[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

doc/source/conf.py

@@ -104,6 +104,7 @@
 #
 autodoc_member_order = "bysource"
 
+
 # Adapted this from
 # https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/docs/conf.py
 # app setup hook

examples/anomaly_detection/test.py

@@ -114,7 +114,6 @@ def compute_loss_test(encoder, vector):
 
     # Make graphs for performance analysis
     if plot:
-
         """Loss distribution graph"""
         plt.hist(loss_a, bins=60, histtype="step", color="red", label="Anomalous data")
         plt.hist(loss_s, bins=60, histtype="step", color="blue", label="Standard data")

examples/qsvd/main.py

@@ -35,7 +35,6 @@
 
 
 def main(nqubits, subsize, nlayers, nshots, RY, method, maxiter):
-
     # We initialize the QSVD
     Qsvd = QSVD(nqubits, subsize, nlayers, RY=RY)
 

examples/qsvd/qsvd.py

@@ -158,7 +158,6 @@ def Schmidt_coeff(self, theta, init_state, nshots=100000):
 
         Schmidt = []
         for i in range(2**small):
-
             bit_string = bin(i)[2:].zfill(small)
             Schmidt.append(result[2 * bit_string])
 

examples/reuploading_classifier/datasets.py

@@ -91,7 +91,7 @@ def fig_template(name):
     elif name == "3_circles":
         centers = np.array([[-1, 1], [1, 0], [-0.5, -0.5]])
         radii = np.array([1, np.sqrt(6 / np.pi - 1), 1 / 2])
-        for (c, r) in zip(centers, radii):
+        for c, r in zip(centers, radii):
             for ax in axs:
                 circle = plt.Circle(c, r, color="black", fill=False, zorder=10)
                 ax.add_artist(circle)
@@ -109,7 +109,7 @@ def fig_template(name):
     elif name == "crown" or name == "tricrown":
         centers = [[0, 0], [0, 0]]
         radii = [np.sqrt(0.8), np.sqrt(0.8 - 2 / np.pi)]
-        for (c, r) in zip(centers, radii):
+        for c, r in zip(centers, radii):
             for ax in axs:
                 circle = plt.Circle(c, r, color="black", fill=False, zorder=10)
                 ax.add_artist(circle)

examples/variational_classifier/main.py

@@ -34,7 +34,6 @@
 
 
 def main(nclasses, nqubits, nlayers, nshots, training, RxRzRx, method):
-
     # We initialize the quantum classifier
     RY = not RxRzRx
     qc = QuantumClassifer(nclasses, nqubits, nlayers, RY=RY)

examples/vqregressor/main.py

@@ -29,7 +29,6 @@
 
 
 def main(layers, learning_rate, epochs, batches, ndata, J_treshold):
-
     # We initialize the quantum regressor
     vqr = VQRegressor(layers=layers, ndata=ndata)
     # and the initial parameters

src/qibo/models/grover.py

@@ -67,7 +67,6 @@ def __init__(
         check_args=(),
         iterative=False,
     ):
-
         self.oracle = oracle
         self.initial_state_circuit = initial_state_circuit
 

src/qibo/models/variational.py

@@ -167,7 +167,6 @@ def __init__(
         bounds_tolerance=1e-7,
         time_tolerance=1e-7,
     ):
-
         if nsteps <= 0:  # pragma: no cover
             raise_error(
                 ValueError,

src/qibo/tests/test_derivative.py

@@ -27,7 +27,6 @@ def circuit(nqubits=1):
     [(1, [8.51104358e-02, 5.20075970e-01, 0]), (0.5, [0.02405061, 0.13560379, 0])],
 )
 def test_derivative(backend, scale_factor, grads):
-
     # initializing the circuit
     c = circuit(nqubits=1)
 

src/qibo/tests/test_noise.py

@@ -331,7 +331,6 @@ def test_kraus_error(backend, density_matrix, nshots):
 @pytest.mark.parametrize("idle_qubits", [True, False])
 def test_noisy_circuit(backend, nshots, idle_qubits):
     if nshots != 20000:
-
         circuit = Circuit(3, density_matrix=True)
         circuit.add(
             [
@@ -420,7 +419,6 @@ def test_noisy_circuit(backend, nshots, idle_qubits):
         backend.assert_allclose(final_samples, target_samples)
 
     else:
-
         circuit = Circuit(3, density_matrix=True)
 
         circuit.add(