Fix FakeToronto import and Fix Aer Deprecate Messages (backport #424) (…

…#451) * Fix FakeToronto import (#424) (cherry picked from commit 505495f) # Conflicts: # test/algorithms/classifiers/test_neural_network_classifier.py # test/algorithms/classifiers/test_vqc.py # test/algorithms/regressors/test_neural_network_regressor.py # test/neural_networks/test_two_layer_qnn.py * fix conflicts Co-authored-by: Manoel Marques <[email protected]>
qiskit-community · Aug 10, 2022 · 49fd69d · 49fd69d
1 parent 94b9db5
commit 49fd69d
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Showing 10 changed files with 48 additions and 36 deletions.
diff --git a/test/algorithms/classifiers/test_neural_network_classifier.py b/test/algorithms/classifiers/test_neural_network_classifier.py
@@ -23,10 +23,11 @@
 import scipy
 
 from ddt import ddt, data, idata, unpack
-from qiskit import Aer, QuantumCircuit
+import qiskit
 from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B, Optimizer
+from qiskit.circuit import QuantumCircuit
 from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit_machine_learning.algorithms import SerializableModelMixin
 from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier
@@ -43,18 +44,19 @@
 class TestNeuralNetworkClassifier(QiskitMachineLearningTestCase):
     """Neural Network Classifier Tests."""
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
 
         # specify quantum instances
         algorithm_globals.random_seed = 12345
         self.sv_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )
         self.qasm_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator"),
             shots=100,
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,

diff --git a/test/algorithms/classifiers/test_vqc.py b/test/algorithms/classifiers/test_vqc.py
@@ -21,10 +21,10 @@
 import numpy as np
 import scipy
 from ddt import ddt, data
-from qiskit import Aer
+import qiskit
 from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
 from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit_machine_learning.algorithms import VQC
 
@@ -33,6 +33,7 @@
 class TestVQC(QiskitMachineLearningTestCase):
     """VQC Tests."""
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
 
@@ -41,12 +42,12 @@ def setUp(self):
         # specify quantum instances
         algorithm_globals.random_seed = 12345
         self.sv_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )
         self.qasm_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator"),
             shots=100,
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,

diff --git a/test/algorithms/regressors/test_neural_network_regressor.py b/test/algorithms/regressors/test_neural_network_regressor.py
@@ -11,19 +11,20 @@
 # that they have been altered from the originals.
 """ Test Neural Network Regressor """
 
+import unittest
 import os
 import tempfile
 
 from test import QiskitMachineLearningTestCase
 
 import numpy as np
 from ddt import ddt, data
-from qiskit import Aer, QuantumCircuit
+import qiskit
 from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
-from qiskit.circuit import Parameter
+from qiskit.circuit import Parameter, QuantumCircuit
 from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes
 from qiskit.opflow import PauliSumOp
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit_machine_learning.algorithms import SerializableModelMixin
 from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor
@@ -34,18 +35,19 @@
 class TestNeuralNetworkRegressor(QiskitMachineLearningTestCase):
     """Test Neural Network Regressor."""
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
 
         # specify quantum instances
         algorithm_globals.random_seed = 12345
         self.sv_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )
         self.qasm_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator"),
             shots=100,
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
@@ -174,3 +176,7 @@ class FakeModel(SerializableModelMixin):
 
         finally:
             os.remove(file_name)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/algorithms/regressors/test_vqr.py b/test/algorithms/regressors/test_vqr.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2021.
+# (C) Copyright IBM 2018, 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
@@ -18,29 +18,30 @@
 import numpy as np
 from ddt import data, ddt
 
-from qiskit import Aer, QuantumCircuit
+import qiskit
 from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
-from qiskit.circuit import Parameter
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.circuit import Parameter, QuantumCircuit
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 from qiskit_machine_learning.algorithms import VQR
 
 
 @ddt
 class TestVQR(QiskitMachineLearningTestCase):
     """VQR Tests."""
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
 
