def simulate_qasm_files(qasm_filenames,
config_filename,
qxc,
MC,
error_rate=0,
nr_averages=int(1e4)):
"""
Takes one or more asm_files as input and runs them on the hardware
"""
if len(qasm_filenames) > 1:
MC.soft_avg(len(qasm_filenames))
nr_hard_averages = 256
else:
nr_hard_averages = nr_averages
MC.soft_avg(1)
qx_det = det.QX_Hard_Detector(qxc,
qasm_filenames,
p_error=error_rate,
num_avg=nr_hard_averages)
measurement_points = extract_msmt_pts_from_config(config_filename)
MC.set_sweep_function(swf.None_Sweep())
MC.set_detector_function(qx_det)
MC.set_sweep_points(measurement_points)
MC.run('Demo QX sim {}'.format(os.path.split(qasm_filenames[0])[-1]))
def measure_asm_files(asm_filenames, config_filename, qubit, MC):
"""
Takes one or more asm_files as input and runs them on the hardware
"""
qubit.prepare_for_timedomain()
CBox = qubit.CBox # The qubit object contains a reference to the CBox
counter_param = ManualParameter('name_ctr', initial_value=0)
if len(asm_filenames) > 1:
MC.soft_avg(len(asm_filenames))
nr_hard_averages = 256
else:
MC.soft_avg(8)
nr_hard_averages = qubit.RO_acq_averages() // MC.soft_avg()
if qubit.cal_pt_zero() is not None:
cal_pts = (qubit.cal_pt_zero(), qubit.cal_pt_one())
else:
cal_pts = None
prepare_function_kwargs = {
'counter_param': counter_param,
'asm_filenames': asm_filenames,
'CBox': CBox
}
detector = CBox_int_avg_func_prep_det_CC(
CBox,
prepare_function=load_range_of_asm_files,
prepare_function_kwargs=prepare_function_kwargs,
nr_averages=nr_hard_averages,
cal_pts=cal_pts)
measurement_points = extract_msmt_pts_from_config(config_filename)
s = swf.None_Sweep()
if 'rb' in asm_filenames[0]:
s.parameter_name = 'Number of Cliffords'
s.unit = '#'
MC.set_sweep_function(s)
MC.set_sweep_points(measurement_points)
MC.set_detector_function(detector)
MC.run('Demo {}'.format(os.path.split(asm_filenames[0])[-1]))
if 'rb' in asm_filenames[0]:
a = ma.RandomizedBenchmarking_Analysis(label='Demo rb',
rotate_and_normalize=False,
close_fig=True)
p = a.fit_res.best_values['p']
Fcl = p + (1 - p) / 2
Fg = Fcl**(1 / 1.875)
print('Clifford Fidelity:\t{:.4f}\nGate Fidelity: \t\t{:.4f}'.format(
Fcl, Fg))
Ncl = np.arange(a.sweep_points[0], a.sweep_points[-1])
RB_fit = ma.fit_mods.RandomizedBenchmarkingDecay(
Ncl, **a.fit_res.best_values)
MC.main_QtPlot.add(x=Ncl, y=RB_fit, subplot=0)
def _simulate_quantumsim(file_path, options):
st = station.Station()
# Connect to the qx simulator
MC_sim = measurement_control.MeasurementControl('MC_sim',
live_plot_enabled=False,
verbose=True)
datadir = os.path.abspath(
os.path.join(os.path.dirname(pq.__file__), os.pardir, 'execute_data'))
MC_sim.datadir(datadir)
MC_sim.station = st
st.add_component(MC_sim)
quantumsim_sweep = swf.None_Sweep()
quantumsim_sweep.parameter_name = 'Circuit number '
quantumsim_sweep.unit = '#'
qubit_parameters = {
'Q0': {
'T1': 30e3,
'T2': 17e3,
'frac1_0': 0.0189,
'frac1_1': 0.918
},
'Q1': {
'T1': 30e3,
'T2': 17e3,
'frac1_0': 0.068,
'frac1_1': 0.949
},
'q0': {
'T1': 30e3,
'T2': 17e3,
'frac1_0': 0.0189,
'frac1_1': 0.918
},
'q1': {
'T1': 30e3,
'T2': 17e3,
'frac1_0': 0.068,
'frac1_1': 0.949
}
}
quantumsim_det = Quantumsim_Two_QB_Hard_Detector(
file_path, dt=(40, 280), qubit_parameters=qubit_parameters)
sweep_points = range(len(quantumsim_det.parser.circuits))
MC_sim.set_detector_function(quantumsim_det)
MC_sim.set_sweep_function(quantumsim_sweep)
MC_sim.set_sweep_points(sweep_points)
dat = MC_sim.run("run QASM")
print('simulation finished')
return dat
def fake_folder(self, folder_name):
'''
Give folder_name in the form of a string.
