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qubit.py
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qubit.py
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"""
Created on Mon Oct 4 2022
@author: Evangelos Vlachos <evlachos@usc.edu>
"""
from scipy.signal.windows import gaussian
from scipy.signal import savgol_filter
# from waveform_tools import *
from qm import generate_qua_script
from qm.qua import *
from qm import LoopbackInterface
from qm.QuantumMachinesManager import QuantumMachinesManager
from qm import SimulationConfig
import plot_functions as pf
import os
from datetime import datetime
import csv
import glob
import time
import numpy as np
import json
from qualang_tools.loops import from_array
from qualang_tools.analysis.discriminator import two_state_discriminator
from qm.logger import logger
from Utilities import *
from datetime import date
from pathlib import Path
from helper_functions import save_data
import warnings
import matplotlib.pyplot as plt
from sequence import *
from Instruments import instruments
from config import Configuration
from collections import OrderedDict
logger.setLevel(level='WARNING')
device = 'darpa3A'
today = date.today()
sDate = today.strftime("%Y%m%d")
class qubit():
#%% INITIALIZATION
#%%% default_pars
default_pars = {
# overall setup parameters
'elements' : ['qubit', 'rr'],
"qubit_LO": 5e9,
"rr_LO": 6e9,
'readout_atten': 25, # default attenuation in dB on variable attenuator
#OPX settings and connections
'host': None, # OPX IP address (eventually, import from local file)
'port': '9510', # OPX port (eventually, import from local file)
'Iout': {'qubit': 3, 'rr': 1}, # OPX output (goes to modulation mixer input I)
'Qout': {'qubit': 4, 'rr': 2}, # OPX output (goes to modulation mixer input Q)
'Iin': 1, # OPX input (comes from demodulation mixer output I)
'Qin': 2, # OPX input (comes from demodulation mixer output Q)
'AWG_trigger_out' : 1, # OPX digital marker output port for triggering AWG
'controller': 'con2', # OPX controller name
# Instrument settings (except OPX)
"qubit_LO": int(4.48e9),
"rr_LO": int(6.42e9),
"readout_atten": 25,
# other measurement setup choices
'n_avg': 100, # number of averages for zero-deadtime measurement
'rr_IF': 50e6,
'qubit_IF': 50e6,
"gauss_len": 48,
"gauss_amp": 0.45,
"amp_r": 0.45,
"readout_pulse_len_in_clk": 500, # length of readout integration weights in clock cycles
"saturation_duration" : clk(10e3), # saturation duration for spectroscopy
'readout_length': 2000, # length of a normal readout in ns,
# qubit parameters
"qubit_freq": int(4.5129e9),
"rr_freq": int(6.2e9),
# calibrated setup parameters
"analog_input_offsets": [0,0],
"analog_input_gain": 3,
"rr_mixer_offsets": [0,0],
"qubit_mixer_offsets": [0,0],
"rr_mixer_imbalance": [0,0],
"qubit_mixer_imbalance": [0,0],
"tof": 260, # time of flight in ns
"smearing": 40, # smearing in ns
# processing choices
"n_avg" : 500,
"IQ_rotation": -0/180*np.pi, # phase rotation applied to IQ data
"switch_weights" : False,
# calibrated device parameters
"resettime" : {"qubit": clk(100e3), "rr" : clk(5e3)},
'kappa': 200e3, # resonator linewidth
'readout_freq': 6.2e9, # resonator center frequency
'Q': 9000, # resonator quality factor
'Qc': 9000, # resonator coupling quality factor
# waveform parameters
'operations': dict(qubit=['const','gauss','arb_op','X180','Y180','X90','Y90'],
rr=['const', 'readout']),
'X180_len': 160, # length of pi pulse in clock cycles
'X180_amp': 0.45, # amplitude of pi pulse
'X90_len': 80, # length of pi/2 pulse in clock cycles
'X90_amp': 0.45, # amplitude of pi/2 pulse
'gauss_len': 48, # length of gaussian pulse in clock cycles
'amp_q': 0.375, # amplitude of qubit pulse, needs to be less than 0.45 to prevent overflowing
'amp_r': 0.375, # amplitude of readout pulse, needs to be less than 0.45
'arb_op_len': 160, # length of arbitrary operation in clock cycles
}
#%%% __init__
def __init__(self, qb, initialize_qmm=True):
# load pars from json, OR create new json file
self._name = qb
try:
print('Loading parameter JSON file')
with open(f'{qb}_pars.json', 'r') as openfile:
self.pars = json.load(openfile)
# compare keys
default_keys = set(self.default_pars.keys())
keys = set(self.pars.keys())
# find all the keys in default_pars that are not in pars, and add them to pars w/ default value
for k in (default_keys - keys):
self.add_key(k, self.default_pars[k])
# find all the keys in pars that are not in default_pars, and remove them from pars
for k in (keys - default_keys):
self.remove_key(k)
except FileNotFoundError:
print('Parameter file not found; loading parameters from template')
self.pars = self.default_pars
self.init_quantum_machine(initialize_qmm)
self.write_pars()
self._directory = f'G:\\Shared drives\\CavityCooling\\DARPA\\data\\{self._name}'
self._instruments = instruments()
self.init_instruments()
# self.make_config(self.pars)
self.config_maker = Configuration(self)
self.config = self.config_maker.make_config()
# self.make_config(self.pars)
#%% EXPERIMENTS
#%%% play_pulses
def play_pulses(self, amplitude = 1, element = 'readout'):
"""
DESCRIPTION:
Same as in original code. Plays constant pulses at correct IF frequencies on `element`.
