def set_tau_procs(initial_state,
start=500,
stop=600,
skip=1,
all_state_skip=5):
is_bools = separate_by_state(initial_state[..., 0, 0])
tau_procedures = [
sp.ReturnFinalState(),
sp.ReturnInitialState(),
sp.MeasureAllState(trial_request=slice(0, 50),
step_request=np.s_[::all_state_skip]),
sp.MeasureAllState(trial_request=np.s_[:],
step_request=np.s_[start::skip],
output_name='final_state_array'),
sp.MeasureMeanValue(rp.get_kinetic, output_name='kinetic'),
sp.MeasureMeanValue(rp.get_potential, output_name='potential'),
sp.MeasureMeanValue(rp.get_current_state,
output_name='zero_means',
trial_request=is_bools['0']),
sp.MeasureMeanValue(rp.get_current_state,
output_name='one_means',
trial_request=is_bools['1']),
tp.CountJumps(state_slice=np.s_[..., 0, 0],
step_request=np.s_[start::skip])
]
return tau_procedures
def set_mean_evolution_procs(info_state_means):
mean_evo_procs = [
sp.ReturnFinalState(),
sp.ReturnInitialState(),
sp.MeasureStepValue(rp.get_current_state,
trial_request=np.s_[0],
output_name='zero_means'),
sp.MeasureStepValue(rp.get_current_state,
trial_request=np.s_[1],
output_name='one_means')
]
return mean_evo_procs
def set_mean_procs(initial_state):
is_bools = separate_by_state(initial_state[..., 0, 0])
mean_procs = [
sp.ReturnInitialState(),
sp.ReturnFinalState(),
sp.MeasureMeanValue(rp.get_current_state,
output_name='zero_means',
trial_request=is_bools['0']),
sp.MeasureMeanValue(rp.get_current_state,
output_name='one_means',
trial_request=is_bools['1'])
]
return mean_procs
def set_bundle_evo_procs(state_bundle, weights=None):
is_bools = separate_by_state(state_bundle[..., 0, 0])
w_z = weights[is_bools['0']]
w_o = weights[is_bools['1']]
bundle_evo_procs = [
sp.ReturnFinalState(),
sp.ReturnInitialState(),
sp.MeasureMeanValue(rp.get_current_state,
trial_request=is_bools['0'],
output_name='zero_means',
weights=w_z),
sp.MeasureMeanValue(rp.get_current_state,
trial_request=is_bools['1'],
output_name='one_means',
weights=w_o)
]
return bundle_evo_procs