def evolve_state(hamiltonian, initial_state, tlist, return_all_states=False):
"""Evolve state with (generally time-dependent) hamiltonian.
This is a wrapper around `qutip.mesolve`, to which `hamiltonian` and
`initial_state` are directly fed.
Parameters
----------
hamiltonian : list of qutip objects
The Hamiltonian specification in `qutip.mesolve` is the
"function based" one, as per qutip.mesolve documentation. This means
that `hamiltonian` is to be given as a list of constant Hamiltonians,
each one pairs with a time-dependent (numeric) coefficient.
The simplest example would be `hamiltonian = [H0, [H1, coeffFun]]`.
initial_state : qutip.Qobj
time : float or list of floats
If a single number, it is divided into a number of subintervals and
the result used as input for `qutip.mesolve`. If a list of numbers,
it is directly fed to `qutip.mesolve`.
"""
if isinstance(tlist, numbers.Number):
tlist = np.linspace(0, tlist, 40)
try:
evolving_states = qutip.mesolve(hamiltonian, initial_state, tlist)
except Exception:
error_data_filename = 'evolution_error_details_{}.pickle'.format(
timestamp())
error_data_filename = os.path.join(os.getcwd(), error_data_filename)
error_data_filename = autonumber_filename(error_data_filename)
logging.info('Something went wrong while trying to evolve from\n'
'initial_state={}\nwith hamiltonian={}.\nSaving data to '
'reproduce in "{}".'.format(initial_state, hamiltonian,
error_data_filename))
with open(error_data_filename, 'wb') as fh:
if len(hamiltonian) == 2 and len(hamiltonian[1]) == 2:
pulse_samples = [
hamiltonian[1][1](t) for t in np.linspace(0, time, 100)
]
hamiltonian = (hamiltonian[0], hamiltonian[1][0])
data = dict(hamiltonian=hamiltonian,
initial_state=initial_state,
times_list=tlist)
data = data.update(dict(pulse_samples=pulse_samples))
print('data: {}'.format(data))
pickle.dump(data, fh)
raise
if return_all_states:
return evolving_states.states
else:
return evolving_states.states[-1]
if '../../' not in sys.path:
sys.path.append('../../')
import src.optimization as optimization
import src.protocol_ansatz as protocol_ansatz
from src.utils import autonumber_filename, basic_logger_configuration
model = 'lmg'
model_parameters = dict(num_spins=50)
optimization_method = 'Powell'
protocol = 'doublebang'
initial_parameters = [[-1, 1], 'halftime', [-1, 1]]
parameters_constraints = [-6, 6]
# ------ build and check name for output file
output_file_name = os.path.basename(__file__)[7:-3] + '.csv'
output_file_name = autonumber_filename(output_file_name)
basic_logger_configuration(filename=output_file_name[:-3] + 'log')
logging.info('Output file name will be "{}"'.format(output_file_name))
# ------ start optimization
results = optimization.find_best_protocol(
problem_specification=dict(
model=model,
model_parameters=model_parameters,
task='critical point'
),
optimization_specs=dict(
protocol=protocol,
optimization_method=optimization_method,
initial_parameters=initial_parameters,
parameters_constraints=parameters_constraints
