None, None]] = 0.01,
+        learning_rate: (
+            float | list[float] | np.ndarray | Callable[[], Generator[float, None, None]]
+        ) = 0.01,
         tol: float = 1e-7,
         callback: CALLBACK | None = None,
         perturbation: float | None = None,

qiskit_algorithms/optimizers/optimizer_utils/learning_rate.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2024.
 #
 # 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
@@ -29,10 +29,9 @@ class LearningRate(Generator):
 
     def __init__(
         self,
-        learning_rate: float
-        | list[float]
-        | np.ndarray
-        | Callable[[], Generator[float, None, None]],
+        learning_rate: (
+            float | list[float] | np.ndarray | Callable[[], Generator[float, None, None]]
+        ),
     ):
         """
         Args:

qiskit_algorithms/state_fidelities/base_state_fidelity.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2024.
 #
 # 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
@@ -171,7 +171,7 @@ def _construct_circuits(
             )
 
         circuits = []
-        for (circuit_1, circuit_2) in zip(circuits_1, circuits_2):
+        for circuit_1, circuit_2 in zip(circuits_1, circuits_2):
 
             # Use the same key for circuits as qiskit.primitives use.
             circuit = self._circuit_cache.get((_circuit_key(circuit_1), _circuit_key(circuit_2)))
@@ -230,7 +230,7 @@ def _construct_value_list(
         elif len(values_1[0]) == 0:
             values = list(values_2)
         else:
-            for (val_1, val_2) in zip(values_1, values_2):
+            for val_1, val_2 in zip(values_1, values_2):
                 # the `+` operation concatenates the lists
                 # and then this new list gets appended to the values list
                 values.append(val_1 + val_2)

requirements-dev.txt

@@ -1,7 +1,7 @@
 coverage>=4.4.0,<7.0
 matplotlib>=3.3
 jupyter
-black[jupyter]~=22.0
+black[jupyter]~=24.1
 pylint>=2.15.0
 stestr>=2.0.0
 pylatexenc>=1.4

test/optimizers/test_optimizer_aqgd.py

@@ -114,6 +114,7 @@ def test_max_grouped_evals_parallelizable(self, max_grouped_evals):
 
     def test_max_grouped_evals_non_parallelizable(self):
         """Tests max_grouped_evals for an objective function that cannot be parallelized"""
+
         # Define the objective function (toy example for functionality)
         def quadratic_objective(x: np.ndarray) -> float:
             # Check if only a single point as parameters is passed