def main():
# GENERAL SETTINGS
num_system_qubits = 3
num_ancillae = 1
num_qubits = num_system_qubits + num_ancillae
n_attempts = 20
logging.info('We are going for 20 attempts.')
# SET TARGET GATE AND INTERACTIONS
target_gate = qutip.fredkin()
# interactions = 'all'
fredkin_model = make_XX_model(num_qubits)
# HYPERPARAMETERS
training_dataset_size = 200
test_dataset_size = 100
n_epochs = 300
batch_size = 2
sgd_method = 'adadelta'
learning_rate = 1
decay_rate = 0.1
initial_values = 'random'
logging.info('Random initial values')
# TAKE CARE NOT TO OVERWRITE PREVIOUSLY SAVED FILES
prefix = os.path.join(OUTPUT_DIR, OUTPUT_FILES_NAME)
ext = '.pickle'
files = glob.glob(prefix + '*')
pre_idx = 0
if files:
pre_idx = max([int(f[len(prefix):-len(ext)]) for f in files])
# STARTING MESSAGES
for idx in range(n_attempts):
logging.info('Starting training no.{}'.format(str(idx + 1)))
model = qn.model.QubitNetworkGateModel(
sympy_expr=fredkin_model,
initial_values=initial_values,
num_system_qubits=num_system_qubits)
optimizer = qn.Optimizer.Optimizer(
net=model,
learning_rate=learning_rate,
decay_rate=decay_rate,
n_epochs=n_epochs,
batch_size=batch_size,
target_gate=target_gate,
training_dataset_size=training_dataset_size,
test_dataset_size=test_dataset_size,
sgd_method=sgd_method,
headless=True)
optimizer.run()
newpath = os.path.join(
OUTPUT_DIR, OUTPUT_FILES_NAME + str(1 + pre_idx + idx) + '.pickle')
optimizer.save_results(newpath)
logging.info('Fidelity obtained: {}'.format(model.average_fidelity()))
def main():
# GENERAL SETTINGS
num_system_qubits = 3
num_ancillae = 0
num_qubits = num_system_qubits + num_ancillae
# the attempts will be equally (when possible) distributed among these
# initial values. Each initial value will be used the same amount of
# times.
attempted_initvalues = [0, 1, 2, 3, 4]
num_attempts_per_initvalue = 10
num_attempts = num_attempts_per_initvalue * len(attempted_initvalues)
logging.info('We are going for a total of {}'
'attempts.'.format(num_attempts))
logging.info('The following initial values will be used:'
'{}.'.format(attempted_initvalues))
# SET TARGET GATE AND INTERACTIONS
target_gate = qutip.fredkin()
# interactions = 'all'
# fredkin_model = make_reduced_fredkin_model(num_qubits)
# HYPERPARAMETERS
training_dataset_size = 200
test_dataset_size = 100
n_epochs = 100
batch_size = 2
sgd_method = 'momentum'
learning_rate = 1
decay_rate = 0.1
# TAKE CARE NOT TO OVERWRITE PREVIOUSLY SAVED FILES
prefix = os.path.join(OUTPUT_DIR, OUTPUT_FILES_NAME)
ext = '.pickle'
files = glob.glob(prefix + '*')
pre_idx = 0
if files:
pre_idx = max([int(f[len(prefix):-len(ext)]) for f in files])
# STARTING MESSAGES
for idx in range(num_attempts):
initial_values = attempted_initvalues[idx //
num_attempts_per_initvalue]
fredkin_model = make_reduced_fredkin_model(num_qubits)
logging.info('Starting training no.{}'.format(str(idx + 1)))
logging.info('Initial values: {}.'.format(initial_values))
model = qn.model.QubitNetworkGateModel(
sympy_expr=fredkin_model,
initial_values=initial_values,
num_system_qubits=num_system_qubits)
optimizer = qn.Optimizer.Optimizer(
net=model,
learning_rate=learning_rate,
decay_rate=decay_rate,
n_epochs=n_epochs,
batch_size=batch_size,
target_gate=target_gate,
training_dataset_size=training_dataset_size,
test_dataset_size=test_dataset_size,
sgd_method=sgd_method,
headless=True)
optimizer.run()
newpath = os.path.join(
OUTPUT_DIR, OUTPUT_FILES_NAME + str(1 + pre_idx + idx) + '.pickle')
optimizer.save_results(newpath)
logging.info('Fidelity obtained: {}'.format(model.average_fidelity()))