ARGS:
None
KWARGS:
amp (int, 1): Amplitude scaling factor.
RETURNS:
qm (?): ?
"""
with program() as play_pulses:
with infinite_loop_():
play("const" * amp(amplitude), element)
initialized = self.qmm is not None
qmm = self.qmm if initialized else QuantumMachinesManager(host=self.pars['host'], port=self.pars['port'])
qm = qmm.open_qm(self.config)
job = qm.execute(play_pulses)
return qm,job
#%%% punchout
def punchout(self,
df = 0.1e6,
IF_min = 10e6,
IF_max = 20e6,
attenuations = [10,30],
atten_step = 0.1,
savedata=True):
"""
Executes punchout measurement for list of resonator frequencies
Args:
df (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
n_avg (TYPE, optional): DESCRIPTION. Defaults to 500.
atten_range (TYPE, optional): [min,max] attenuation values.
f_LO (TYPE, optional): list with all the resonator frequencies in GHz. Defaults to [6e9,7.2e9].
res_ringdown_time (TYPE, optional): DESCRIPTION. Defaults to int(4e3).
Returns:
None.
"""
iteration = counter(self._directory,self.experiment,element='rr',extension='*.csv')
data = dict(I=[],Q=[],freqs=[],mag=[],z_data=[])
data['attenuations'] = attenuations
for a in tqdm(attenuations):
print(f'Attenuation = {a} dB')
self._instruments.set('DA','attenuation',a)
spec_data,job = self.resonator_spec(f_LO=self.pars['rr_LO'],IF_min=IF_min,IF_max=IF_max,df=df,showprogress=True,savedata=False,fit=False)
data['freqs'].append(np.around(spec_data['freqs']*1e-9,5))
data['I'].append(spec_data['I'])
data['Q'].append(spec_data['Q'])
data['mag'].append(np.abs(spec_data['I']+1j*spec_data['Q']))
data['z_data'].append(spec_data['I']+1j*spec_data['Q'])
self._instruments.set('DA','attenuation',self.pars['readout_atten'])
if savedata:
metadata = dict(timestamp = datetime.now(),
qubit_pars = self.pars,
attenuation_range = attenuation_range,
atten_step = atten_step,)
dataPath = f'{saveDir}\\{self.experiment}\\rr'
data = dict(I=I,Q=Q,freqs=freq_arr,mag=mag)
save_data(dataPath, iteration, metadata, data)
return data, job
#def find_dispersive_shift(self, ):
#%%% run_scan
def run_scan(self,
df = 0.1e6,
element='resonator',
check_mixers=False,
chunksize = 200e6,
lo_min = 6e9,
lo_max = 7e9,
on_off=True,
amp_q_scaling = 1,
saturation_dur = 20e3,
showprogress=False,
savedata=False):
"""
Scans a broad range of frequencies in search for qubits/resonators
Args:
IF_min (TYPE, optional): DESCRIPTION. Defaults 0.1e6.
IF_max (TYPE, optional): DESCRIPTION. Defaults to 400e6.
df (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
n_avg (TYPE, optional): DESCRIPTION. Defaults to 500.
res_ringdown_time (TYPE, optional): DESCRIPTION. Defaults to int(4e3).
Returns:
I (TYPE): DESCRIPTION.
Q (TYPE): DESCRIPTION.
freq_arr: DESCRIPTION.
TYPE: DESCRIPTION.
