def test_vqc_callback(self, use_circuits):
""" vqc callback test """
history = {
'eval_count': [],
'parameters': [],
'cost': [],
'batch_index': []
}
def store_intermediate_result(eval_count, parameters, cost,
batch_index):
history['eval_count'].append(eval_count)
history['parameters'].append(parameters)
history['cost'].append(cost)
history['batch_index'].append(batch_index)
aqua_globals.random_seed = self.seed
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
optimizer = COBYLA(maxiter=3)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2)
var_form = RY(num_qubits=num_qubits, depth=1)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
vqc = VQC(optimizer,
feature_map,
var_form,
self.training_data,
self.testing_data,
callback=store_intermediate_result)
quantum_instance = QuantumInstance(backend,
shots=1024,
seed_simulator=self.seed,
seed_transpiler=self.seed)
vqc.run(quantum_instance)
self.assertTrue(
all(isinstance(count, int) for count in history['eval_count']))
self.assertTrue(
all(isinstance(cost, float) for cost in history['cost']))
self.assertTrue(
all(isinstance(index, int) for index in history['batch_index']))
for params in history['parameters']:
self.assertTrue(all(isinstance(param, float) for param in params))
def test_qsvm_binary(self, use_circuits):
""" QSVM Binary test """
ref_kernel_training = np.array([[1., 0.85366667, 0.12341667, 0.36408333],
[0.85366667, 1., 0.11141667, 0.45491667],
[0.12341667, 0.11141667, 1., 0.667],
[0.36408333, 0.45491667, 0.667, 1.]])
ref_kernel_testing = np.array([[0.14316667, 0.18208333, 0.4785, 0.14441667],
[0.33608333, 0.3765, 0.02316667, 0.15858333]])
# ref_alpha = np.array([0.36064489, 1.49204209, 0.0264953, 1.82619169])
ref_alpha = np.array([0.34903335, 1.48325498, 0.03074852, 1.80153981])
# ref_bias = np.array([-0.03380763])
ref_bias = np.array([-0.03059226])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
svm = QSVM(feature_map, self.training_data, self.testing_data, None)
quantum_instance = QuantumInstance(backend,
shots=self.shots,
seed_simulator=self.random_seed,
seed_transpiler=self.random_seed)
try:
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_training'], ref_kernel_training, decimal=1)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=1)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
np.testing.assert_array_almost_equal(result['svm']['alphas'], ref_alpha, decimal=8)
np.testing.assert_array_almost_equal(result['svm']['bias'], ref_bias, decimal=8)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_vqc_with_max_evals_grouped(self, use_circuits):
""" vqc with max evals grouped test """
aqua_globals.random_seed = self.seed
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
c1=0.1,
c2=0.602,
c3=0.101,
c4=0.0,
skip_calibration=True)
feature_map = SecondOrderExpansion(
feature_dimension=get_feature_dimension(self.training_data),
depth=2)
var_form = RYRZ(num_qubits=feature_map.num_qubits, depth=3)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
# set up algorithm
vqc = VQC(optimizer,
feature_map,
var_form,
self.training_data,
self.testing_data,
max_evals_grouped=2)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
quantum_instance = QuantumInstance(
BasicAer.get_backend('qasm_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqc.run(quantum_instance)
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params,
decimal=8)
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss,
decimal=8)
self.assertEqual(1.0, result['testing_accuracy'])
def test_vqc_minibatching_with_gradient_support(self, use_circuits):
""" vqc minibatching with gradient support test """
n_dim = 2 # dimension of each data point
seed = 1024
aqua_globals.random_seed = seed
_, training_input, test_input, _ = ad_hoc_data(training_size=4,
test_size=2,
n=n_dim,
gap=0.3,
plot_data=False)
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = n_dim
optimizer = L_BFGS_B(maxfun=30)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=1)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
# set up algorithm
vqc = VQC(optimizer,
feature_map,
var_form,
training_input,
test_input,
minibatch_size=2)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed)
result = vqc.run(quantum_instance)
vqc_accuracy = 0.5
self.log.debug(result['testing_accuracy'])
self.assertAlmostEqual(result['testing_accuracy'],
vqc_accuracy,
places=3)
def test_qsvm_multiclass_error_correcting_code(self, use_circuits):
