def perform_run(n_gates, model_number):
model_name = "random_circuits_" + str(model_number)
start_time = time_module.clock()
test_circuit_generation_function = lambda: generate_completely_random_circuit(
20, n_gates)
environment = IBMQ20Tokyo(test_circuit_generation_function().to_dqn_rep())
agent = DoubleDQNAgent(environment)
agent.load_model(model_name)
average_test_time = 0.0
average_circuit_depth_overhead = 0.0
average_circuit_depth_ratio = 0.0
for e in range(test_episodes):
circuit = test_circuit_generation_function()
original_depth = circuit.depth()
actions, circuit_depth = schedule_swaps(environment,
agent,
circuit=circuit.to_dqn_rep(),
experience_db=None)
average_test_time += (1.0 / test_episodes) * len(actions)
average_circuit_depth_overhead += (1.0 / test_episodes) * (
circuit_depth - original_depth)
average_circuit_depth_ratio += (1.0 / test_episodes) * (
float(circuit_depth) / float(original_depth))
end_time = time_module.clock()
total_time = end_time - start_time
result = (n_gates, average_test_time, average_circuit_depth_overhead,
average_circuit_depth_ratio, total_time)
print('Completed run:', result)
return result
def train_model_on_random_circuits(model_number):
model_name = "random_circuits_" + str(model_number)
training_circuit_generation_function = lambda: generate_completely_random_circuit(
20, 50).to_dqn_rep()
environment = IBMQ20Tokyo(training_circuit_generation_function())
agent = DoubleDQNAgent(environment)
train_model(
environment,
agent,
training_episodes=training_episodes,
circuit_generation_function=training_circuit_generation_function,
should_print=False)
agent.save_model(model_name)
def train_model_on_full_layers(model_number):
model_name = "full_layers_" + str(model_number)
def training_circuit_generation_function():
return generate_multi_layer_circuit(20, 2).to_dqn_rep()
environment = IBMQ20Tokyo(training_circuit_generation_function())
agent = DoubleDQNAgent(environment)
train_model(
environment,
agent,
training_episodes=training_episodes,
circuit_generation_function=training_circuit_generation_function,
should_print=False)
agent.save_model(model_name)