"""
# today = datetime.today()
# sDate = today.strftime("%Y%m%d")
# saveDir = f'G:\\Shared drives\\CavityCooling\data\\{self.name}\\{sDate}'
# dataPath = f'{saveDir}\\spectroscopy\\{element}_spec'
# filename = 'data'
# iteration = get_index_for_filename(dataPath, filename, file_format='csv')
iteration = counter(self._directory,self.experiment,element=element,extension='*.csv')
freq_arr = []
I = []
Q = []
reports = ''
if lo_min != lo_max:
numchunks = int((lo_max-lo_min)/chunksize) + 1
lo_list = [i*chunksize+lo_min for i in range(numchunks)]
else:
numchunks = 1
lo_list = [lo_min]
for f in tqdm(lo_list):
if element == 'resonator':
data, job = self.resonator_spec(f_LO=f,IF_min=df,IF_max=chunksize,df=df,showprogress=showprogress,savedata=False)
elif element == 'qubit':
data,job = self.qubit_spec(f_LO=f,
amp_q_scaling=amp_q_scaling,
check_mixers=check_mixers,
on_off=on_off,
saturation_dur=saturation_dur,
showprogress=showprogress,
IF_min=df,IF_max=chunksize,df=df,
savedata=False)
# elif element == 'fflqb':
# dataI, dataQ,freqs, job = self.fflt1_spec(spec='qb',f_LO=f,
# IF_min=df,IF_max=chunksize,df=df,
# n_avg=n_avg,
# amp_ffl_scaling=amp_q_scaling,
# check_mixers= check_mixers,showprogress=True,
# savedata=True, flux=flux)
# elif element == 'fflrr':
# dataI, dataQ,freqs, job = self.fflt1_spec(spec='rr',f_LO=f,
# IF_min=df,IF_max=chunksize,df=df,
# n_avg=n_avg,
# amp_ffl_scaling=amp_q_scaling,
# check_mixers= check_mixers,showprogress=True,
# savedata=True, flux=flux)
# elif element == 'diss':
# dataI, dataQ,freqs, job = self.diss_spec(f_LO=f,
# IF_min=df,IF_max=chunksize,df=df,
# n_avg=n_avg,
# amp_ffl_scale=amp_q_scaling,
# check_mixers= check_mixers,
# savedata=False)
freq_arr.extend(data['freqs'])
I.extend(data['I'])
Q.extend(data['Q'])
reports += str(job.execution_report()) + '\n'
if savedata:
dataPath = f'{self._directory}\{self.experiment}\\rr'
data = {"I": I, "Q": Q, "freqs": freq_arr}
save_data(dataPath, iteration, self.pars, data)
data = dict(I=np.array(I),Q=np.array(Q),freqs=np.array(freq_arr))
return data, job
#%%% resonator_spec
def resonator_spec(self, IF_min = 0.1e6,
f_LO = 7e9,
IF_max = 400e6,
df = 0.1e6,
showprogress=False,
savedata=True,
on_off=False,
flux = None,
**kwargs):
"""
Args:
IF_min (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
f_LO (TYPE, optional): DESCRIPTION. Defaults to 7e9.
IF_max (TYPE, optional): DESCRIPTION. Defaults to 400e6.
df (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
atten (TYPE, optional): DESCRIPTION. Defaults to 10.
n_avg (TYPE, optional): DESCRIPTION. Defaults to 500.
res_ringdown_time (TYPE, optional): DESCRIPTION. Defaults to int(4e3).
port_type (TYPE, optional): DESCRIPTION. Defaults to 'notch'.
fit (TYPE, optional): DESCRIPTION. Defaults to True.
plot (TYPE, optional): DESCRIPTION. Defaults to True.
savedata (TYPE, optional): DESCRIPTION. Defaults to True.
Returns:
I (TYPE): DESCRIPTION.
Q (TYPE): DESCRIPTION.
TYPE: DESCRIPTION.
TYPE: DESCRIPTION.
"""
iteration = counter(self._directory,self.experiment,element='rr',extension='*.csv')
freqs = np.arange(IF_min, IF_max + df/2, df, dtype=int)
self.update_value('rr_LO', value = f_LO)
seq = sequence(self,'rr_spec',on_off=on_off,n_avg=self.pars['n_avg'], IF_min=IF_min, IF_max=IF_max, df=df,)
rr_spec = seq.make_resonator_spec_sequence()
datadict,job = self.get_results(rr_spec,result_names=["I","Q"],progress_key='n',showprogress=showprogress)
I = np.array(datadict["I"])
Q = np.array(datadict["Q"])
z_data = I + 1j*Q
freq_arr = np.array(freqs + self.pars['rr_LO'])
data = dict(I=I,Q=Q,z_data=z_data,freqs=freq_arr)
if savedata:
dataPath = f'{self._directory}\{self.experiment}\\rr'
data = {"I": I, "Q": Q, "freqs": freq_arr}
save_data(dataPath, iteration, self.pars, data)
return data, job
def resonator_spec_wffl(self, IF_min = 0.1e6,
f_LO = 7e9,
IF_max = 400e6,
df = 0.1e6,
atten = 10,
n_avg = 500,
port_type = 'notch',
fit=True,
savedata=True,
flux = None,
simulate=True,
**kwargs):
"""
Args:
IF_min (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
f_LO (TYPE, optional): DESCRIPTION. Defaults to 7e9.
