def test_vqc_statevector(self, mode):
""" vqc statevector test """
aqua_globals.random_seed = 10598
optimizer = COBYLA()
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer, data_preparation, wavefunction,
self.training_data, self.testing_data)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = self._sorted_wavefunction_params
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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_vqc_minibatching_with_gradient_support(self, mode):
""" 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')
optimizer = L_BFGS_B(maxfun=30)
# set up data encoding circuit
data_preparation = self.data_preparation[mode]
# set up wavefunction
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
wavefunction = RYRZ(2, depth=1)
else:
wavefunction = TwoLocal(2, ['ry', 'rz'],
'cz',
reps=1,
insert_barriers=True)
theta = ParameterVector('theta', wavefunction.num_parameters)
resorted = []
for i in range(4):
layer = wavefunction.ordered_parameters[4 * i:4 * (i + 1)]
resorted += layer[::2]
resorted += layer[1::2]
wavefunction.assign_parameters(dict(zip(resorted, theta)),
inplace=True)
if mode == 'circuit':
wavefunction = QuantumCircuit(2).compose(wavefunction)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
training_input,
test_input,
minibatch_size=2)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = list(theta)
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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_vqc_on_wine(self, mode):
"""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
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
data_preparation = SecondOrderExpansion(feature_dim)
wavefunction = RYRZ(feature_dim, depth=1)
else:
data_preparation = ZZFeatureMap(feature_dim)
x = data_preparation.ordered_parameters
wavefunction = TwoLocal(feature_dim, ['ry', 'rz'],
'cz',
reps=1,
insert_barriers=True)
theta = ParameterVector('theta', wavefunction.num_parameters)
resorted = []
for i in range(2 * feature_dim):
layer = wavefunction.ordered_parameters[2 * feature_dim * i:2 *
feature_dim * (i + 1)]
resorted += layer[::2]
resorted += layer[1::2]
wavefunction.assign_parameters(dict(zip(resorted, theta)),
inplace=True)
if mode == 'circuit':
data_preparation = QuantumCircuit(feature_dim).compose(
data_preparation)
wavefunction = QuantumCircuit(feature_dim).compose(wavefunction)
vqc = VQC(COBYLA(maxiter=100), data_preparation, wavefunction,
training_input, test_input)
# sort parameters for reproducibility
if mode in ['circuit', 'library']:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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_vqc_callback(self, mode):
""" 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')
optimizer = COBYLA(maxiter=3)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
self.training_data,
self.testing_data,
callback=store_intermediate_result)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = self._sorted_wavefunction_params
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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_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_vqc_with_max_evals_grouped(self, mode):
""" 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)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
self.training_data,
self.testing_data,
max_evals_grouped=2)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = self._sorted_wavefunction_params
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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_no_gradient_support(self, mode):
""" vqc minibatching with no 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=6,
test_size=3,
n=n_dim,
gap=0.3,
plot_data=False)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = COBYLA(maxiter=40)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
training_input,
test_input,
minibatch_size=2)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = self._sorted_wavefunction_params
else:
warnings.filterwarnings('always', category=DeprecationWarning)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed,
optimization_level=0)
result = vqc.run(quantum_instance)
self.log.debug(result['testing_accuracy'])
self.assertGreaterEqual(result['testing_accuracy'], 0.5)
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_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_save_and_load_model(self, mode):
""" save and load model test """
aqua_globals.random_seed = self.seed
backend = BasicAer.get_backend('qasm_simulator')
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
skip_calibration=True)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer, data_preparation, wavefunction,
self.training_data, self.testing_data)
if mode in ['circuit', 'library']:
vqc._feature_map_params = self._sorted_data_params
vqc._var_form_params = self._sorted_wavefunction_params
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, data_preparation, wavefunction,
self.training_data, None)
# sort parameters for reproducibility
if mode in ['circuit', 'library']:
loaded_vqc._feature_map_params = self._sorted_data_params
loaded_vqc._var_form_params = self._sorted_wavefunction_params
else:
warnings.filterwarnings('always', category=DeprecationWarning)
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
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
