def probabilities(self, out_state, sort=False):
state_copy = out_state.full().copy()
length = state_copy.shape[0]-len(self.state.indistinguishable_states)
new_state = np.zeros((length,1),dtype=np.complex128)
new_dict = {}
for i,(label,dim) in enumerate(self.state.reduced_label_map.iteritems()):
reverse_label = "_".join(label.split('_')[::-1])
if (label, reverse_label) in self.state.indistinguishable_states.keys():
if (label, reverse_label) not in new_dict.keys():
new_key = (label, reverse_label)
new_dict[new_key] = np.complex128(0)
dims_to_add = self.state.indistinguishable_states[(label, reverse_label)]
for dim in dims_to_add:
new_dict[new_key] += state_copy[dim]
else:
if (label) not in new_dict.keys():
if (reverse_label) not in new_dict.keys():
if (label, reverse_label) not in self.state.indistinguishable_states.keys():
if (reverse_label, label) not in self.state.indistinguishable_states.keys():
new_dict[(label)] = state_copy[dim]
new_state = np.array(new_dict.values())
new_qstate = Qobj(new_state).unit()
prob_array = new_qstate.conj().full()*new_qstate.full()
probs = {lab: prob for lab,prob in zip(new_dict.keys(), prob_array)}
probs = {lab: prob.real.tolist()[0] for lab,prob in probs.iteritems() }
# Check that probabilities add to one
np.testing.assert_almost_equal(sum(probs.values()),1.0,decimal=5)
# Return list if sort=True, else return dict
if sort:
return sorted(probs.items(), key=lambda x:x[1])
else:
return probs
def generate_training_sample(unit_nb, ctrl_init, initial, params, n_ts,
evo_time, noise_name, model_dim):
f_ext = None
path_template = "training/dim_{}/mtx/idx_{}"
fid_err_targ = 1e-12
max_iter = 200000
max_wall_time = 5 * 60
min_grad = 1e-20
#target_DP = 0
if model_dim == "2x1":
current_path = (path_template).format(model_dim, unit_nb)
if pathlib.Path(current_path + ".npz").exists():
rnd_unit = Qobj(np.load(current_path + ".npz")["arr_0"])
rnd_unitC = rnd_unit.conj()
target_DP = tensor(rnd_unit, rnd_unitC)
else:
rnd_unit = tensor(rand_unitary(2), identity(2))
rnd_unitC = rnd_unit.conj()
np.savez(current_path, rnd_unit.full())
target_DP = tensor(rnd_unit, rnd_unitC)
elif model_dim == "2" or model_dim == "4":
current_path = (path_template).format(model_dim, unit_nb)
if pathlib.Path(current_path + ".npz").exists():
rnd_unit = Qobj(np.load(current_path + ".npz")["arr_0"])
rnd_unitC = rnd_unit.conj()
target_DP = tensor(rnd_unit, rnd_unitC)
else:
rnd_unit = rand_unitary(int(model_dim))
rnd_unitC = rnd_unit.conj()
np.savez(current_path, rnd_unit.full())
target_DP = tensor(rnd_unit, rnd_unitC)
if noise_name == 'id_aSxbSy_spinChain_2x1':
ctrls, drift = id_aSxbSy_spinChain_2x1(params)
elif noise_name == "aSxbSy_id_spinChain_dim_2x1":
ctrls, drift = aSxbSy_id_spinChain_dim_2x1(params)
ctrls = [Qobj(ctrls[i]) for i in range(len(ctrls))]
drift = Qobj(drift)
result = cpo.optimize_pulse(drift,
ctrls,
initial,
target_DP,
n_ts,
evo_time,
amp_lbound=-1,
amp_ubound=1,
fid_err_targ=fid_err_targ,
min_grad=min_grad,
max_iter=max_iter,
max_wall_time=max_wall_time,
out_file_ext=f_ext,
init_pulse_type=ctrl_init,
log_level=log_level,
gen_stats=True)
print("Sample number ", unit_nb, " have error ", result.fid_err)
np.savez("training/dim_{}/NCP_data/idx_{}".format(model_dim, unit_nb),
result.final_amps)
def generate_training_sample(
unit_nb, params, argv_number
