def setup_class(cls):
# gets called at initialization of test class
cls.AWG = v8.VirtualAWG8('DummyAWG8')
cls.fluxlutman = flm.AWG8_Flux_LutMan('fluxlutman_main')
cls.k0 = lko.LinDistortionKernel('k0')
cls.fluxlutman_partner = flm.AWG8_Flux_LutMan('fluxlutman_partner')
def test_simulate_using_detector(self):
fluxlutman = flm.AWG8_Flux_LutMan('fluxlutman')
# Hamiltonian pars
alpha_q0 = 285e6 * 2 * np.pi
J = 2.9e6 * 2 * np.pi
w_q0 = 4.11e9 * 2 * np.pi
w_q1 = 5.42e9 * 2 * np.pi
H_0 = czu.coupled_transmons_hamiltonian(w_q0,
w_q1,
alpha_q0=alpha_q0,
J=J)
fluxlutman.cz_J2(J * np.sqrt(2))
fluxlutman.cz_freq_01_max(w_q1)
fluxlutman.cz_freq_interaction(w_q0 + alpha_q0)
fluxlutman.cz_theta_f(80)
fluxlutman.cz_lambda_2(0)
fluxlutman.cz_lambda_3(0)
fluxlutman.sampling_rate(2.4e9)
fluxlutman.cz_length(220e-9)
d = czu.CZ_trajectory(H_0=H_0, fluxlutman=fluxlutman)
vals = d.acquire_data_point()
self.assertAlmostEquals(vals[0], 13.588, places=1)
self.assertAlmostEquals(vals[1], 166.87, places=1)
self.assertAlmostEquals(vals[2], 0.0920, places=1)
self.assertAlmostEquals(vals[3], 0.1841, places=1)
fluxlutman.close()
def f_to_parallelize_new(arglist):
# cluster wants a list as an argument.
# Below the various list items are assigned to their own variable
fitted_stepresponse_ty = arglist['fitted_stepresponse_ty']
fluxlutman_args = arglist['fluxlutman_args'] # see function return_instrument_args in czf
noise_parameters_CZ_args = arglist['noise_parameters_CZ_args'] # see function return_instrument_args in czf
number = arglist['number']
adaptive_pars = arglist['adaptive_pars']
try:
MC = Instrument.find_instrument('MC'+'{}'.format(number))
except KeyError:
MC = mc.MeasurementControl('MC'+'{}'.format(number), live_plot_enabled=False)
from qcodes import station
station = station.Station()
station.add_component(MC)
MC.station =station
fluxlutman = flm.AWG8_Flux_LutMan('fluxlutman'+'{}'.format(number))
station.add_component(fluxlutman)
noise_parameters_CZ = npCZ.NoiseParametersCZ('noise_parameters_CZ'+'{}'.format(number))
station.add_component(noise_parameters_CZ)
fluxlutman, noise_parameters_CZ = czf.return_instrument_from_arglist(fluxlutman,fluxlutman_args,noise_parameters_CZ,noise_parameters_CZ_args)
d=ramsey_experiment(fluxlutman=fluxlutman, noise_parameters_CZ=noise_parameters_CZ,
fitted_stepresponse_ty=fitted_stepresponse_ty)
MC.set_sweep_functions([fluxlutman.cz_length])
MC.set_detector_function(d)
MC.set_sweep_points(np.arange(0, adaptive_pars['max_time'], adaptive_pars['time_step']))
exp_metadata = {'detuning': noise_parameters_CZ.detuning(),
'sigma_q1': noise_parameters_CZ.sigma_q1(),
'sigma_q0': noise_parameters_CZ.sigma_q0()}
if noise_parameters_CZ.cluster():
dat = MC.run('1D ramsey_new_cluster sigma_q1 {:.0f}, sigma_q0 {:.0f}, detuning {:.0f}'.format(noise_parameters_CZ.sigma_q1()*1e6, noise_parameters_CZ.sigma_q0()*1e6,
noise_parameters_CZ.detuning()/1e6),
mode='1D',exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run('1D ramsey_new sigma_q1 {:.0f}, sigma_q0 {:.0f}, detuning {:.0f}'.format(noise_parameters_CZ.sigma_q1()*1e6, noise_parameters_CZ.sigma_q0()*1e6,
noise_parameters_CZ.detuning()/1e6),
mode='1D',exp_metadata=exp_metadata)
else:
dat = MC.run('1D ramsey_new', mode='1D',exp_metadata=exp_metadata)
fluxlutman.close()
noise_parameters_CZ.close()
MC.close()