IF_max (TYPE, optional): DESCRIPTION. Defaults to 400e6.
df (TYPE, optional): DESCRIPTION. Defaults to 0.1e6.
atten (TYPE, optional): DESCRIPTION. Defaults to 10.
n_avg (TYPE, optional): DESCRIPTION. Defaults to 500.
res_ringdown_time (TYPE, optional): DESCRIPTION. Defaults to int(4e3).
port_type (TYPE, optional): DESCRIPTION. Defaults to 'notch'.
fit (TYPE, optional): DESCRIPTION. Defaults to True.
plot (TYPE, optional): DESCRIPTION. Defaults to True.
savedata (TYPE, optional): DESCRIPTION. Defaults to True.
Returns:
I (TYPE): DESCRIPTION.
Q (TYPE): DESCRIPTION.
TYPE: DESCRIPTION.
TYPE: DESCRIPTION.
"""
try:
list_of_files = glob.glob(r'D:\weak_measurements\spectroscopy\resonator_spec_wffl\*.csv')
latest_file = max(list_of_files, key=os.path.getctime)
iteration = int(latest_file[-7:-4].lstrip('0')) + 1
except:
iteration = 1
freqs = np.arange(IF_min, IF_max + df/2, df, dtype=int)
# freqs_list = freqs.tolist()
# set attenuation and change rr_LO freq
inst.set_attenuator(attenuation=atten)
self.update_value('rr_LO', value = f_LO)
inst.set_rr_LO(self.pars['rr_LO'])
seq = sequence('spec_wffl',n_avg=n_avg, IF_min=IF_min, IF_max=IF_max, df=df,)
rr_spec_wffl = seq.make_sequence(self)
if simulate:
qmm = QuantumMachinesManager(host=host, port=port)
job = qmm.simulate(config=self.config, program=rr_spec_wffl, simulate=SimulationConfig(duration=9000))
job.get_simulated_samples().con1.plot()
datadict,job = self.get_results(rr_spec_wffl,result_names=["I","Q","n"],showprogress=False)
I = np.array(datadict["I"])
Q = np.array(datadict["Q"])
freq_arr = np.array(freqs + self.pars['rr_LO'])
if fit:
fc,fwhm = pf.fit_res(freq_arr,np.abs(I+1j*Q))
print(f'Resonant Frequency: {fc*1e-9:.5f} GHz\nFWHM = {fwhm*1e-6} MHz\nkappa = {2*np.pi*fwhm*1e-6:.3f} MHz')
else:
fc = np.nan
fwhm= np.nan
if 'fc' in kwargs.keys():
fc = kwargs['fc']
pf.spec_plot(freq_arr,I,Q,attenuation=atten,df=df,iteration=iteration,element='resonator',fwhm=fwhm,fc=fc, flux=flux)
exp_dict = {'date/time': datetime.now(),
'nAverages': n_avg,
'w_LO': self.pars['rr_LO'],
'wait_period': self.pars['rr_resettime'],
}
if savedata:
# save data
dataPath = f'{saveDir}\spectroscopy\\resonator_spec'
if not os.path.exists(dataPath):
Path(dataPath).mkdir(parents=True, exist_ok=True)
with open(f"{dataPath}\data_{iteration:03d}.csv","w") as datafile:
writer = csv.writer(datafile)
writer.writerow(exp_dict.keys())
writer.writerow(exp_dict.values())
writer.writerow(freqs)
writer.writerow(I)
writer.writerow(Q)
return I, Q, freqs+self.pars['rr_LO'], job;
#%%% qubit_spec
def qubit_spec(self,
f_LO = 5e9,
IF_min = 0.1e6, # min IF frequency
IF_max = 400e6, # max IF frequency
check_mixers=False,
df = 0.1e6, # IF frequency step
amp_q_scaling = 0.1, # prefactor to scale default "const" qubit tone, amp_q
saturation_dur = int(10e3), # time qubit saturated w/ qubit tone, in ns
on_off = True, # background subtraction
notify = False,
showprogress=False,
savedata=True,
**kwargs): # create notification on Slack when measurement finishes
iteration = counter(self._directory,self.experiment,element='qubit',extension='*.csv')
freq_arr = np.arange(IF_min, IF_max + df/2, df, dtype=int)
saturation_dur = int(saturation_dur)
self.update_value('qubit_LO',value = f_LO)
if check_mixers:
self.opt_lo_leakage(mode='coarse',element='qubit',sa_span=0.5e6,threshold=-30,plot=True)
self.update_value('qubit_IF',50e6)
self.opt_sideband(mode='coarse',element='qubit',sa_span=0.5e6,threshold=-20,plot=True)
self.opt_lo_leakage(mode='coarse',element='rr',sa_span=0.5e6,threshold=-30,plot=True)
self.update_value('rr_IF',50e6)
self.opt_sideband(mode='coarse',element='rr',sa_span=0.5e6,threshold=-20,plot=True)
# prog = self.make_sequence(self,on_off=on_off,saturation_dur=saturation_dur,amp_q_scaling=amp_q_scaling, IF_min=IF_min, IF_max=IF_max, df=df,)
seq = sequence(self,name='qubit_spec',on_off=on_off,saturation_dur=saturation_dur,amp_q_scaling=amp_q_scaling, IF_min=IF_min, IF_max=IF_max, df=df,)
