Make linter happy

qiskit_algorithms/eigensolvers/vqd.py

@@ -283,7 +283,7 @@ def compute_eigenvalues(
                 prev_states.append(self.ansatz.assign_parameters(current_optimal_point["x"]))
 
             self._eval_count = 0
-            current_optimal_point = {"optimal_value": float('inf')}
+            current_optimal_point = {"optimal_value": float("inf")}
             energy_evaluation = self._get_evaluate_energy(
                 step,
                 operator,
@@ -322,7 +322,9 @@ def compute_eigenvalues(
 
             eval_time = time() - start_time
 
-            self._update_vqd_result(result, opt_result, eval_time, self.ansatz.copy(), current_optimal_point)
+            self._update_vqd_result(
+                result, opt_result, eval_time, self.ansatz.copy(), current_optimal_point
+            )
 
             if aux_operators is not None:
                 aux_value = estimate_observables(
@@ -341,7 +343,10 @@ def compute_eigenvalues(
             else:
                 average_fidelity = current_optimal_point["total_fidelity"][0] / (step - 1)
 
-                if self.convergence_threshold is not None and average_fidelity > self.convergence_threshold:
+                if (
+                    self.convergence_threshold is not None
+                    and average_fidelity > self.convergence_threshold
+                ):
                     last_digit = step % 10
 
                     if last_digit == 1:
@@ -355,8 +360,9 @@ def compute_eigenvalues(
 
                     raise AlgorithmError(
                         f"Convergence threshold is set to {self.convergence_threshold} but an "
-                        f"average fidelity {average_fidelity:.5f} with the previous eigenstates have been"
-                        f" observed during the evaluation of the {step}{suffix} lowest eigenvalue."
+                        f"average fidelity {average_fidelity:.5f} with the previous eigenstates"
+                        f"have been observed during the evaluation of the {step}{suffix} lowest"
+                        f"eigenvalue."
                     )
                 logger.info(
                     (
@@ -500,7 +506,9 @@ def _update_vqd_result(
         result.optimal_parameters.append(
             dict(zip(ansatz.parameters, cast(np.ndarray, optimal_point["x"])))
         )
-        result.optimal_values = np.concatenate([result.optimal_values, [optimal_point["optimal_value"]]])
+        result.optimal_values = np.concatenate(
+            [result.optimal_values, [optimal_point["optimal_value"]]]
+        )
         result.cost_function_evals = np.concatenate([result.cost_function_evals, [opt_result.nfev]])
         result.optimizer_times = np.concatenate([result.optimizer_times, [eval_time]])
         result.eigenvalues.append(optimal_point["eigenvalue"] + 0j)  # type: ignore[attr-defined]

test/eigensolvers/test_vqd.py

@@ -105,7 +105,14 @@ def test_basic_operator(self, op):
 
     def test_full_spectrum(self):
         """Test obtaining all eigenvalues."""
-        vqd = VQD(self.estimator, self.fidelity, self.ryrz_wavefunction, optimizer=COBYLA(), k=4, betas=[3, 3, 3])
+        vqd = VQD(
+            self.estimator,
+            self.fidelity,
+            self.ryrz_wavefunction,
+            optimizer=COBYLA(),
+            k=4,
+            betas=[3, 3, 3],
+        )
         result = vqd.compute_eigenvalues(H2_SPARSE_PAULI)
         np.testing.assert_array_almost_equal(
             result.eigenvalues.real, self.h2_energy_excited, decimal=2
@@ -190,7 +197,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             self.assertTrue(all(isinstance(param, float) for param in params))
 
         ref_eval_count = [1, 2, 3, 1, 2, 3]
-        ref_mean = [-1.07, -1.45, -1.36,  1.24,  1.55,  1.07]
+        ref_mean = [-1.07, -1.45, -1.36, 1.24, 1.55, 1.07]
         # new ref_mean since the betas were changed
 
         ref_step = [1, 1, 1, 2, 2, 2]
@@ -440,6 +447,7 @@ def test_aux_operator_std_dev(self, op):
         self.assertIsInstance(result.aux_operators_evaluated[0][3][1], dict)
 
     def test_convergence_threshold(self):
+        """Test the convergence threshold raises an error if and only if too high"""
         vqd = VQD(
             self.estimator,
             self.fidelity,