def main():
# We add the regularizer function to the model
# The strength of regularizer is regulated by the
# hyperparameter 'regularization_strength'.
# Setting 'plot' to an integer automatically plots some default values
# as well as the monitored circuit parameters. (Requires matplotlib).
hyperparams = {
'circuit': circuit_with_cost_function,
'init_circuit_params': my_init_params,
'task': 'optimization',
'loss': myloss,
'regularizer': myregularizer,
'regularization_strength': 0.5,
'optimizer': 'SGD',
# 'init_learning_rate': 1e-7,
'init_learning_rate': 1e-1,
'log_every': 1,
'plot': True
}
learner = CircuitLearner(hyperparams=hyperparams)
learner.train_circuit(steps=50)
# Print out the final parameters
final_params = learner.get_circuit_parameters()
# final_params is a dictionary
for name, value in final_params.items():
print("Parameter {} has the final value {}.".format(name, value))
n_qmodes = 4
all_results = []
final_params_translated = []
final_params_translated.append(final_params["regularized/squeeze_0"])
final_params_translated.append(final_params["regularized/squeeze_1"])
final_params_translated.append(final_params["regularized/squeeze_2"])
final_params_translated.append(final_params["regularized/squeeze_3"])
final_params_translated.append(final_params["regularized/displacement_0"])
final_params_translated.append(final_params["regularized/displacement_1"])
final_params_translated.append(final_params["regularized/displacement_2"])
final_params_translated.append(final_params["regularized/displacement_3"])
final_params_translated.append(final_params["regularized/bs_00"])
final_params_translated.append(final_params["regularized/bs_01"])
final_params_translated.append(final_params["regularized/bs_10"])
final_params_translated.append(final_params["regularized/bs_11"])
final_params_translated.append(final_params["regularized/bs_20"])
final_params_translated.append(final_params["regularized/bs_21"])
final_params_translated.append(final_params["regularized/bs_30"])
final_params_translated.append(final_params["regularized/bs_31"])
for i in range(1):
bits = get_bits_from_circuit(A, n_qmodes, final_params_translated)
string_bits = [str(bit) for bit in bits]
all_results.append(",".join(string_bits))
print(Counter(all_results))
def get_circuit_params(self, steps):
learner = CircuitLearnerNUM(hyperparams=self.hyperp)
learner.train_circuit(X=self.X, Y=self.Y, steps=steps)
params = learner.get_circuit_parameters()
param_value = params['regularized/dummy']
return param_value
# We add the regularizer function to the model
# The strength of regularizer is regulated by the
# hyperparameter 'regularization_strength'.
# Setting 'plot' to an integer automatically plots some default values
# as well as the monitored circuit parameters. (Requires matplotlib).
hyperparams = {
'circuit': circuit,
'init_circuit_params': my_init_params,
'task': 'optimization',
'loss': myloss,
'regularizer': myregularizer,
'regularization_strength': 0.5,
'optimizer': 'SGD',
'init_learning_rate': 0.1,
'log_every': 1,
'plot': True
}
learner = CircuitLearner(hyperparams=hyperparams)
learner.train_circuit(steps=50)
# Print out the final parameters
final_params = learner.get_circuit_parameters()
# final_params is a dictionary
for name, value in final_params.items():
print("Parameter {} has the final value {}.".format(name, value))
# Look in the 'logsNUM' directory, there should be a file called 'log.csv' that records what happened to alpha
# during training. Play around with the 'regularization_strength' and see how a large strength forces alpha to zero.