MAX_PARAM_NORMS = (
MAX_AMP_C,
MAX_AMP_T,
)
OPTIMIZER = Adam()
PULSE_TIME = 500
PULSE_STEP_COUNT = 500
ITERATION_COUNT = 5000
# Define the problem.
INITIAL_STATE_0 = np.kron(CAVITY_ZERO, TRANSMON_G)
TARGET_STATE_0 = np.kron(CAVITY_ONE, TRANSMON_G)
INITIAL_STATES = np.stack((INITIAL_STATE_0, ), )
TARGET_STATES = np.stack((TARGET_STATE_0, ), )
COSTS = (TargetInfidelity(TARGET_STATES),
ParamVariation(MAX_PARAM_NORMS, PARAM_COUNT, PULSE_STEP_COUNT))
# Define the output.
LOG_ITERATION_STEP = 1
SAVE_ITERATION_STEP = 1
SAVE_FILE_NAME = "binomialcode_exp2_3"
SAVE_PATH = os.path.join(
"./pulses/",
SAVE_FILE_NAME,
)
def main():
result = grape_schroedinger_discrete(
COSTS,
hamiltonian,
# Define the optimization.
ITERATION_COUNT = 5000
OPTIMIZER = Adam()
PARAM_COUNT = 3
PULSE_TIME = 500
PULSE_STEP_COUNT = PULSE_TIME
# Define the problem.
INITIAL_STATE_0 = anp.kron(TRANSMON_G, CAVITY_ZERO)
INITIAL_STATES = anp.stack((INITIAL_STATE_0,))
TARGET_STATE_0 = anp.kron(TRANSMON_G, CAVITY_ONE)
TARGET_STATES = anp.stack((TARGET_STATE_0,))
COSTS = (TargetInfidelity(TARGET_STATES),
ParamVariation(MAX_PARAM_NORMS,
PARAM_COUNT,
PULSE_STEP_COUNT,
cost_multiplier=0.5,
order=1),
ParamVariation(MAX_PARAM_NORMS,
PARAM_COUNT,
PULSE_STEP_COUNT,
cost_multiplier=0.5,
order=2),)
# Define the output.
LOG_ITERATION_STEP = 1
SAVE_FILE_NAME = "piccolo_exp1"
SAVE_ITERATION_STEP = 1
SAVE_PATH = os.path.join("./pulses/", SAVE_FILE_NAME,)