def get_costs(control_eval_count, evolution_time):
control_norm_weights = 1 - gauss(
np.linspace(0, evolution_time, control_eval_count))
control_norm_weights = np.repeat(control_norm_weights[:, np.newaxis],
CONTROL_COUNT,
axis=1)
# Don't penalize controls in the middle.
control_offset = int(control_eval_count / 5)
control_norm_weights[control_offset:-control_offset] = 0
return [
TargetStateInfidelity(TARGET_STATES,
cost_multiplier=FIDELITY_MULTIPLIER),
ControlVariation(CONTROL_COUNT,
control_eval_count,
cost_multiplier=CONTROL_VAR_01_MULTIPLIER,
max_control_norms=MAX_CONTROL_NORMS,
order=1),
ControlVariation(CONTROL_COUNT,
control_eval_count,
cost_multiplier=CONTROL_VAR_02_MULTIPLIER,
max_control_norms=MAX_CONTROL_NORMS,
order=2),
ControlNorm(
CONTROL_COUNT,
control_eval_count,
control_weights=control_norm_weights,
cost_multiplier=CONTROL_NORM_MULTIPLIER,
max_control_norms=MAX_CONTROL_NORMS,
),
]
# Let's try to put a photon in the cavity. # That is, we desire the fock state transition |0, g> -> |1, g>. INITIAL_STATE_0 = krons(CAVITY_ZERO, TRANSMON_ZERO) # Notice that when we specify states (or probability density matrices!) # to qoc, we always give qoc an array of states that we would like it to track, # even if we only give qoc a single state. The `,` in anp.stack((INITIAL_STATE_0`,`)) # makes a difference. INITIAL_STATES = anp.stack((INITIAL_STATE_0,)) assert(INITIAL_STATES.ndim == 3) TARGET_STATE_0 = krons(CAVITY_ONE, TRANSMON_ZERO) TARGET_STATES = anp.stack((TARGET_STATE_0,)) # Costs are functions that we want qoc to minimize the output of. # In this example, we want to minimize the infidelity (maximize the fidelity) of # the initial state and the target state. # Note that `COSTS` is a list of cost function objects. COSTS = [TargetStateInfidelity(TARGET_STATES)] # We want to tell qoc how often to store information about the optimization # and how often to log output. Both `log_iteration_step` and `save_iteration_step` # are specified in units of optimization iterations. LOG_ITERATION_STEP = 1 SAVE_INTERMEDIATE_STATES = True SAVE_ITERATION_STEP = 1 # For this problem, the LBFGSB optimizer reaches a reasonable # answer very quickly. OPTIMIZER = LBFGSB() ITERATION_COUNT = 30 # In practice, we find that using a second-order optimizer, such as LBFGSB, # gives a good initial answer. Then, this answer may be used with a first- # order optimizer, such as Adam, to achieve the desired error.
CONTROL_COUNT,
axis=1)
# COSTS = [
# ControlNorm(CONTROL_COUNT, CONTROL_EVAL_COUNT,
# control_weights=CONTROL_NORM_WEIGHTS,
# cost_multiplier=1,
# max_control_norms=MAX_CONTROL_NORMS,),
# ]
# psi_f1 = tensor(basis(qnum,0),basis(mnum,11)).full()
# psi_f2 = tensor(basis(qnum,1),basis(mnum,11)).full()
# forbidden_states = np.stack([[psi_f1, psi_f2] for _ in range(STATE_COUNT)]) # For multiple states
# forbidden_states = np.stack([[psi_f1, psi_f2]],)
COSTS = [
TargetStateInfidelity(TARGET_STATES),
# ControlVariation(CONTROL_COUNT, CONTROL_EVAL_COUNT,
# cost_multiplier=1, max_control_norms=MAX_CONTROL_NORMS, order=1),
# ControlVariation(CONTROL_COUNT, CONTROL_EVAL_COUNT,
# cost_multiplier=1, max_control_norms=MAX_CONTROL_NORMS, order=2),
]
# ForbidStates(forbidden_states, SYSTEM_EVAL_COUNT, cost_multiplier=10),
# ControlArea(CONTROL_COUNT, CONTROL_EVAL_COUNT, cost_multiplier=1, max_control_norms=MAX_CONTROL_NORMS),
WDIR = os.environ["MULTIMODE_QOC_PATH"]
SAVE_PATH = os.path.join(WDIR, "out", EXPERIMENT_META)
GRAB_CONTROLS = True
if GRAB_CONTROLS:
controls_path = os.path.join(SAVE_PATH, "00002_swap_g10_g01.h5")
controls_path_lock = "{}.lock".format(controls_path)
def gauss(x):
b = anp.mean(x)
c = anp.std(x)
return anp.exp(-((x - b) ** 2) / (c ** 2))
CONTROL_NORM_WEIGHTS = 1 - gauss(anp.linspace(0, EVOLUTION_TIME, CONTROL_EVAL_COUNT))
CONTROL_NORM_WEIGHTS = anp.repeat(CONTROL_NORM_WEIGHTS[:, anp.newaxis], CONTROL_COUNT, axis=1)
# Don't penalize controls in the middle.
# CONTROL_OFFSET = int(CONTROL_COUNT / 5)
# CONTROL_NORM_WEIGHTS[CONTROL_OFFSET:-CONTROL_OFFSET] = 0
CONTROL_BANDWIDTH_MULTIPLIER = 1.
CONTROL_NORM_MULTIPLIER = 0.5
CONTROL_VAR_01_MULTIPLIER = 1.
CONTROL_VAR_02_MULTIPLIER = 1.
FIDELITY_MULTIPLIER = 1.
COSTS = [
TargetStateInfidelity(TARGET_STATES,
cost_multiplier=FIDELITY_MULTIPLIER),
ControlVariation(CONTROL_COUNT,
CONTROL_EVAL_COUNT,
cost_multiplier=CONTROL_VAR_01_MULTIPLIER,
max_control_norms=MAX_CONTROL_NORMS,
order=1),
ControlVariation(CONTROL_COUNT,
CONTROL_EVAL_COUNT,
cost_multiplier=CONTROL_VAR_02_MULTIPLIER,
max_control_norms=MAX_CONTROL_NORMS,
order=2),
# ControlNorm(CONTROL_COUNT, CONTROL_EVAL_COUNT,
# control_weights=CONTROL_NORM_WEIGHTS,
# cost_multiplier=CONTROL_NORM_MULTIPLIER,
# max_control_norms=MAX_CONTROL_NORMS,),
# ControlBandwidthMax(CONTROL_COUNT, CONTROL_EVAL_COUNT, EVOLUTION_TIME,