prog = seq.make_qubit_spec_sequence()
# execute 'QubitSpecProg' using configuration settings in 'config'
# fetch averaged I and Q values that were saved
datadict, job = self.get_results(jobtype = prog, result_names = ["I", "Q"], showprogress=showprogress, notify = notify)
# print('qubit power')
# qb_power = self.get_power(sa,freq=self.pars['qubit_LO']+self.pars['qubit_IF'],reference=0,amp_q = amp_q_scaling, span=1e6,config=True,output=False)
# print('rr power')
# rr_power = self.get_power(sa,freq=self.pars['rr_LO']+self.pars['rr_IF'],reference=0,span=1e6,config=True,output=False)
I = np.array(datadict["I"])
Q = np.array(datadict["Q"])
freq_arr = np.array(freq_arr + self.pars['qubit_LO'])
data = dict(I=I,Q=Q,freqs=freq_arr)
# print(f'Qubit Frequency: {fc*1e-9:.5f} GHz\nFWHM = {fwhm*1e-6} MHz\nkappa = {2*np.pi*fwhm*1e-6:.3f} MHz')
if savedata:
exp_dict = {
'n_avg': n_avg,
'amp_r_scale': amp_r_scaling,
'amp_r_scale': amp_q_scaling ,
'rr_atten': inst.get_attenuation(),
'qubit_LO': self.pars['qubit_LO'],
}
# save data
dataPath = f'{saveDir}\\spectroscopy\qubit_spec'
if not os.path.exists(dataPath):
Path(dataPath).mkdir(parents=True, exist_ok=True)
if 'saveto' in kwargs:
dataDict = {'metadata': exp_dict,
'time': freq_arr,
'I': I,
'Q': Q,
}
file = kwargs.get('saveto')['file']
name = kwargs.get('saveto')['name']
save_datadict_to_fgroup(file,name , dataDict)
else:
with open(f"{dataPath}\data_{iteration:03d}.csv","w") as datafile:
writer = csv.writer(datafile)
writer.writerow(exp_dict.keys())
writer.writerow(exp_dict.values())
writer.writerow(freq_arr)
writer.writerow(I)
writer.writerow(Q)
return data, job;
def fflt1_spec(self,
sa = 0,
f_LO = 5e9,
IF_min = 0.1e6, # min IF frequency
IF_max = 400e6, # max IF frequency
check_mixers=False,
df = 0.1e6, # IF frequency step
rr_freq = 6e9, #resonator frequency
amp_r_scaling = 1,
amp_q_scaling = 0.1, # prefactor to scale default "const" qubit tone, amp_q
n_avg = 500, # number of averages
atten=10, # readout attenuation
saturation_dur = int(10e3), # time qubit saturated w/ qubit tone, in ns
resettime = int(40e3), # wait time between experiments, in ns
on_off = True, # background subtraction
notify = False,
showprogress=False,
savedata=True,
amp_cav_scaling=0.01,
amp_ffl_scaling=0.01,
flux=0,
spec='rr',
**kwargs): # create notification on Slack when measurement finishes
try:
list_of_files = glob.glob(f'{saveDir}\spectroscopy\qubit_spec\*.csv')
latest_file = max(list_of_files, key=os.path.getctime)
iteration = int(latest_file[-7:-4].lstrip('0')) + 1
except:
iteration = 1
freq_arr = np.arange(IF_min, IF_max + df/2, df, dtype=int)
saturation_dur = int(saturation_dur)
inst.set_attenuator(attenuation=self.pars['rr_atten'])
self.update_value('ffl_LO',value = f_LO)
inst.set_ffl_LO(f_LO)
inst.set_rr_LO(self.pars['rr_LO'])
self.check_mix_cal(sa,check=check_mixers,amp_q = amp_q_scaling, threshold = - 60)
if spec=='qb':
prog = self.make_sequence(exp='fflqb-spec',on_off=on_off,saturation_dur=saturation_dur,var_arr=freq_arr,n_avg=n_avg,amp_q_scaling=amp_q_scaling,amp_r_scale=amp_r_scaling,amp_cav_scaling=amp_cav_scaling,amp_ffl_scaling=amp_ffl_scaling)
else:
prog = self.make_sequence(exp='fflrr-spec',on_off=on_off,saturation_dur=saturation_dur,var_arr=freq_arr,n_avg=n_avg,amp_q_scaling=amp_q_scaling,amp_r_scaling=amp_r_scaling,amp_cav_scaling=amp_cav_scaling,amp_ffl_scaling=amp_ffl_scaling)
# execute 'QubitSpecProg' using configuration settings in 'config'
# fetch averaged I and Q values that were saved
datadict, job = self.get_results(jobtype = prog, result_names = ["I", "Q"], n_total=n_avg,showprogress=showprogress, notify = notify)
