def _train(self):
# Get the result of optimization.
result = grape_schroedinger_discrete(**self.qoc_config)
error = result.best_error
# Log the result.
self.log_config.update({
"error": error,
})
log_str = "{}\n".format(
json.dumps(self.log_config, cls=CustomJSONEncoder))
try:
with FileLock(LOG_FILE_LOCK_PATH):
with open(LOG_FILE_PATH, "a+") as log_file:
log_file.write(log_str)
except Timeout:
print("log file timeout:\n{}" "".format(self.log_config))
# Report the result.
result_dict = {
"error": error,
"done": True,
}
return result_dict
def run_grape():
save_file_path = generate_save_file_path(EXPERIMENT_NAME, SAVE_PATH)
config = copy(GRAPE_CONFIG)
config.update({
"save_file_path": save_file_path
})
result = grape_schroedinger_discrete(**config)
def run_grape():
t_start = time.process_time()
save_file_path = generate_save_file_path(EXPERIMENT_NAME, SAVE_PATH)
config = copy(GRAPE_CONFIG)
config.update({"save_file_path": save_file_path})
result = grape_schroedinger_discrete(**config)
tot_time = time.process_time() - t_start
print("Total time (%s iterations): %d s, time/ieration: %.2f s" %
(ITERATION_COUNT, tot_time, tot_time / ITERATION_COUNT))
def main():
result = grape_schroedinger_discrete(COSTS, hamiltonian, INITIAL_STATES,
ITERATION_COUNT, PARAM_COUNT,
PULSE_STEP_COUNT,
PULSE_TIME,
log_iteration_step=LOG_ITERATION_STEP,
max_param_norms=MAX_PARAM_NORMS,
optimizer=OPTIMIZER,
save_file_name=SAVE_FILE_NAME,
save_iteration_step=SAVE_ITERATION_STEP,
save_path=SAVE_PATH,)
def main():
result = grape_schroedinger_discrete(
COSTS,
hamiltonian,
INITIAL_STATES,
ITERATION_COUNT,
PARAM_COUNT,
PULSE_STEP_COUNT,
PULSE_TIME,
log_iteration_step=LOG_ITERATION_STEP,
max_param_norms=MAX_PARAM_NORMS,
)
def main():
result = grape_schroedinger_discrete(
CONTROL_COUNT,
CONTROL_STEP_COUNT,
COSTS,
EVOLUTION_TIME,
hamiltonian,
INITIAL_STATES,
complex_controls=COMPLEX_CONTROLS,
iteration_count=ITERATION_COUNT,
log_iteration_step=LOG_ITERATION_STEP,
save_file_path=SAVE_FILE_PATH,
save_iteration_step=SAVE_ITERATION_STEP,
)
def main():
result = grape_schroedinger_discrete(
CONTROL_COUNT,
CONTROL_STEP_COUNT,
COSTS,
EVOLUTION_TIME,
hamiltonian,
INITIAL_STATES,
ITERATION_COUNT,
log_iteration_step=LOG_ITERATION_STEP,
max_control_norms=MAX_CONTROL_NORMS,
optimizer=OPTIMIZER,
save_file_path=SAVE_FILE_PATH,
save_iteration_step=SAVE_ITERATION_STEP,
)
print("SYSTEM_EVAL_TIMES:\n{}"
"".format(anp.linspace(0, EVOLUTION_TIME, SYSTEM_EVAL_COUNT)))
# qoc saves data in h5 format. You can parse h5 files using the `h5py` package [5].
EXPERIMENT_NAME = "tutorial_schroed_cavity01"
SAVE_PATH = "./out"
SCHROED_FILE_PATH = generate_save_file_path(EXPERIMENT_NAME, SAVE_PATH)
# Next, we use the GRAPE algorithm to find a set of time-dependent
# controls that accomplishes the state transfer that we desire.
result = grape_schroedinger_discrete(CONTROL_COUNT,
CONTROL_EVAL_COUNT,
COSTS, EVOLUTION_TIME,
hamiltonian, INITIAL_STATES,
SYSTEM_EVAL_COUNT,
complex_controls=COMPLEX_CONTROLS,
iteration_count=ITERATION_COUNT,
log_iteration_step=LOG_ITERATION_STEP,
optimizer=OPTIMIZER,
save_file_path=SCHROED_FILE_PATH,
save_intermediate_states=SAVE_INTERMEDIATE_STATES,
save_iteration_step=SAVE_ITERATION_STEP,)
# Next, we want to do some analysis of our results.
CONTROLS_PLOT_FILE = "{}_controls.png".format(EXPERIMENT_NAME)
CONTROLS_PLOT_FILE_PATH = os.path.join(SAVE_PATH, CONTROLS_PLOT_FILE)
POPULATION_PLOT_FILE = "{}_population.png".format(EXPERIMENT_NAME)
POPULATION_PLOT_FILE_PATH = os.path.join(SAVE_PATH, POPULATION_PLOT_FILE)
SHOW = True
# This function will plot the controls, and their fourier transform.
plot_controls(SCHROED_FILE_PATH,
save_file_path=CONTROLS_PLOT_FILE_PATH,
def main():
result = grape_schroedinger_discrete(**GRAPE_CONFIG)
