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SiddharthgolechaSiddharthgolecha
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15 files changed

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‎test/algorithms/classifiers/test_vqc.py‎

Lines changed: 7 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -27,9 +27,8 @@
2727
from sklearn.datasets import make_classification
2828
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
2929

30-
from qiskit.circuit.library import RealAmplitudes, ZFeatureMap
31-
from qiskit.circuit.library import real_amplitudes, zz_feature_map, z_feature_map
32-
from qiskit.providers.fake_provider import GenericBackendV2
30+
from qiskit.circuit.library import real_amplitudes, zz_feature_map, z_feature_map # change: RealAmplitudes is migrated to real_amplitudes
31+
from qiskit_ibm_runtime.fake_provider import GenericBackendV2 # change: GenericBackendV2 is migrated to qiskit_ibm_runtime.fake_provider
3332
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
3433
from qiskit_ibm_runtime import Session, SamplerV2
3534
from qiskit_machine_learning.optimizers import COBYLA
@@ -87,7 +86,7 @@ def setUp(self):
8786
# We want string keys to ensure DDT-generated tests have meaningful names.
8887
self.properties = {
8988
"cobyla": COBYLA(maxiter=25),
90-
"real_amplitudes": real_amplitudes(num_qubits=2, reps=1),
89+
"real_amplitudes": real_amplitudes(num_qubits=2, reps=1), # change: RealAmplitudes is migrated to real_amplitudes
9190
"zz_feature_map": zz_feature_map(2),
9291
"binary": _create_dataset(6, 2),
9392
"multiclass": _create_dataset(10, 3),
@@ -332,8 +331,8 @@ def test_circuit_extensions(self):
332331
num_qubits = 2
333332
classifier = VQC(
334333
num_qubits=num_qubits,
335-
feature_map=ZFeatureMap(1),
336-
ansatz=RealAmplitudes(1),
334+
feature_map=z_feature_map(1),
335+
ansatz=real_amplitudes(1), # change: RealAmplitudes is migrated to real_amplitudes
337336
)
338337
self.assertEqual(classifier.feature_map.num_qubits, num_qubits)
339338
self.assertEqual(classifier.ansatz.num_qubits, num_qubits)
@@ -342,9 +341,9 @@ def test_circuit_extensions(self):
342341
_ = VQC(
343342
num_qubits=num_qubits,
344343
feature_map=z_feature_map(1),
345-
ansatz=real_amplitudes(1),
344+
ansatz=real_amplitudes(1), # change: RealAmplitudes is migrated to real_amplitudes
346345
)
347346

348347

349348
if __name__ == "__main__":
350-
unittest.main()
349+
unittest.main()

‎test/algorithms/inference/test_qbayesian.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -19,8 +19,8 @@
1919

2020
from qiskit import QuantumCircuit
2121
from qiskit.circuit import QuantumRegister
22-
from qiskit.primitives import Sampler
23-
from qiskit.providers.fake_provider import GenericBackendV2
22+
from qiskit.primitives import StatevectorSampler # change: Sampler is migrated to StatevectorSampler
23+
from qiskit_ibm_runtime.fake_provider import GenericBackendV2 # change: GenericBackendV2 is migrated to qiskit_ibm_runtime.fake_provider
2424
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
2525

2626
from qiskit_ibm_runtime import Session, SamplerV2
@@ -174,7 +174,7 @@ def test_parameter(self):
174174
self.assertTrue(self.qbayesian.converged)
175175
self.assertTrue(self.qbayesian.limit == 1)
176176
# Test sampler
177-
sampler = Sampler()
177+
sampler = StatevectorSampler() # change: Sampler is migrated to StatevectorSampler
178178
self.qbayesian.sampler = sampler
179179
self.qbayesian.inference(query={"B": 1}, evidence={"A": 0, "C": 0})
180180
self.assertTrue(self.qbayesian.sampler == sampler)
@@ -259,4 +259,4 @@ def test_trivial_circuit_V2(self):
259259

