# logFormatter = logging.Formatter("%(asctime)s [%(threadName)-12.12s]"
# "[%(levelname)-5.5s] %(message)s")
# rootLogger = logging.getLogger()
# rootLogger.setLevel(logging.DEBUG)
# # consoleHandler = logging.StreamHandler()
# # consoleHandler.setFormatter(logFormatter)
# # rootLogger.addHandler(consoleHandler)
# fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
# fileHandler.setFormatter(logFormatter)
# fileHandler.setLevel(logging.DEBUG)
# rootLogger.addHandler(fileHandler)
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,
protocol_options=dict(num_frequencies=num_frequencies),
optimization_method=optimization_method,
initial_parameters=initial_parameters,
parameters_constraints=parameters_constraints,
optimization_options=dict(xatol=1e-16, fatol=1e-16, disp=True)),
other_options=dict(scan_times=np.linspace(2, 3.2, 500)))
# ------ save results to file
results.to_csv(output_file_name)
import numpy as np
import pandas as pd
import logging
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
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='rabi',
model_parameters=dict(N=100,
Omega=100,
omega_0=1.),
task=dict(initial_intensity=0,
final_intensity=10)),
optimization_specs=dict(protocol='crab',
protocol_options=dict(num_frequencies=8),
optimization_method='Nelder-Mead',
parameters_constraints=[-20, 20]),
other_options=dict(scan_times=np.linspace(4, 20, 400)))
# ------ save results to file
results.to_csv(output_file_name)
# rootLogger.addHandler(consoleHandler)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
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 state generation'
),
optimization_specs=dict(
protocol=protocol,
optimization_method=optimization_method,
initial_parameters=initial_parameters,
parameters_constraints=parameters_constraints
),
other_options=dict(
scan_times=np.linspace(0.1, 2, 100)
)
)
# ------ save results to file
results.to_csv(output_file_name)
import pandas as pd
import logging
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
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
num_frequencies = 8
results = optimization.find_best_protocol(
problem_specification=dict(model='lz',
model_parameters=dict(omega_0=1),
task=dict(initial_intensity=-5,
final_intensity=5)),
optimization_specs=dict(
protocol='crab',
protocol_options=dict(num_frequencies=num_frequencies),
optimization_method='Nelder-Mead',
parameters_constraints=[-10, 10],
initial_parameters=[[-1, 1]] * (2 * num_frequencies)),
other_options=dict(scan_times=np.linspace(0.01, 10, 400)))
# ------ save results to file
results.to_csv(output_file_name)
# ------ start optimization
num_frequencies = 8
protocol = protocol_ansatz.CRABProtocolAnsatz(num_frequencies=num_frequencies)
protocol.generate_rnd_frequencies_each_tf = False
for idx in range(num_frequencies):
protocol.hyperpars['nuk' + str(idx + 1)] = 0
protocol.fill_hyperpar_value(y0=-5, y1=5)
results = optimization.find_best_protocol(
problem_specification=dict(
model='lz',
model_parameters=dict(omega_0=1),
task=dict(initial_intensity=-5, final_intensity=5)
),
optimization_specs=dict(
protocol=protocol,
protocol_options=dict(num_frequencies=num_frequencies),
optimization_method='Nelder-Mead',
parameters_constraints=[-10, 10],
initial_parameters=[0] * (2 * num_frequencies),
optimization_options=dict(maxiter=1e5, maxfev=1e5,
xatol=1e-8, fatol=1e-8, adaptive=True)
),
other_options=dict(
scan_times=np.linspace(1, 2, 30)
)
)
# ------ save results to file
results.to_csv(output_file_name)
rootLogger.setLevel(logging.DEBUG)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
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=task
),
optimization_specs=dict(
protocol=protocol,
optimization_method=optimization_method,
parameters_constraints=parameters_constraints,
initial_parameters=initial_parameters
),
other_options=dict(
scan_times=np.linspace(time_initial, time_final, time_subintervals)
)
)
# ------ save results to file
results.to_csv(output_file_name)
import logging
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
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))
third_time = lambda t: t / 3.
twothirds_time = lambda t: 2 * t / 3.
initial_parameters = [[-1, 1]] * 3 + [third_time, twothirds_time]
# ------ start optimization
results = optimization.find_best_protocol(
problem_specification=dict(model='lmg',
model_parameters=dict(num_spins=50),
task=dict(initial_intensity=0,
final_intensity=2)),
optimization_specs=dict(
protocol=protocol_ansatz.TripleBangProtocolAnsatz(),
optimization_method='Nelder-Mead',
parameters_constraints=[-10, 10],
initial_parameters=initial_parameters),
other_options=dict(scan_times=np.linspace(10, 20, 400)))
# ------ save results to file
results.to_csv(output_file_name)
sys.path.append('../../')
import src.optimization as optimization
import src.protocol_ansatz as protocol_ansatz
from src.utils import autonumber_filename, basic_logger_configuration
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))
third_time = lambda t: t / 3.
twothirds_time = lambda t: 2 * t / 3.
