Restore unchanged unit tests

Restore opflow test case

Restore estimator test

Restore unchanged tests

test/python/algorithms/eigensolvers/test_vqd.py

@@ -10,7 +10,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-""" Test VQD """
+"""Test VQD"""
 
 import unittest
 from test.python.algorithms import QiskitAlgorithmsTestCase
@@ -19,13 +19,9 @@
 from ddt import data, ddt
 
 from qiskit import QuantumCircuit
-from qiskit.algorithms.eigensolvers import VQD
+from qiskit.algorithms.eigensolvers import VQD, VQDResult
 from qiskit.algorithms import AlgorithmError
-from qiskit.algorithms.optimizers import (
- COBYLA,
- L_BFGS_B,
- SLSQP,
-)
+from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, SLSQP, SPSA
 from qiskit.algorithms.state_fidelities import ComputeUncompute
 from qiskit.circuit.library import TwoLocal, RealAmplitudes
 from qiskit.opflow import PauliSumOp
@@ -207,6 +203,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
  @data(H2_PAULI, H2_OP, H2_SPARSE_PAULI)
  def test_vqd_optimizer(self, op):
  """Test running same VQD twice to re-use optimizer, then switch optimizer"""
+
  vqd = VQD(
  estimator=self.estimator,
  fidelity=self.fidelity,
@@ -231,6 +228,17 @@ def run_check():
  vqd.optimizer = L_BFGS_B()
  run_check()
 
+ with self.subTest("Batched optimizer replace"):
+ vqd.optimizer = SLSQP(maxiter=60, max_evals_grouped=10)
+ run_check()
+
+ with self.subTest("SPSA replace"):
+ # SPSA takes too long to converge, so we will
+ # only check that it runs with no errors.
+ vqd.optimizer = SPSA(maxiter=5, learning_rate=0.01, perturbation=0.01)
+ result = vqd.compute_eigenvalues(operator=op)
+ self.assertIsInstance(result, VQDResult)
+
  @data(H2_PAULI, H2_OP, H2_SPARSE_PAULI)
  def test_optimizer_list(self, op):
  """Test sending an optimizer list"""

test/python/algorithms/gradients/test_estimator_gradient.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2023.
+# (C) Copyright IBM 2019, 2021.
 #
 # 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
@@ -14,7 +14,7 @@
 """Test Estimator Gradients"""
 
 import unittest
-from test.python.algorithms.gradients.logging_primitives import LoggingEstimator
+
 import numpy as np
 from ddt import ddt, data, unpack
 
@@ -35,6 +35,8 @@
 from qiskit.quantum_info.random import random_pauli_list
 from qiskit.test import QiskitTestCase
 
+from .logging_primitives import LoggingEstimator
+
 gradient_factories = [
  lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="central"),
  lambda estimator: FiniteDiffEstimatorGradient(estimator, epsilon=1e-6, method="forward"),

test/python/algorithms/gradients/test_qgt.py

@@ -14,7 +14,6 @@
 """Test QGT."""
 
 import unittest
-from test.python.algorithms.gradients.logging_primitives import LoggingEstimator
 from ddt import ddt, data
 
 import numpy as np
@@ -26,6 +25,8 @@
 from qiskit.primitives import Estimator
 from qiskit.test import QiskitTestCase
 
+from .logging_primitives import LoggingEstimator
+
 
 @ddt
 class TestQGT(QiskitTestCase):

test/python/algorithms/gradients/test_sampler_gradient.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2023.
+# (C) Copyright IBM 2019, 2021.
 #
 # 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
@@ -15,9 +15,10 @@
 
 import unittest
 from typing import List
-from test.python.algorithms.gradients.logging_primitives import LoggingSampler
+
 import numpy as np
 from ddt import ddt, data
+
 from qiskit import QuantumCircuit
 from qiskit.algorithms.gradients import (
  FiniteDiffSamplerGradient,
@@ -32,6 +33,8 @@
 from qiskit.result import QuasiDistribution
 from qiskit.test import QiskitTestCase
 
+from .logging_primitives import LoggingSampler
+
 gradient_factories = [
  lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="central"),
  lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="forward"),

test/python/algorithms/minimum_eigensolvers/test_sampling_vqe.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2021.
 #
 # 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
@@ -12,6 +12,7 @@
 
 """Test the Sampler VQE."""
 
+
 import unittest
 from functools import partial
 from test.python.algorithms import QiskitAlgorithmsTestCase

test/python/algorithms/time_evolvers/variational/test_var_qite.py

@@ -99,8 +99,8 @@ def test_run_d_1_with_aux_ops(self):
  var_qite = VarQITE(
  ansatz, init_param_values, var_principle, estimator, num_timesteps=25
  )
-
  evolution_result = var_qite.evolve(evolution_problem)
+
  aux_ops = evolution_result.aux_ops_evaluated
 
  parameter_values = evolution_result.parameter_values[-1]
@@ -128,6 +128,7 @@ def test_run_d_1_with_aux_ops(self):
  ansatz, init_param_values, var_principle, estimator, num_timesteps=25
  )
  evolution_result = var_qite.evolve(evolution_problem)
+
  aux_ops = evolution_result.aux_ops_evaluated
 
  parameter_values = evolution_result.parameter_values[-1]

test/python/algorithms/time_evolvers/variational/test_var_qrte.py

@@ -131,6 +131,7 @@ def test_run_d_1_with_aux_ops(self):
  ansatz, init_param_values, var_principle, estimator, num_timesteps=25
  )
  evolution_result = var_qrte.evolve(evolution_problem)
+
  aux_ops = evolution_result.aux_ops_evaluated
 
  parameter_values = evolution_result.parameter_values[-1]
@@ -158,6 +159,7 @@ def test_run_d_1_with_aux_ops(self):
  ansatz, init_param_values, var_principle, estimator, num_timesteps=25
  )
  evolution_result = var_qrte.evolve(evolution_problem)
+
  aux_ops = evolution_result.aux_ops_evaluated
 
  parameter_values = evolution_result.parameter_values[-1]

test/python/circuit/library/test_qft.py

@@ -13,6 +13,7 @@
 """Test library of QFT circuits."""
 
 import unittest
+import warnings
 import numpy as np
 from ddt import ddt, data, unpack
 
@@ -184,11 +185,18 @@ def __init__(self, *_args, **_kwargs):
  # We don't want to issue a warning on mutation until we know that the values are
  # finalised; this is because a user might want to mutate the number of qubits and the
  # approximation degree. In these cases, wait until we try to build the circuit.
- with self.assertWarns(DeprecationWarning):
+ with warnings.catch_warnings(record=True) as caught_warnings:
+ warnings.filterwarnings(
+ "always",
+ category=RuntimeWarning,
+ module=r"qiskit\..*",
+ message=r".*precision loss in QFT.*",
+ )
  qft = QFT()
  # Even with the approximation this will trigger the warning.
  qft.num_qubits = 1080
  qft.approximation_degree = 20
+ self.assertFalse(caught_warnings)
 
  # Short-circuit the build method so it exits after input validation, but without actually
  # spinning the CPU to build a huge, useless object.

test/python/opflow/opflow_test_case.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2020.
 #
 # 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
@@ -12,7 +12,6 @@
 
 """Opflow Test Case"""
 
-import warnings
 from qiskit.test import QiskitTestCase
 
 

test/python/primitives/test_estimator.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022.
 #
 # 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
@@ -13,6 +13,7 @@
 """Tests for Estimator."""
 
 import unittest
+
 import numpy as np
 from ddt import data, ddt, unpack