def setUpClass(self):
self.AWG = v8.VirtualAWG8('DummyAWG8')
self.fluxlutman = flm.AWG8_Flux_LutMan('Flux_LutMan')
self.k0 = ko.DistortionKernel('k0')
self.fluxlutman.instr_distortion_kernel(self.k0.name)
self.fluxlutman.AWG(self.AWG.name)
self.fluxlutman.sampling_rate(2.4e9)
self.fluxlutman.cz_theta_f(80)
self.fluxlutman.cz_freq_01_max(6.8e9)
self.fluxlutman.cz_J2(4.1e6)
# self.fluxlutman.cz_E_c(250e6)
self.fluxlutman.cz_freq_interaction(5.1e9)
self.fluxlutman.cfg_max_wf_length(5e-6)
poly_coeffs = np.array(
[1.95027142e+09, -3.22560292e+08, 5.25834946e+07])
self.fluxlutman.polycoeffs_freq_conv(poly_coeffs)
def f_to_parallelize_new(arglist):
# cluster wants a list as an argument.
# Below the various list items are assigned to their own variable
fitted_stepresponse_ty = arglist['fitted_stepresponse_ty']
fluxlutman_args = arglist[
'fluxlutman_args'] # see function return_instrument_args in czf
noise_parameters_CZ_args = arglist[
'noise_parameters_CZ_args'] # see function return_instrument_args in czf
number = arglist['number']
adaptive_pars = arglist['adaptive_pars']
try:
MC = Instrument.find_instrument('MC' + '{}'.format(number))
except KeyError:
MC = mc.MeasurementControl('MC' + '{}'.format(number),
live_plot_enabled=False)
from qcodes import station
station = station.Station()
station.add_component(MC)
MC.station = station
fluxlutman = flm.AWG8_Flux_LutMan('fluxlutman' + '{}'.format(number))
station.add_component(fluxlutman)
noise_parameters_CZ = npCZ.NoiseParametersCZ('noise_parameters_CZ' +
'{}'.format(number))
station.add_component(noise_parameters_CZ)
fluxlutman, noise_parameters_CZ = czf.return_instrument_from_arglist(
fluxlutman, fluxlutman_args, noise_parameters_CZ,
noise_parameters_CZ_args)
d = CZ_trajectory_superoperator(
fluxlutman=fluxlutman,
noise_parameters_CZ=noise_parameters_CZ,
fitted_stepresponse_ty=fitted_stepresponse_ty,
qois=adaptive_pars.get('qois', 'all'))
MC.set_detector_function(d)
exp_metadata = {
'double sided': fluxlutman.czd_double_sided(),
'length': fluxlutman.cz_length(),
'distortions': noise_parameters_CZ.distortions(),
'T2_scaling': noise_parameters_CZ.T2_scaling(),
'sigma_q1': noise_parameters_CZ.sigma_q1(),
'sigma_q0': noise_parameters_CZ.sigma_q0()
}
if adaptive_pars['mode'] == 'adaptive':
MC.set_sweep_functions([fluxlutman.cz_theta_f, fluxlutman.cz_lambda_2])
if adaptive_pars['uniform']:
loss_per_triangle = adaptive.learner.learner2D.uniform_loss
else:
loss_per_triangle = None
MC.set_adaptive_function_parameters({
'adaptive_function':
adaptive.Learner2D,
'loss_per_triangle':
loss_per_triangle,
'goal':
lambda l: l.npoints > adaptive_pars['n_points'],
'bounds':
[(adaptive_pars['theta_f_min'], adaptive_pars['theta_f_max']),
(adaptive_pars['lambda2_min'], adaptive_pars['lambda2_max'])]
})
if noise_parameters_CZ.cluster():
dat = MC.run(
'2D simulation_new_cluster2 double sided {} - length {:.1f} - distortions {} - waiting {:.2f} - T2_scaling {:.2f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.waiting_at_sweetspot(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'2D simulation_new_2 double sided {} - length {:.1f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
dat = MC.run('2D simulation_new_2',
exp_metadata=exp_metadata,
mode='adaptive')
elif adaptive_pars['mode'] == '1D':
MC.set_sweep_functions([fluxlutman.cz_theta_f])