initial_parameters = [[-1, 1]] * 3 + [third_time, twothirds_time]
# ------ start optimization
results = optimization.find_best_protocol(
problem_specification=dict(model='rabi',
model_parameters=dict(N=100,
Omega=100,
omega_0=1.),
task=dict(initial_intensity=0,
final_intensity=10)),
optimization_specs=dict(
protocol=protocol_ansatz.TripleBangProtocolAnsatz(),
optimization_method='powell',
parameters_constraints=[-20, 20],
initial_parameters=initial_parameters),
other_options=dict(scan_times=np.linspace(4, 20, 400)))
# ------ save results to file
results.to_csv(output_file_name)
import os
import sys
import numpy as np
import pandas as pd
import logging
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
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='lmg',
model_parameters=dict(num_spins=50),
task=dict(initial_intensity=0,
final_intensity=2)),
optimization_specs=dict(protocol='crab',
protocol_options=dict(num_frequencies=8),
optimization_method='Nelder-Mead',
parameters_constraints=[-10, 10]),
other_options=dict(scan_times=np.linspace(10, 20, 400)))
# ------ save results to file
results.to_csv(output_file_name)
# consoleHandler = logging.StreamHandler()
# consoleHandler.setFormatter(logFormatter)
# rootLogger.addHandler(consoleHandler)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
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 state generation'
),
optimization_specs=dict(
protocol=protocol, protocol_options=dict(num_frequencies=2),
optimization_method=optimization_method
),
other_options=dict(
scan_times=np.linspace(0.1, 4, 100)
)
)
# ------ save results to file
results.to_csv(output_file_name)
# consoleHandler.setFormatter(logFormatter)
# rootLogger.addHandler(consoleHandler)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
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=task
),
optimization_specs=dict(
protocol=protocol,
optimization_method=optimization_method,
parameters_constraints=parameters_constraints
),
other_options=dict(
scan_times=np.linspace(0.1, 4, 200)
)
)
# ------ save results to file
results.to_csv(output_file_name)
rootLogger.setLevel(logging.DEBUG)
# consoleHandler = logging.StreamHandler()
# consoleHandler.setFormatter(logFormatter)
# rootLogger.addHandler(consoleHandler)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
logging.info('Output file name will be "{}"'.format(output_file_name))
# ------ start optimization
num_spins_list = np.arange(50, 210, 10) # [50, 60, ..., 140, 150]
tf_results = np.zeros(shape=(len(num_spins_list),
2)) # will contain (num_spin, tf)
for idx, num_spins in enumerate(num_spins_list):
logging.info('Starting optimization with {} spins'.format(num_spins))
results = optimization.find_best_protocol(
problem_specification=dict(model=model,
model_parameters=dict(num_spins=num_spins),
task='critical point state generation'),
optimization_specs=dict(protocol=protocol,
optimization_method=optimization_method),
other_options=dict(scan_times=np.arange(1, 2, 0.01),
stopping_condition=0.99))
tf_results[idx] = [num_spins, results.iloc[-1]['tf']]
tf_results = pd.DataFrame(tf_results, columns=['num_spins', 'tf'])
# ------ save results to file
tf_results.to_csv(output_file_name)
import os
import sys
import numpy as np
import pandas as pd
import logging
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
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='lz',
model_parameters=dict(omega_0=1),
task=dict(initial_intensity=-5,
final_intensity=5)),
optimization_specs=dict(protocol='doublebang',
optimization_method='Powell',
parameters_constraints=[-30, 30],
initial_parameters=[[-2, 2], 'halftime', [-2, 2]]),
other_options=dict(scan_times=np.linspace(0.01, 10, 400)))
# ------ save results to file
results.to_csv(output_file_name)
logFormatter = logging.Formatter("%(asctime)s [%(threadName)-12.12s]"
"[%(levelname)-5.5s] %(message)s")
rootLogger = logging.getLogger()
rootLogger.setLevel(logging.DEBUG)
# consoleHandler = logging.StreamHandler()
# consoleHandler.setFormatter(logFormatter)
# rootLogger.addHandler(consoleHandler)
fileHandler = logging.FileHandler(output_file_name[:-4] + '.log')
fileHandler.setFormatter(logFormatter)
fileHandler.setLevel(logging.DEBUG)
rootLogger.addHandler(fileHandler)
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,
optimization_options=dict(xtol=1e-16,
ftol=1e-16,
disp=False)),
other_options=dict(scan_times=np.linspace(0.1, 20, 1000)))
# ------ save results to file
results.to_csv(output_file_name)
import os
import sys
import numpy as np
import pandas as pd
import logging
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
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='lmg',
model_parameters=dict(num_spins=20),
task=dict(initial_intensity=0,
final_intensity=2)),
optimization_specs=dict(protocol='doublebang',
optimization_method='Powell',
parameters_constraints=[-10, 10]),
other_options=dict(scan_times=np.linspace(0.1, 20, 400)))
# ------ save results to file
results.to_csv(output_file_name)
rootLogger.addHandler(fileHandler)
logging.info('Output file name will be "{}"'.format(output_file_name))
# ------ start optimization
Omegas_list = np.arange(20, 210, 10)
tf_results = np.zeros(shape=(len(Omegas_list),
6)) # will contain (num_spin, tf)
for idx, Omega in enumerate(Omegas_list):
logging.info('Starting optimization with {} spins'.format(Omega))
results = optimization.find_best_protocol(
problem_specification=dict(model=model,
model_parameters=dict(N=Omega,
Omega=Omega,
omega_0=1.),
task='critical point'),
optimization_specs=dict(protocol=protocol,
optimization_method=optimization_method,
optimization_options=dict(xtol=1e-8,
ftol=1e-8,
disp=True)),
other_options=dict(scan_times=np.arange(1, 3, 0.01),
stopping_condition=0.999))
tf_results[idx][0] = Omega
tf_results[idx][1:] = results.iloc[-1]
tf_results = pd.DataFrame(tf_results,
columns=['Omega', 'tf', 'fid', 'y0', 't1', 'y1'])
# ------ save results to file
tf_results.to_csv(output_file_name)
