def find_amp90_scaling(self,
scales=0.5,
N_steps=[5, 9],
max_n=100,
close_fig=True,
verbose=False,
MC=None,
update=True,
take_fit_I=False):
'''
Finds the scaling factor of pi/2 pulses w.r.t pi pulses using a rabi
type with each pi pulse replaced by 2 pi/2 pulses.
If scales is an array it starts by fitting a cos to a Rabi experiment
to get an initial guess for the amplitude.
This experiment is only useful after carefully calibrating the pi pulse
using flipping sequences.
'''
if MC is None:
MC = self.MC
if np.size(scales) != 1:
self.measure_rabi_amp90(scales=scales, n=1, MC=MC, analyze=False)
a = ma.Rabi_Analysis(close_fig=close_fig)
if take_fit_I:
scale = abs(a.fit_res[0].params['period'].value) / 2
else:
if (a.fit_res[0].params['period'].stderr <=
a.fit_res[1].params['period'].stderr):
scale = abs(a.fit_res[0].params['period'].value) / 2
else:
scale = abs(a.fit_res[1].params['period'].value) / 2
else:
scale = scales
if verbose:
print('Initial scaling factor:', scale, '\n')
for n in N_steps:
if n > max_n:
break
else:
scale_span = 0.3 * scale / n
scales = np.linspace(scale - scale_span, scale + scale_span,
15)
self.measure_rabi_amp90(scales, n=n, MC=MC, analyze=False)
a = ma.Rabi_parabola_analysis(close_fig=close_fig)
if take_fit_I:
scale = a.fit_res[0].params['x0'].value
else:
if (a.fit_res[0].params['x0'].stderr <=
a.fit_res[1].params['x0'].stderr):
scale = a.fit_res[0].params['x0'].value
else:
scale = a.fit_res[1].params['x0'].value
if verbose:
print('Founcaleitude', scale, '\n')
if update:
self.amp90_scale(scale)
print("should be updated")
print(scale)
def find_pulse_amplitude(self,
amps=np.linspace(-.5, .5, 31),
N_steps=[3, 7, 13, 17],
max_n=18,
close_fig=True,
verbose=False,
MC=None,
update=True,
take_fit_I=False):
'''
Finds the pulse-amplitude using a Rabi experiment.
Fine tunes by doing a Rabi around the optimum with an odd
multiple of pulses.
Args:
amps: (array or float) amplitudes of the first Rabi if an array,
if a float is specified it will be treated as an estimate
for the amplitude to be found.
N_steps: (list of int) number of pulses used in the fine tuning
max_n: (int) break of if N> max_n
'''
if MC is None:
MC = self.MC.get_instr()
if np.size(amps) != 1:
ampl = self.calibrate_pulse_amplitude_coarse(amps=amps,
close_fig=close_fig,
verbose=verbose,
MC=MC,
update=update,
take_fit_I=take_fit_I)
else:
ampl = amps
if verbose:
print('Initial Amplitude:', ampl, '\n')
for n in N_steps:
if n > max_n:
break
else:
old_amp = ampl
ampl_span = 0.5 * ampl / n
amps = np.linspace(ampl - ampl_span, ampl + ampl_span, 15)
self.measure_rabi(amps, n=n, MC=MC, analyze=False)
a = ma.Rabi_parabola_analysis(close_fig=close_fig)
# Decide which quadrature to take by comparing the contrast
if take_fit_I:
ampl = a.fit_res[0].params['x0'].value
elif (np.abs(
max(a.measured_values[0]) -
min(a.measured_values[0]))) > (np.abs(
max(a.measured_values[1]) -
min(a.measured_values[1]))):
ampl = a.fit_res[0].params['x0'].value
else:
ampl = a.fit_res[1].params['x0'].value
if not min(amps) < ampl < max(amps):
ampl_span *= 2
amps = np.linspace(old_amp - ampl_span,
old_amp + ampl_span, 15)
self.measure_rabi(amps, n=n, MC=MC, analyze=False)
a = ma.Rabi_parabola_analysis(close_fig=close_fig)
# Decide which quadrature to take by comparing the contrast
if take_fit_I:
ampl = a.fit_res[0].params['x0'].value
elif (np.abs(
max(a.measured_values[0]) -
min(a.measured_values[0]))) > (np.abs(
max(a.measured_values[1]) -
min(a.measured_values[1]))):
ampl = a.fit_res[0].params['x0'].value
else:
ampl = a.fit_res[1].params['x0'].value
if verbose:
print('Found amplitude', ampl, '\n')
if update:
self.Q_amp180.set(ampl)
return ampl