# print('qubit power')
# qb_power = self.get_power(sa,freq=self.pars['qubit_LO']+self.pars['qubit_IF'],reference=0,amp_q = amp_q_scaling, span=1e6,config=True,output=False)
# print('rr power')
# rr_power = self.get_power(sa,freq=self.pars['rr_LO']+self.pars['rr_IF'],reference=0,span=1e6,config=True,output=False)
I = np.array(datadict["I"])
Q = np.array(datadict["Q"])
freq_arr = np.array(freq_arr + self.pars['ffl_LO'])
pf.spec_plot(freq_arr,I,Q,iteration=iteration,element='ffl',rrFreq=self.pars['rr_freq'],find_peaks=True, amp_q_scaling=amp_q_scaling, amp_cav_scaling=amp_cav_scaling, amp_ffl_scaling=amp_ffl_scaling, attenuation=self.pars['ffl_atten'],df=df, flux=flux)
# print(f'Qubit Frequency: {fc*1e-9:.5f} GHz\nFWHM = {fwhm*1e-6} MHz\nkappa = {2*np.pi*fwhm*1e-6:.3f} MHz')
if savedata:
exp_dict = {
'n_avg': n_avg,
'amp_r_scale': amp_r_scaling,
'amp_r_scale': amp_q_scaling ,
'rr_atten': inst.get_attenuation(),
'qubit_LO': self.pars['qubit_LO'],
'ffl_LO':self.pars['ffl_LO']
}
# save data
dataPath = f'{saveDir}\\spectroscopy\qubit_spec'
if not os.path.exists(dataPath):
Path(dataPath).mkdir(parents=True, exist_ok=True)
if 'saveto' in kwargs:
dataDict = {'metadata': exp_dict,
'time': freq_arr,
'I': I,
'Q': Q,
}
file = kwargs.get('saveto')['file']
name = kwargs.get('saveto')['name']
save_datadict_to_fgroup(file,name , dataDict)
else:
with open(f"{dataPath}\data_{iteration:03d}.csv","w") as datafile:
writer = csv.writer(datafile)
writer.writerow(exp_dict.keys())
writer.writerow(exp_dict.values())
writer.writerow(freq_arr)
writer.writerow(I)
writer.writerow(Q)
return I, Q, freq_arr, job;
#%%% power_rabi
def power_rabi(self,sa = 0,
a_min = 0.01, # minimum amp_q scaling
a_max = 1, # maximum amp_q scaling
da = 0.005, # step of amp_q
check_mixers = True,
pulse = 'pi',
n_avg = 2000, # number of averages
fit = True,
plot = True,
detuning = 0e6):
amps = np.arange(a_min, a_max + da/2, da)
inst.set_attenuator(attenuation=self.pars['rr_atten'])
# self.check_mix_cal(sa, check = check_mixers, threshold = -55)
prog = self.make_sequence(exp = 'p-rabi', pulse = pulse,
var_arr = amps,
detuning = detuning,
n_avg = n_avg)
qmm = QuantumMachinesManager(host=host, port=port)
job = qmm.simulate(config=self.config, program=prog, simulate=SimulationConfig(duration=3000))
job.get_simulated_samples().con1.plot()
datadict, job = self.get_results(jobtype = prog, result_names = ['I','Q'], showprogress = True, progress_key = 'n', n_total = n_avg)
I = datadict['I']
Q = datadict['Q']
if fit:
fitted_pars,error = pf.fit_data(amps,np.abs(I+1j*Q),sequence='p-rabi',dt=amps[-1]/len(amps),fitFunc='rabi')
if plot:
pf.plot_data(x_vector=amps, y_vector=np.abs(I+1j*Q),sequence='p-rabi',fitted_pars=fitted_pars,
qubitDriveFreq=self.pars['qubit_LO']+self.pars['qubit_IF']+detuning,savefig=False,nAverages=n_avg)
'''Update pulse amplitude'''
A = fitted_pars[1] #self.pars['pi_amp'] * fitted_pars[1]
return amps, I, Q, job, A, fitted_pars
#%%% single_shot
def single_shot(self,
n_reps = 1000,
liveplot = False,
numSamples = 1000):
inst.set_attenuator(attenuation=self.pars['rr_atten'])
prog = self.make_sequence(exp='ss', n_reps = n_reps)
datadict, job = self.get_results(jobtype = prog,result_names=['n', 'I','Q','Iexc','Qexc'], showprogress=True, liveplot = liveplot)
if liveplot == False:
plot, ax = pf.init_IQ_plot()
pf.plot_single_shot(datadict,axes=ax)
return datadict, job, prog
#%%% make_sequence
def make_sequence(self,exp='rabi',IFmin=200e6, IFmax=204e6, df=50e3,var_arr=0,detuning=0,n_avg=0,amp_q_scaling=1,amp_r_scale=1,numPeriods=2,nIterations=1,n_reps=100,on_off=True,saturation_dur=int(10e3),pulse='pi', play_init_pi=True, amp_cav_scaling=0.01, amp_ffl_scaling=0.01):
if exp == 'qubit-spec':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
n, f, I, Q = self.declare_vars([int, int, fixed, fixed])
update_frequency("rr", self.pars['rr_IF'])