""" QSVM Multiclass error Correcting Code test """
training_input = {'A': np.asarray([[0.6560706, 0.17605998], [0.25776033, 0.47628296],
[0.8690704, 0.70847635]]),
'B': np.asarray([[0.38857596, -0.33775802], [0.49946978, -0.48727951],
[0.49156185, -0.3660534]]),
'C': np.asarray([[-0.68088231, 0.46824423], [-0.56167659, 0.65270294],
[-0.82139073, 0.29941512]])}
test_input = {'A': np.asarray([[0.57483139, 0.47120732], [0.48372348, 0.25438544],
[0.48142649, 0.15931707]]),
'B': np.asarray([[-0.06048935, -0.48345293], [-0.01065613, -0.33910828],
[0.06183066, -0.53376975]]),
'C': np.asarray([[-0.74561108, 0.27047295], [-0.69942965, 0.11885162],
[-0.66489165, 0.1181712]])}
total_array = np.concatenate((test_input['A'], test_input['B'], test_input['C']))
aqua_globals.random_seed = self.random_seed
feature_map = SecondOrderExpansion(feature_dimension=get_feature_dimension(training_input),
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
try:
svm = QSVM(feature_map, training_input, test_input, total_array,
multiclass_extension=ErrorCorrectingCode(code_size=5))
quantum_instance = QuantumInstance(BasicAer.get_backend('qasm_simulator'),
shots=self.shots,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = svm.run(quantum_instance)
self.assertAlmostEqual(result['testing_accuracy'], 0.444444444, places=4)
self.assertEqual(result['predicted_classes'], ['A', 'A', 'C', 'A',
'A', 'A', 'A', 'C', 'C'])
except NameError as ex:
self.skipTest(str(ex))
def test_vqc_on_wine(self, use_circuits):
"""Test VQE on the wine test using circuits as feature map and variational form."""
feature_dim = 4 # dimension of each data point
training_dataset_size = 6
testing_dataset_size = 3
_, training_input, test_input, _ = wine(
training_size=training_dataset_size,
test_size=testing_dataset_size,
n=feature_dim,
plot_data=False)
aqua_globals.random_seed = self.seed
feature_map = SecondOrderExpansion(feature_dimension=feature_dim)
var_form = RYRZ(feature_map.num_qubits, depth=1)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
vqc = VQC(COBYLA(maxiter=100), feature_map, var_form, training_input,
test_input)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
result = vqc.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.log.debug(result['testing_accuracy'])
self.assertLess(result['testing_accuracy'], 0.6)
def test_qsvm_setup_data(self, use_circuits):
""" QSVM Setup Data test """
ref_kernel_testing = np. array([[0.1443953, 0.18170069, 0.47479649, 0.14691763],
[0.33041779, 0.37663733, 0.02115561, 0.16106199]])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
try:
svm = QSVM(feature_map)
svm.setup_training_data(self.training_data)
svm.setup_test_data(self.testing_data)
quantum_instance = QuantumInstance(backend, seed_transpiler=self.random_seed,
seed_simulator=self.random_seed)
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_vqc_statevector(self, use_circuits):
""" vqc statevector test """
aqua_globals.random_seed = 10598
optimizer = COBYLA()
feature_map = SecondOrderExpansion(
feature_dimension=get_feature_dimension(self.training_data),
depth=2)
var_form = RYRZ(num_qubits=feature_map.num_qubits, depth=3)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
# set up algorithm
vqc = VQC(optimizer, feature_map, var_form, self.training_data,
self.testing_data)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
quantum_instance = QuantumInstance(
BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqc.run(quantum_instance)
ref_train_loss = 0.1059404
np.testing.assert_array_almost_equal(result['training_loss'],
ref_train_loss,
decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
def test_qsvm_binary_directly_statevector(self, use_circuits):
""" QSVM Binary Directly Statevector test """
ref_kernel_testing = np. array([[0.1443953, 0.18170069, 0.47479649, 0.14691763],
[0.33041779, 0.37663733, 0.02115561, 0.16106199]])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
if use_circuits:
x = ParameterVector('x', num_qubits)
feature_map = feature_map.construct_circuit(x)
feature_map.ordered_parameters = list(x)
svm = QSVM(feature_map, self.training_data, self.testing_data, None)
quantum_instance = QuantumInstance(backend, seed_transpiler=self.random_seed,
seed_simulator=self.random_seed)
file_path = self.get_resource_path('qsvm_test.npz')
try:
result = svm.run(quantum_instance)
ori_alphas = result['svm']['alphas']
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