Creates a folder with the desired folder name containing a dummy measurement.
'''
if isinstance(folder_name, str):
sweep_pts = np.linspace(0, 10, 3)
self.MC.set_sweep_function(swf.None_Sweep())
self.MC.set_sweep_points(sweep_pts)
self.MC.set_detector_function(det.Dummy_Detector_Soft())
dat = self.MC.run(folder_name)
else:
raise ValueError('Please enter a string as the folder name!')
def fake_folder(self, folder_name):
'''
Give folder_name in the form of a string.
Create a fake folder in the nanowire experiments folder with the desired folder name.
This is usefull to use when magnetic fields change or when you start a new measurement cycle.
Such that you can distinguish the different measurement sets.
'''
if isinstance(folder_name, str):
sweep_pts = np.linspace(0, 10, 3)
self.MC.set_sweep_function(swf.None_Sweep())
self.MC.set_sweep_points(sweep_pts)
self.MC.set_detector_function(det.Dummy_Detector_Soft())
dat = self.MC.run(folder_name)
else:
raise ValueError('Please enter a string as the folder name!')
def measure_discrimination_fid(self,
no_fits=False,
return_detector=False,
MC=None,
analyze=True,
close_fig=True,
make_fig=True,
verbose=True):
'''
Measures the single shot discrimination fidelity.
Uses whatever sequence is currently loaded and takes 8000 single shots
Constructs histograms based on those and uses it to extract the
single-shot discrimination fidelity.
'''
self.prepare_for_timedomain()
if MC is None:
MC = self.MC
# If I return the detector to use it must do analysis internally
# Otherwise I do it here in the qubit object so that I can pass args
analysis_in_det = return_detector
d = cdet.CBox_SSRO_discrimination_detector(
'SSRO-disc' + self.msmt_suffix,
analyze=analysis_in_det,
MC=MC,
AWG=self.AWG,
CBox=self.CBox,
sequence_swf=swf.None_Sweep(sweep_control='hard',
sweep_points=np.arange(10)))
if return_detector:
return d
d.prepare()
discr_vals = d.acquire_data_point()
if analyze:
current_threshold = self.CBox.sig0_threshold_line.get()
a = ma.SSRO_discrimination_analysis(
label='SSRO-disc' + self.msmt_suffix,
current_threshold=current_threshold,
close_fig=close_fig,
plot_2D_histograms=make_fig)
return (a.F_discr_curr_t * 100, a.F_discr * 100, a.theta,
a.opt_I_threshold, a.relative_separation,
a.relative_separation_I)
return discr_vals
def _simulate_quantumsim(file_path, options):
quantumsim_sweep = swf.None_Sweep()
quantumsim_sweep.parameter_name = 'Starmon number '
quantumsim_sweep.unit = '#'
qubit_parameters = {
'Q0': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.0189, 'frac1_1': 0.918},
'Q1': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.068, 'frac1_1': 0.949},
'q0': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.0189, 'frac1_1': 0.918},
'q1': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.068, 'frac1_1': 0.949}}
quantumsim_det = Quantumsim_Two_QB_Hard_Detector(
file_path, dt=(40, 280), qubit_parameters=qubit_parameters)
sweep_points = range(len(quantumsim_det.parser.circuits))
MC_demo.set_detector_function(quantumsim_det)
MC_demo.set_sweep_function(quantumsim_sweep)
MC_demo.set_sweep_points(sweep_points)
dat = MC_demo.run("run QASM")
print('simulation execute_Starmon finished')
return dat
def _simulate_quantumsim(file_path, options):
quantumsim_sweep = swf.None_Sweep()
quantumsim_sweep.parameter_name = 'Circuit number '
quantumsim_sweep.unit = '#'
qubit_parameters = {
'Q0': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.0189, 'frac1_1': 0.918},
'Q1': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.068, 'frac1_1': 0.949},
'q0': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.0189, 'frac1_1': 0.918},
'q1': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.068, 'frac1_1': 0.949},
'default': {'T1': 30e3, 'T2': 17e3, 'frac1_0': 0.068, 'frac1_1': 0.949}}
quantumsim_det = Quantumsim_Two_QB_Hard_Detector(
file_path, timegrid=20, gate_1_step=1, gate_2_step=5,
qubit_parameters=qubit_parameters)
sweep_points = range(len(quantumsim_det.parser.circuits))
MC.set_detector_function(quantumsim_det)
MC.set_sweep_function(quantumsim_sweep)
MC.set_sweep_points(sweep_points)
dat = MC.run("run QASM")
print('simulation finished')
return _MC_result_to_chart_dict(dat)