260260

261261
if __name__ == "__main__":
262-
unittest.main()
262+
unittest.main()

‎test/algorithms/regressors/test_fidelity_quantum_kernel_qsvr.py‎

Lines changed: 154 additions & 156 deletions
Original file line numberDiff line numberDiff line change
@@ -1,156 +1,154 @@
1-
# This code is part of a Qiskit project.
2-
#
3-
# (C) Copyright IBM 2021, 2025.
4-
#
5-
# This code is licensed under the Apache License, Version 2.0. You may
6-
# obtain a copy of this license in the LICENSE.txt file in the root directory
7-
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
8-
#
9-
# Any modifications or derivative works of this code must retain this
10-
# copyright notice, and modified files need to carry a notice indicating
11-
# that they have been altered from the originals.
12-
"""Test QSVR on fidelity quantum kernel."""
13-
14-
import os
15-
import tempfile
16-
import unittest
17-
18-
from test import QiskitMachineLearningTestCase
19-
20-
import numpy as np
21-
from sklearn.metrics import mean_squared_error
22-
23-
from qiskit.primitives import Sampler
24-
from qiskit.circuit.library import zz_feature_map
25-
26-
from qiskit_machine_learning.utils import algorithm_globals
27-
from qiskit_machine_learning.algorithms import QSVR, SerializableModelMixin
28-
from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
29-
from qiskit_machine_learning.kernels import FidelityQuantumKernel
30-
31-
32-
class TestQSVR(QiskitMachineLearningTestCase):
33-
"""Test QSVR Algorithm on fidelity quantum kernel."""
34-
35-
def setUp(self):
36-
super().setUp()
37-
38-
algorithm_globals.random_seed = 10598
39-
40-
self.sampler = Sampler()
41-
self.feature_map = zz_feature_map(feature_dimension=2, reps=2)
42-
43-
self.sample_train = np.asarray(
44-
[
45-
[-0.36572221, 0.90579879],
46-
[-0.41816432, 0.03011426],
47-
[-0.48806982, 0.87208714],
48-
[-0.67078436, -0.91017876],
49-
[-0.12980588, 0.98475113],
50-
[0.78335453, 0.49721604],
51-
[0.78158498, 0.78689328],
52-
[0.03771672, -0.3681419],
53-
[0.54402486, 0.32332253],
54-
[-0.25268454, -0.81106666],
55-
]
56-
)
57-
self.label_train = np.asarray(
58-
[
59-
0.07045477,
60-
0.80047778,
61-
0.04493319,
62-
-0.30427998,
63-
-0.02430856,
64-
0.17224315,
65-
-0.26474769,
66-
0.83097582,
67-
0.60943777,
68-
0.31577759,
69-
]
70-
)
71-
72-
self.sample_test = np.asarray(
73-
[
74-
[-0.60713067, -0.37935265],
75-
[0.55480968, 0.94365285],
76-
[0.00148237, -0.71220499],
77-
[-0.97212742, -0.54068794],
78-
]
79-
)
80-
self.label_test = np.asarray([0.45066614, -0.18052862, 0.4549451, -0.23674218])
81-
82-
def test_qsvr(self):
83-
"""Test QSVR"""
84-
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
85-
qsvr = QSVR(quantum_kernel=qkernel)
86-
qsvr.fit(self.sample_train, self.label_train)
87-
88-
predictions = qsvr.predict(self.sample_test)
89-
mse = mean_squared_error(self.label_test, predictions)
90-
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
91-
92-
def test_change_kernel(self):
93-
"""Test QSVR with QuantumKernel later"""
94-
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
95-
96-
qsvr = QSVR()
97-
qsvr.quantum_kernel = qkernel
98-
qsvr.fit(self.sample_train, self.label_train)
99-
100-
predictions = qsvr.predict(self.sample_test)
101-
mse = mean_squared_error(self.label_test, predictions)
102-
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
103-
104-
def test_qsvr_parameters(self):
105-