MC.set_sweep_points(
np.linspace(adaptive_pars['theta_f_min'],
adaptive_pars['theta_f_max'],
adaptive_pars['n_points']))
if noise_parameters_CZ.cluster():
dat = MC.run(
'1D simulation_new_cluster2 double sided {} - length {:.1f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'1D simulation_new_2 double sided {} - length {:.1f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
dat = MC.run('1D simulation_new_2',
exp_metadata=exp_metadata,
mode='1D')
if adaptive_pars['mode'] == 'cma_optimizer':
MC.set_sweep_functions([fluxlutman.cz_theta_f, fluxlutman.cz_lambda_2])
if adaptive_pars['uniform']:
loss_per_triangle = adaptive.learner.learner2D.uniform_loss
else:
loss_per_triangle = None
MC.set_adaptive_function_parameters({
'adaptive_function': cma.fmin,
'x0': adaptive_pars['x0'],
'sigma0': adaptive_pars['sigma0'],
# options for the CMA algorithm can be found using
# "cma.CMAOptions()"
'options': {
'maxfevals':
adaptive_pars['n_points'], # maximum function cals
# Scaling for individual sigma's
'cma_stds': [5, 6, 3],
'ftarget': 0.005
}, # Target function value
})
if noise_parameters_CZ.cluster():
dat = MC.run(
'2D simulation_new_cluster2 double sided {} - length {:.1f} - waiting {:.2f} - T2_scaling {:.2f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.waiting_at_sweetspot(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'2D simulation_new_2 double sided {} - length {:.1f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
dat = MC.run('2D simulation_new_2',
exp_metadata=exp_metadata,
mode='adaptive')
fluxlutman.close()
noise_parameters_CZ.close()
MC.close()
def f_to_parallelize_new(arglist):
# cluster wants a list as an argument.
# Below the various list items are assigned to their own variable
fitted_stepresponse_ty = arglist['fitted_stepresponse_ty']
fluxlutman_args = arglist[
'fluxlutman_args'] # see function return_instrument_args in czf
noise_parameters_CZ_args = arglist[
'noise_parameters_CZ_args'] # see function return_instrument_args in czf
number = arglist['number']
adaptive_pars = arglist['adaptive_pars']
try:
MC = Instrument.find_instrument('MC' + '{}'.format(number))
except KeyError:
MC = mc.MeasurementControl('MC' + '{}'.format(number),
live_plot_enabled=False)
from qcodes import station
station = station.Station()
station.add_component(MC)
MC.station = station
fluxlutman = flm.AWG8_Flux_LutMan('fluxlutman' + '{}'.format(number))
station.add_component(fluxlutman)
noise_parameters_CZ = npCZ.NoiseParametersCZ('noise_parameters_CZ' +
'{}'.format(number))
station.add_component(noise_parameters_CZ)
fluxlutman, noise_parameters_CZ = czf.return_instrument_from_arglist(
fluxlutman, fluxlutman_args, noise_parameters_CZ,
noise_parameters_CZ_args)
d = CZ_trajectory_superoperator(
fluxlutman=fluxlutman,
noise_parameters_CZ=noise_parameters_CZ,
fitted_stepresponse_ty=fitted_stepresponse_ty,
qois=adaptive_pars.get('qois', 'all'))
MC.set_detector_function(d)
exp_metadata = {
'double sided': fluxlutman.czd_double_sided(),
'length': fluxlutman.cz_length(),
'distortions': noise_parameters_CZ.distortions(),
'T2_scaling': noise_parameters_CZ.T2_scaling(),
'sigma_q1': noise_parameters_CZ.sigma_q1(),
'sigma_q0': noise_parameters_CZ.sigma_q0()
}
if adaptive_pars['mode'] == 'adaptive':
MC.set_sweep_functions([fluxlutman.cz_theta_f, fluxlutman.cz_lambda_2])