self.assertEqual(result['testing_accuracy'], 0.5)
svm.save_model(file_path)
self.assertTrue(os.path.exists(file_path))
loaded_svm = QSVM(feature_map)
loaded_svm.load_model(file_path)
np.testing.assert_array_almost_equal(
loaded_svm.ret['svm']['support_vectors'], ref_support_vectors, decimal=4)
np.testing.assert_array_almost_equal(
loaded_svm.ret['svm']['alphas'], ori_alphas, decimal=4)
loaded_test_acc = loaded_svm.test(svm.test_dataset[0],
svm.test_dataset[1],
quantum_instance)
self.assertEqual(result['testing_accuracy'], loaded_test_acc)
np.testing.assert_array_almost_equal(
loaded_svm.ret['kernel_matrix_testing'], ref_kernel_testing, decimal=4)
except NameError as ex:
self.skipTest(str(ex))
finally:
if os.path.exists(file_path):
try:
os.remove(file_path)
except Exception: # pylint: disable=broad-except
pass
def setUp(self):
super().setUp()
self.seed = 1376
aqua_globals.random_seed = self.seed
self.training_data = {
'A': np.asarray([[2.95309709, 2.51327412],
[3.14159265, 4.08407045]]),
'B': np.asarray([[4.08407045, 2.26194671],
[4.46106157, 2.38761042]])
}
self.testing_data = {
'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])
}
self.ref_opt_params = np.array([
10.03814083, -12.22048954, -7.58026833, -2.42392954, 12.91555293,
13.44064652, -2.89951454, -10.20639406, 0.81414546, -1.00551752,
-4.7988307, 14.00831419, 8.26008064, -7.07543736, 11.43368677,
-5.74857438
])
self.ref_train_loss = 0.69366523
self.ref_prediction_a_probs = [[0.79882812, 0.20117188]]
self.ref_prediction_a_label = [0]
# ignore warnings from creating VariationalForm and FeatureMap objects
warnings.filterwarnings('ignore', category=DeprecationWarning)
var_form_ryrz = RYRZ(2, depth=3)
feature_map = SecondOrderExpansion(2, depth=2)
warnings.filterwarnings('always', category=DeprecationWarning)
library_ryrz = TwoLocal(2, ['ry', 'rz'],
'cz',
reps=3,
insert_barriers=True)
theta = ParameterVector('theta', var_form_ryrz.num_parameters)
circuit_ryrz = var_form_ryrz.construct_circuit(theta)
resorted = []
for i in range(4):
layer = library_ryrz.ordered_parameters[4 * i:4 * (i + 1)]
resorted += layer[::2]
resorted += layer[1::2]
library_ryrz.assign_parameters(dict(zip(resorted, theta)),
inplace=True)
self._sorted_wavefunction_params = list(theta)
self.ryrz_wavefunction = {
'wrapped': var_form_ryrz,
'circuit': circuit_ryrz,
'library': library_ryrz
}
library_circuit = ZZFeatureMap(2, reps=2)
x = ParameterVector('x', 2)
circuit = feature_map.construct_circuit(x)
self._sorted_data_params = list(x)
library_circuit.assign_parameters(x, inplace=True)
self.data_preparation = {
'wrapped': feature_map,
'circuit': circuit,
'library': library_circuit
}
def test_save_and_load_model(self, use_circuits):
""" save and load model test """
aqua_globals.random_seed = self.seed
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
skip_calibration=True)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=3)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
# set up algorithm
vqc = VQC(optimizer, feature_map, var_form, self.training_data,
self.testing_data)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
quantum_instance = QuantumInstance(backend,
shots=1024,
seed_simulator=self.seed,
seed_transpiler=self.seed)
result = vqc.run(quantum_instance)
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params,
decimal=4)
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss,
decimal=8)
self.assertEqual(1.0, result['testing_accuracy'])
file_path = self.get_resource_path('vqc_test.npz')
vqc.save_model(file_path)
self.assertTrue(os.path.exists(file_path))
loaded_vqc = VQC(optimizer, feature_map, var_form, self.training_data,
None)
# sort parameters for reproducibility
if use_circuits:
loaded_vqc._feature_map_params = list(x)
loaded_vqc._var_form_params = list(theta)
loaded_vqc.load_model(file_path)
np.testing.assert_array_almost_equal(loaded_vqc.ret['opt_params'],
self.ref_opt_params,
decimal=4)
loaded_test_acc = loaded_vqc.test(vqc.test_dataset[0],
vqc.test_dataset[1],
quantum_instance)
self.assertEqual(result['testing_accuracy'], loaded_test_acc)
predicted_probs, predicted_labels = loaded_vqc.predict(
self.testing_data['A'], quantum_instance)
np.testing.assert_array_almost_equal(predicted_probs,
self.ref_prediction_a_probs,
decimal=8)
np.testing.assert_array_equal(predicted_labels,
self.ref_prediction_a_label)
if os.path.exists(file_path):
try:
os.remove(file_path)
except Exception: # pylint: disable=broad-except
pass