         # specify quantum instances
         algorithm_globals.random_seed = 12345
         self.sv_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )
         self.qasm_quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator"),
             shots=100,
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,

diff --git a/test/circuit/library/test_raw_feature_vector.py b/test/circuit/library/test_raw_feature_vector.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2020, 2021.
+# (C) Copyright IBM 2020, 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
@@ -17,13 +17,13 @@
 from test import QiskitMachineLearningTestCase
 
 import numpy as np
-from qiskit import transpile, Aer
+import qiskit
 from qiskit.algorithms.optimizers import COBYLA
 from qiskit.circuit import QuantumCircuit
 from qiskit.circuit.library import RealAmplitudes
 from qiskit.exceptions import QiskitError
 from qiskit.quantum_info import Statevector
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit_machine_learning.algorithms import VQC
 from qiskit_machine_learning.circuit.library import RawFeatureVector
@@ -45,7 +45,7 @@ def test_construction(self):
 
         with self.subTest("check unrolling fails"):
             with self.assertRaises(QiskitError):
-                _ = transpile(circuit, basis_gates=["u", "cx"], optimization_level=0)
+                _ = qiskit.transpile(circuit, basis_gates=["u", "cx"], optimization_level=0)
 
     def test_fully_bound(self):
         """Test fully binding the circuit works."""
@@ -90,13 +90,14 @@ def test_partially_bound(self):
             self.assertEqual(circuit.num_qubits, bound.num_qubits)
             self.assertEqual(circuit.feature_dimension, bound.feature_dimension)
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def test_usage_in_vqc(self):
         """Test using the circuit the a single VQC iteration works."""
 
         # specify quantum instance and random seed
         algorithm_globals.random_seed = 12345
         quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )

diff --git a/test/connectors/test_torch.py b/test/connectors/test_torch.py
@@ -34,7 +34,7 @@ def setup_test(self):
         algorithm_globals.random_seed = 12345
         # specify quantum instances
         self._sv_quantum_instance = QuantumInstance(
-            qiskit.Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )

diff --git a/test/neural_networks/test_circuit_qnn.py b/test/neural_networks/test_circuit_qnn.py
@@ -25,7 +25,7 @@
 from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
 from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 from qiskit.compiler.transpiler import PassManagerConfig, level_1_pass_manager, level_2_pass_manager
-from qiskit.test.mock import FakeToronto
+from qiskit.providers.fake_provider import FakeToronto
 
 from qiskit_machine_learning import QiskitMachineLearningError
 from qiskit_machine_learning.neural_networks import CircuitQNN
@@ -52,7 +52,7 @@ def setUp(self):
         algorithm_globals.random_seed = 12345
         # specify "run configuration"
         self.quantum_instance_sv = QuantumInstance(
-            qiskit.Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )

diff --git a/test/neural_networks/test_effective_dimension.py b/test/neural_networks/test_effective_dimension.py
@@ -18,10 +18,10 @@
 import numpy as np
 from ddt import ddt, data, unpack
 
-from qiskit import Aer, QuantumCircuit
+import qiskit
+from qiskit.circuit import QuantumCircuit
 from qiskit.circuit.library import ZFeatureMap, RealAmplitudes
-from qiskit.utils import QuantumInstance, algorithm_globals
-from qiskit.utils import optionals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit.opflow import PauliSumOp
 from qiskit_machine_learning.neural_networks import TwoLayerQNN, CircuitQNN
@@ -42,7 +42,7 @@ def setUp(self):
 
         algorithm_globals.random_seed = 1234
         qi_sv = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )

diff --git a/test/neural_networks/test_opflow_qnn.py b/test/neural_networks/test_opflow_qnn.py
@@ -43,7 +43,7 @@ def setUp(self):
         algorithm_globals.random_seed = 12345
         # specify quantum instances
         self.sv_quantum_instance = QuantumInstance(
-            qiskit.Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )

diff --git a/test/neural_networks/test_two_layer_qnn.py b/test/neural_networks/test_two_layer_qnn.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2021.
+# (C) Copyright IBM 2018, 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
@@ -18,9 +18,9 @@
 
 import numpy as np
 from ddt import ddt, data
-from qiskit import Aer
+import qiskit
 from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
-from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils import QuantumInstance, algorithm_globals, optionals
 
 from qiskit_machine_learning.neural_networks import TwoLayerQNN
 
@@ -29,12 +29,13 @@
 class TestTwoLayerQNN(QiskitMachineLearningTestCase):
     """Two Layer QNN Tests."""
 
+    @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
         algorithm_globals.random_seed = 12345
         # specify "run configuration"
         quantum_instance = QuantumInstance(
-            Aer.get_backend("aer_simulator_statevector"),
+            qiskit.providers.aer.Aer.get_backend("aer_simulator_statevector"),
             seed_simulator=algorithm_globals.random_seed,
             seed_transpiler=algorithm_globals.random_seed,
         )