if adaptive_pars['uniform']:
loss_per_triangle = adaptive.learner.learner2D.uniform_loss
else:
loss_per_triangle = None
MC.set_adaptive_function_parameters({
'adaptive_function':
adaptive.Learner2D,
'loss_per_triangle':
loss_per_triangle,
'goal':
lambda l: l.npoints > adaptive_pars['n_points'],
'bounds':
[(adaptive_pars['theta_f_min'], adaptive_pars['theta_f_max']),
(adaptive_pars['lambda2_min'], adaptive_pars['lambda2_max'])]
})
if noise_parameters_CZ.cluster():
dat = MC.run(
'2D simulation_new_cluster2 double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'2D simulation_new_2 double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='adaptive',
exp_metadata=exp_metadata)
else:
dat = MC.run('2D simulation_new_2',
exp_metadata=exp_metadata,
mode='adaptive')
elif adaptive_pars['mode'] == '1D':
MC.set_sweep_functions([fluxlutman.cz_theta_f])
MC.set_sweep_points(
np.linspace(adaptive_pars['theta_f_min'],
adaptive_pars['theta_f_max'],
adaptive_pars['n_points']))
if noise_parameters_CZ.cluster():
dat = MC.run(
'1D simulation_new_cluster2 double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'1D simulation_new_2 double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
dat = MC.run('1D simulation_new_2',
exp_metadata=exp_metadata,
mode='1D')
elif adaptive_pars['mode'] == 'spectral_tomo':
MC.set_sweep_functions([noise_parameters_CZ.T1_q0])
MC.set_sweep_points(
np.logspace(adaptive_pars['theta_f_min'],
adaptive_pars['theta_f_max'],
adaptive_pars['n_points']))
if noise_parameters_CZ.cluster():
dat = MC.run(
'1D sim_spectral_tomo double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'1D sim_spectral_tomo double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
dat = MC.run('1D sim_spectral_tomo',
exp_metadata=exp_metadata,
mode='1D')
elif adaptive_pars['mode'] == 'spectral_tomo_nonmarkovian':
MC.set_sweep_functions([noise_parameters_CZ.repetitions])
MC.set_sweep_points(np.arange(0, adaptive_pars['n_points'], 1))
if noise_parameters_CZ.cluster():
dat = MC.run(
'1D sim_spectral_tomo_nonmarkovian double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'1D sim_spectral_tomo_nonmarkovian double sided {} - length {:.0f} - distortions {} - T2_scaling {:.1f} - sigma_q1 {:.0f}, sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.distortions(),
noise_parameters_CZ.T2_scaling(),
noise_parameters_CZ.sigma_q1() * 1e6,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
dat = MC.run('1D sim_spectral_tomo_nonmarkovian',
exp_metadata=exp_metadata,
mode='1D')
elif adaptive_pars['mode'] == 'time_series':
MC.set_sweep_functions(
[noise_parameters_CZ.detuning]
) # random sweep function never used in this file. Put it just because I need to put one
MC.set_sweep_points(np.array([-1]))
if noise_parameters_CZ.cluster():
dat = MC.run(
'1D time_series_cluster double sided {} - length {:.0f} - sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
if adaptive_pars['long_name']:
dat = MC.run(
'1D time_series double sided {} - length {:.0f} - sigma_q0 {:.0f}'
.format(fluxlutman.czd_double_sided(),
fluxlutman.cz_length() * 1e9,
noise_parameters_CZ.sigma_q0() * 1e6),
mode='1D',
exp_metadata=exp_metadata)
else:
dat = MC.run('1D time_series',
exp_metadata=exp_metadata,
mode='1D')
fluxlutman.close()
noise_parameters_CZ.close()
MC.close()