I_stream, Q_stream, n_stream = self.declare_streams(stream_num=3)
if on_off:
I_b, Q_b, I_tot,Q_tot = self.declare_vars([fixed,fixed, fixed, fixed])
# loop over n_avg iterations
with for_(n, 0, n < n_avg, n + 1):
save(n,n_stream)
# loop over list of IF frequencies
with for_each_(f,var_arr): #with for_(*from_array(f,var_arr)):
# update IF frequency going into qubit mixer
update_frequency("qubit", f)
# measure background
if on_off:
measure("readout"*amp(amp_r_scaling), "rr", None, *self.res_demod(I_b, Q_b))
wait(clk(self.pars['rr_resettime']), "rr")
align("rr", "qubit") # wait for operations on resonator to finish before playing qubit pulse
# play qubit pulse and measure
play("gauss" * amp(amp_q_scaling), "qubit", duration = clk(saturation_dur))
#play('pi','qubit')
play("const"*amp(amp_cav_scaling), "rr", duration=clk(saturation_dur))
play('gaussian'*amp(amp_ffl_scaling), "ffl", duration=clk(saturation_dur))
align("qubit", "rr") # wait for operations on resonator to finish before playing qubit pulse
measure("readout"*amp(amp_r_scaling), "rr", None, *self.res_demod(I, Q))
# subtract background and save to stream
if on_off:
assign(I_tot, I - I_b)
assign(Q_tot, Q - Q_b)
save(I_tot, I_stream)
save(Q_tot, Q_stream)
else:
save(I, I_stream)
save(Q, Q_stream)
# wait some time before continuing to next IF frequency
wait(resettime_clk, "rr")
# average data over iterations and save to stream
with stream_processing():
I_stream.buffer(len(var_arr)).average().save('I')
Q_stream.buffer(len(var_arr)).average().save('Q')
n_stream.save('n')
if exp == 'fflrr-spec':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
n, f, I, Q = self.declare_vars([int, int, fixed, fixed])
update_frequency("rr", self.pars['rr_IF'])
I_stream, Q_stream, n_stream = self.declare_streams(stream_num=3)
update_frequency("qubit", self.pars['qubit_IF'])
#update_frequency("ffl", self.pars['ffl_IF'])
# loop over n_avg iterations
with for_(n, 0, n < n_avg, n + 1):
save(n,n_stream)
# loop over list of IF frequencies
with for_each_(f,var_arr): #with for_(*from_array(f,var_arr)):
# update IF frequency going into qubit mixer
update_frequency("ffl", f)
# measure background
#update_frequency("rr", f)
play("readout", "rr")
align("ffl", "rr")
play('gaussian'*amp(amp_ffl_scaling), "ffl", duration=clk(200))
align("ffl", "rr")
measure("void", "rr", None,*self.res_demod(I,Q))
wait(resettime_clk, "rr")
save(I, I_stream)
save(Q, Q_stream)
# average data over iterations and save to stream
with stream_processing():
I_stream.buffer(len(var_arr)).average().save('I')
Q_stream.buffer(len(var_arr)).average().save('Q')
n_stream.save('n')
if exp == 'fflqb-spec':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
n, f, I, Q = self.declare_vars([int, int, fixed, fixed])
update_frequency("rr", self.pars['rr_IF'])
I_stream, Q_stream, n_stream = self.declare_streams(stream_num=3)
update_frequency("qubit", self.pars['qubit_IF'])
#update_frequency("ffl", self.pars['ffl_IF'])
# loop over n_avg iterations
with for_(n, 0, n < n_avg, n + 1):
save(n,n_stream)
# loop over list of IF frequencies
with for_each_(f,var_arr): #with for_(*from_array(f,var_arr)):
# update IF frequency going into qubit mixer
update_frequency("ffl", f)
play('pi','qubit')
align('ffl','qubit')
wait(clk(60),'ffl')
play('gaussian'*amp(amp_ffl_scaling), "ffl", duration=clk(10000))
wait(clk(30))
align("ffl","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
# subtract background and save to stream
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk, "qubit")
# wait some time before continuing to next IF frequency
# average data over iterations and save to stream
with stream_processing():
I_stream.buffer(len(var_arr)).average().save('I')
Q_stream.buffer(len(var_arr)).average().save('Q')
n_stream.save('n')