"""Test QSVR with extra constructor parameters"""
106-
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
107-
108-
qsvr = QSVR(quantum_kernel=qkernel, tol=1e-3, C=1.0)
109-
qsvr.fit(self.sample_train, self.label_train)
110-
111-
predictions = qsvr.predict(self.sample_test)
112-
mse = mean_squared_error(self.label_test, predictions)
113-
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
114-
115-
def test_qsvc_to_string(self):
116-
"""Test QSVR print works when no *args passed in"""
117-
qsvr = QSVR()
118-
_ = str(qsvr)
119-
120-
def test_with_kernel_parameter(self):
121-
"""Test QSVC with the `kernel` argument."""
122-
with self.assertWarns(QiskitMachineLearningWarning):
123-
QSVR(kernel=1)
124-
125-
def test_save_load(self):
126-
"""Tests save and load models."""
127-
features = np.array([[0, 0], [0.1, 0.1], [0.4, 0.4], [1, 1]])
128-
labels = np.array([0, 0.1, 0.4, 1])
129-
130-
quantum_kernel = FidelityQuantumKernel(feature_map=zz_feature_map(2))
131-
regressor = QSVR(quantum_kernel=quantum_kernel)
132-
regressor.fit(features, labels)
133-
134-
test_features = np.array([[0.5, 0.5]])
135-
original_predicts = regressor.predict(test_features)
136-
137-
with tempfile.TemporaryDirectory() as dir_name:
138-
file_name = os.path.join(dir_name, "qsvr.model")
139-
regressor.to_dill(file_name)
140-
141-
regressor_load = QSVR.from_dill(file_name)
142-
loaded_model_predicts = regressor_load.predict(test_features)
143-
144-
np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
145-
146-
class FakeModel(SerializableModelMixin):
147-
"""Fake model class for test purposes."""
148-
149-
pass
150-
151-
with self.assertRaises(TypeError):
152-
FakeModel.from_dill(file_name)
153-
154-
155-
if __name__ == "__main__":
156-
unittest.main()
1+
# This code is part of a Qiskit project.
2+
#
3+
# (C) Copyright IBM 2021, 2025.
4+
#
5+
# This code is licensed under the Apache License, Version 2.0. You may
6+
# obtain a copy of this license in the LICENSE.txt file in the root directory
7+
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
8+
#
9+
# Any modifications or derivative works of this code must retain this
10+
# copyright notice, and modified files need to carry a notice indicating
11+
# that they have been altered from the originals.
12+
"""Test QSVR on fidelity quantum kernel."""
13+
14+
import os
15+
import tempfile
16+
import unittest
17+
18+
from test import QiskitMachineLearningTestCase
19+
20+
import numpy as np
21+
from sklearn.metrics import mean_squared_error
22+
23+
from qiskit.primitives import BaseSamplerV2 # change: Sampler migrated to BaseSamplerV2
24+
from qiskit.circuit.library import zz_feature_map
25+
26+
from qiskit_machine_learning.utils import algorithm_globals
27+
from qiskit_machine_learning.algorithms import QSVR, SerializableModelMixin
28+
from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
29+
from qiskit_machine_learning.kernels import FidelityQuantumKernel
30+
31+
class TestQSVR(QiskitMachineLearningTestCase):
32+
"""Test QSVR Algorithm on fidelity quantum kernel."""
33+
34+
def setUp(self):
35+
super().setUp()
36+
37+
algorithm_globals.random_seed = 10598
38+
39+
self.sampler = BaseSamplerV2() # change: Sampler migrated to BaseSamplerV2
40+
self.feature_map = zz_feature_map(feature_dimension=2, reps=2)
41+
42+
self.sample_train = np.asarray(
43+
[
44+
[-0.36572221, 0.90579879],
45+
[-0.41816432, 0.03011426],
