lint and format

qadence_libs/qinfo_tools/qng.py

@@ -28,11 +28,13 @@ def __init__(
     ):
         """
         Args:
+
             params (tuple | torch.Tensor): Variational parameters to be updated
             circuit (QuantumCircuit): Quantum circuit. Needed to compute the QFI matrix
             lr (float): Learning rate.
             beta (float):
-                Shift applied to the QFI matrix before inversion to ensure numerical stability. Defaults to 10e-3.
+                Shift applied to the QFI matrix before inversion to ensure numerical stability.
+                Defaults to 10e-3.
         """
 
         if not 0.0 <= lr:
@@ -105,13 +107,16 @@ def __init__(
     ):
         """
         Args:
+
             params (tuple | torch.Tensor): Variational parameters to be updated
             circuit (QuantumCircuit): Quantum circuit. Required to compute the QFI matrix.
             lr (float): Learning rate.
             iteration (int): Current iteration. Required to compute the SPSA estimator of the QFI.
             beta (float):
-                Shift applied to the QFI matrix before inversion to ensure numerical stability. Defaults to 10e-3.
-            epsilon (float): Finite shift applied when computing the SPSA derivatives.
+                Shift applied to the QFI matrix before inversion to ensure numerical stability.
+                Defaults to 10e-3.
+            epsilon (float):
+                Finite shift applied when computing the SPSA derivatives. Defaults to 10e-2.
         """
 
         if not 0.0 <= lr:

tests/conftest.py

@@ -1,9 +1,10 @@
 # use this file for configuring test fixtures and
 # functions common to every test
-from pytest import fixture
+from __future__ import annotations
 
-from qadence import BasisSet, QuantumCircuit, QNN
-from qadence.constructors import feature_map, hea, hamiltonian_factory
+from pytest import fixture
+from qadence import QNN, BasisSet, QuantumCircuit
+from qadence.constructors import feature_map, hamiltonian_factory, hea
 from qadence.operations import RX, RY, Z
 
 N_QUBITS_OPTIM = 2

tests/qinfo_tools/test_qfi.py

@@ -1,11 +1,12 @@
 from __future__ import annotations
 
 import random
+
 import numpy as np
 import torch
+from qadence import QuantumCircuit
 from torch import Size, allclose
 
-from qadence import QuantumCircuit
 from qadence_libs.qinfo_tools import get_quantum_fisher, get_quantum_fisher_spsa
 
 SEED = 42

tests/qinfo_tools/test_qng.py

@@ -5,9 +5,7 @@
 import numpy as np
 import pytest
 import torch
-from qadence import QNN, BasisSet, QuantumCircuit, hamiltonian_factory
-from qadence.constructors import feature_map, hea
-from qadence.operations import RX, RY, Z
+from qadence import QuantumCircuit
 from torch import Tensor
 
 from qadence_libs.qinfo_tools import QNG, QNG_SPSA
@@ -39,7 +37,9 @@ def sin_dataset(samples: int) -> tuple[Tensor, Tensor]:
 
 @pytest.mark.parametrize("dataset", DATASETS)
 @pytest.mark.parametrize("optim_config", OPTIMIZERS_CONFIG)
-def test_optims(dataset: tuple[Tensor, Tensor], optim_config: dict, basic_optim_model) -> None:
+def test_optims(
+    dataset: tuple[Tensor, Tensor], optim_config: dict, basic_optim_model: QuantumCircuit
+) -> None:
     circuit, model = basic_optim_model
     model.reset_vparams(torch.rand((len(model.vparams))))
 

tests/qinfo_tools/test_spsa.py

@@ -5,12 +5,9 @@
 import numpy as np
 import pytest
 import torch
-from qadence import BasisSet, Overlap, QuantumCircuit
-from qadence.constructors import feature_map, hea
-from qadence.operations import RX, RY
+from qadence import Overlap, QuantumCircuit
 from torch import Size
 
-from qadence import QuantumCircuit
 from qadence_libs.qinfo_tools.spsa import _shifted_overlap, spsa_2gradient_step
 
 SEED = 42