): # ctrl_init, noise_params, n_ts,evo_time,noise_name, model_dim, supeop_size):
f_ext = None
path_template = "training/dim_{}/mtx/idx_{}"
fid_err_targ = 1e-12
max_iter = 200000
max_wall_time = 5 * 60
min_grad = 1e-20
#target_DP = 0
if params.model_dim == "2x1":
current_path = (path_template).format(params.model_dim, unit_nb)
if pathlib.Path(current_path + ".npz").exists():
rnd_unit = Qobj(np.load(current_path + ".npz")["arr_0"])
rnd_unitC = rnd_unit.conj()
target_DP = tensor(rnd_unit, rnd_unitC)
else:
rnd_unit = tensor(rand_unitary(2), identity(2))
rnd_unitC = rnd_unit.conj()
np.savez(current_path, rnd_unit.full())
target_DP = tensor(rnd_unit, rnd_unitC)
elif params.model_dim == "2" or model_dim == "4":
current_path = (path_template).format(params.model_dim, unit_nb)
if pathlib.Path(current_path + ".npz").exists():
rnd_unit = Qobj(np.load(current_path + ".npz")["arr_0"])
rnd_unitC = rnd_unit.conj()
target_DP = tensor(rnd_unit, rnd_unitC)
else:
rnd_unit = rand_unitary(int(dim))
rnd_unitC = rnd_unit.conj()
np.savez(current_path, rnd_unit.full())
target_DP = tensor(rnd_unit, rnd_unitC)
if params.noise_name == 'id_aSxbSy_spinChain_2x1':
ctrls, drift = id_aSxbSy_spinChain_2x1(params.noise_params)
elif params.noise_name == "aSxbSy_id_spinChain_dim_2x1":
ctrls, drift = aSxbSy_id_spinChain_dim_2x1(params.noise_params)
elif params.noise_name == "spinChainDrift_spinChain_dim_2x1":
ctrls, drift = spinChainDrift_spinChain_dim_2x1(params.noise_params)
elif params.noise_name == "Sz_id_and_ketbra01_id_Lindbald_spinChain_drift":
ctrls, drift = Sz_id_and_ketbra01_id_Lindbald_spinChain_drift(
params.noise_params)
elif params.noise_name == "ketbra01_id_Lindbald_spinChain_drift":
ctrls, drift = ketbra01_id_Lindbald_spinChain_drift(
params.noise_params)
elif params.noise_name == "Sz_id_and_ketbra01_id_and_reverse_Lindbald_spinChain_drift":
ctrls, drift = Sz_id_and_ketbra01_id_and_reverse_Lindbald_spinChain_drift(
params.noise_params)
elif params.noise_name == "Sz_id_id_Sz_Lindbald_spinChain_drift":
ctrls, drift = Sz_id_id_Sz_Lindbald_spinChain_drift(
params.noise_params)
ctrls = [Qobj(ctrls[i]) for i in range(len(ctrls))]
drift = Qobj(drift)
initial = identity(params.supeop_size)
if argv_number == 0.:
result = cpo.optimize_pulse(drift,
ctrls,
initial,
target_DP,
params.n_ts,
params.evo_time,
amp_lbound=-1,
amp_ubound=1,
fid_err_targ=fid_err_targ,
min_grad=min_grad,
max_iter=max_iter,
max_wall_time=max_wall_time,
out_file_ext=f_ext,
init_pulse_type=params.ctrl_init,
log_level=log_level,
gen_stats=True)
print("Sample number ", unit_nb, " have error ", result.fid_err)
np.savez(
"training/dim_{}/NCP_data_unbounded/idx_{}".format(
params.model_dim, unit_nb), result.final_amps)
else:
ampsy = np.load("training/dim_{}/NCP_data/idx_{}.npz".format(
params.model_dim, unit_nb))['arr_0']
result = my_opt(drift,
ctrls,
initial,
target_DP,
params.n_ts,
params.evo_time,
amp_lbound=-1,
amp_ubound=1,
fid_err_targ=fid_err_targ,
min_grad=min_grad,
max_iter=max_iter,
max_wall_time=max_wall_time,
out_file_ext=f_ext,
init_pulse_type=params.ctrl_init,
log_level=log_level,
gen_stats=True,
init_pulse=ampsy)
print("Sample number ", unit_nb, " have error ", 1 - result.fid_err)
np.savez(
"training/dim_{}/DCP_data/DCP_config{}/idx_{}".format(
params.model_dim, argv_number, unit_nb), result.final_amps)