46+
[-0.48806982, 0.87208714],
47+
[-0.67078436, -0.91017876],
48+
[-0.12980588, 0.98475113],
49+
[0.78335453, 0.49721604],
50+
[0.78158498, 0.78689328],
51+
[0.03771672, -0.3681419],
52+
[0.54402486, 0.32332253],
53+
[-0.25268454, -0.81106666],
54+
]
55+
)
56+
self.label_train = np.asarray(
57+
[
58+
0.07045477,
59+
0.80047778,
60+
0.04493319,
61+
-0.30427998,
62+
-0.02430856,
63+
0.17224315,
64+
-0.26474769,
65+
0.83097582,
66+
0.60943777,
67+
0.31577759,
68+
]
69+
)
70+
71+
self.sample_test = np.asarray(
72+
[
73+
[-0.60713067, -0.37935265],
74+
[0.55480968, 0.94365285],
75+
[0.00148237, -0.71220499],
76+
[-0.97212742, -0.54068794],
77+
]
78+
)
79+
self.label_test = np.asarray([0.45066614, -0.18052862, 0.4549451, -0.23674218])
80+
81+
def test_qsvr(self):
82+
"""Test QSVR"""
83+
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
84+
qsvr = QSVR(quantum_kernel=qkernel)
85+
qsvr.fit(self.sample_train, self.label_train)
86+
87+
predictions = qsvr.predict(self.sample_test)
88+
mse = mean_squared_error(self.label_test, predictions)
89+
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
90+
91+
def test_change_kernel(self):
92+
"""Test QSVR with QuantumKernel later"""
93+
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
94+
95+
qsvr = QSVR()
96+
qsvr.quantum_kernel = qkernel
97+
qsvr.fit(self.sample_train, self.label_train)
98+
99+
predictions = qsvr.predict(self.sample_test)
100+
mse = mean_squared_error(self.label_test, predictions)
101+
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
102+
103+
def test_qsvr_parameters(self):
104+
"""Test QSVR with extra constructor parameters"""
105+
qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
106+
107+
qsvr = QSVR(quantum_kernel=qkernel, tol=1e-3, C=1.0)
108+
qsvr.fit(self.sample_train, self.label_train)
109+
110+
predictions = qsvr.predict(self.sample_test)
111+
mse = mean_squared_error(self.label_test, predictions)
112+
self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
113+
114+
def test_qsvc_to_string(self):
115+
"""Test QSVR print works when no *args passed in"""
116+
qsvr = QSVR()
117+
_ = str(qsvr)
118+
119+
def test_with_kernel_parameter(self):
120+
"""Test QSVC with the `kernel` argument."""
121+
with self.assertWarns(QiskitMachineLearningWarning):
122+
QSVR(kernel=1)
123+
124+
def test_save_load(self):
125+
"""Tests save and load models."""
126+
features = np.array([[0, 0], [0.1, 0.1], [0.4, 0.4], [1, 1]])
127+
labels = np.array([0, 0.1, 0.4, 1])
128+
129+
quantum_kernel = FidelityQuantumKernel(feature_map=zz_feature_map(2))
130+
regressor = QSVR(quantum_kernel=quantum_kernel)
131+
regressor.fit(features, labels)
132+
133+
test_features = np.array([[0.5, 0.5]])
134+
original_predicts = regressor.predict(test_features)
135+
136+
with tempfile.TemporaryDirectory() as dir_name:
137+
file_name = os.path.join(dir_name, "qsvr.model")
138+
regressor.to_dill(file_name)
139+
140+
regressor_load = QSVR.from_dill(file_name)
141+
loaded_model_predicts = regressor_load.predict(test_features)
142+
143+
np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
144+
145+
class FakeModel(SerializableModelMixin):
146+
"""Fake model class for test purposes."""
147+
148+
pass
149+
150+
with self.assertRaises(TypeError):
151+
FakeModel.from_dill(file_name)
152+
153+
if __name__ == "__main__":
154+
unittest.main()

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