if exp == 'rabi':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
n, t, I, Q = self.declare_vars([int, int, fixed, fixed])
I_stream, Q_stream, n_stream = self.declare_streams(stream_num=3)
update_frequency("rr", self.pars['rr_IF'])
update_frequency('qubit', (self.pars['qubit_freq']-self.pars['qubit_LO']) + detuning) # sets the IF frequency of the qubit
with for_(n, 0, n < n_avg, n + 1):
save(n, n_stream)
with for_each_(t,var_arr):
with if_(t==0):
measure("readout", "rr", None, *self.res_demod(I, Q))
# save(t,t_stream)
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk,"qubit")
with else_():
play("pi" * amp(amp_q_scaling), "qubit", duration=t)
align("qubit", "rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
# save(t,t_stream)
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk,"qubit")
with stream_processing():
I_stream.buffer(len(var_arr)).average().save("I")
Q_stream.buffer(len(var_arr)).average().save("Q")
n_stream.save('n')
elif exp == 'p-rabi':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
a, n, I, Q = self.declare_vars([fixed, int, fixed, fixed])
I_stream, Q_stream, n_stream = self.declare_streams(stream_num=3)
update_frequency("rr", self.pars['rr_IF'])
update_frequency('qubit', (self.pars['qubit_freq']-self.pars['qubit_LO']) + detuning) # sets the IF frequency of the qubit
with for_(n, 0, n < n_avg, n + 1):
save(n, n_stream)
with for_each_(a,var_arr): # Sweep pulse duration
play('const'*amp(0.2), "ffl", duration=25)
wait(10,"qubit")
play(pulse * amp(a), "qubit")
#align("qubit", "rr")
align("ffl","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk)
with stream_processing():
I_stream.buffer(len(var_arr)).average().save("I")
Q_stream.buffer(len(var_arr)).average().save("Q")
n_stream.save("n")
elif exp == 'ramsey':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
update_frequency("rr", self.pars['rr_IF'])
update_frequency('qubit', (self.pars['qubit_freq']-self.pars['qubit_LO']) + detuning)
n, t, I, Q = self.declare_vars([int, int, fixed, fixed])
I_stream,Q_stream,n_stream = self.declare_streams(stream_num=3)
with for_(n, 0, n < n_avg, n + 1):
save(n, n_stream)
with for_each_(t,var_arr):
with if_(t==0):
play("pi_half", "qubit")
play("pi_half", "qubit")
align("qubit","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk, "qubit")
with else_():
play("pi_half", "qubit")
wait(t, "qubit")
# frame_rotation_2pi(phi, 'qubit') # this was in Haimeng's code and was commented out by her,
play("pi_half", "qubit")
align("qubit","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk, "qubit")
with stream_processing():
I_stream.buffer(len(var_arr)).average().save("I")
Q_stream.buffer(len(var_arr)).average().save("Q")
n_stream.save("n")
elif exp == 'ramsey_chi':
resettime_clk= clk(self.pars['qubit_resettime'])
with program() as prog:
update_frequency("rr", self.pars['rr_IF'])
update_frequency('qubit', (self.pars['qubit_freq']-self.pars['qubit_LO']) + detuning)
n, t, I, Q = self.declare_vars([int, int, fixed, fixed])
I_stream,Q_stream,n_stream = self.declare_streams(stream_num=3)
with for_(n, 0, n < n_avg, n + 1):
save(n, n_stream)
with for_each_(t,var_arr):
with if_(t==0):
play("pi_half", "qubit")
play("pi_half", "qubit")
align("qubit","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk, "qubit")
with else_():
play("pi_half", "qubit")
align('qubit','rr')
play("readout"*amp(amp_r_scale), "rr",duration=t)
align('rr','qubit')
# frame_rotation_2pi(phi, 'qubit') # this was in Haimeng's code and was commented out by her,
play("pi_half", "qubit")
align("qubit","rr")
measure("readout", "rr", None, *self.res_demod(I, Q))
save(I, I_stream)
save(Q, Q_stream)
wait(resettime_clk, "qubit")
with stream_processing():
I_stream.buffer(len(var_arr)).average().save("I")
Q_stream.buffer(len(var_arr)).average().save("Q")
n_stream.save("n")
elif exp == 'echo':