def __init__(self,
connection_info,
caching_allowed=True,
caching_permissions={},
auto_open=True):
super(QuantumMachine, self).__init__(connection_info, caching_allowed,
caching_permissions, auto_open)
self.connection_info = connection_info
port = ""
if connection_info[
"gateway_port"] and connection_info["gateway_port"] != "":
port = connection_info["gateway_port"]
ip = ""
if connection_info[
"gateway_ip"] and connection_info["gateway_ip"] != "":
ip = connection_info["gateway_ip"]
if ip != "" and port != "":
self.qmm = QuantumMachinesManager(host=ip, port=port)
else:
self.qmm = QuantumMachinesManager()
self.qmObj = None
self.job = None
def __init__(self):
self.qmm = QuantumMachinesManager()
self.qm = None
self.config = None
# Qubit
self.qubit_freq = 1e9
self.QB_IF_freq = 80.0e6
self.QB_LO_freq = self.qubit_freq - self.QB_IF_freq # for the mixer
self.saturation_pulse_len = 50000
self.pi_pulse_len = 48
self.pi_wf = pulses.gauss(0.49, 0.0, 10.0, self.pi_pulse_len)
self.pi2_wf = self.pi_wf / 2
# Readout
self.res_freq = 7e9
self.RR_IF_freq = 62.5e6
self.RR_LO_up_freq = self.res_freq + self.RR_IF_freq
self.RR_LO_down_freq = self.res_freq - self.RR_IF_freq
self.wait_sig = 160 # minimum 32 clock cycles
self.smearing_sig = 0 # multiple period
self.wait_ref = 53 # minimum 32 clock cycles
self.smearing_ref = 0
self.readout_pulse_len = 512 # multiple of 16
self.readout_wf = pulses.const(0.49, self.readout_pulse_len)
self.theta = 0.0 # rotation of IQ plane
# create config and load it
self.load_config()
def optimize():
# Connects to the quantum machine through the network
qmManager = QuantumMachinesManager(host='132.77.48.245')
# Initial guess for the best offsets
offsets = [-0.10059273, 0.1322426]
DC_I = offsets[0]
DC_Q = offsets[1]
# Initializes the instrument
inst = setinstrument(7e9 - 25e6, 250e3)
try:
# Using fmin function to find the best offsets to minimize the leakage
xopt = fmin(power, 0.2, (qmManager, inst), maxiter=10)
# If there's an error trying to use the "power" function
finally:
print(
"An error has occurred trying to find to minimum of the power function"
)
inst.close() # Closes the spectrum analyzer
print(str(xopt) + " ==> " +
str(power(xopt, qmManager, inst))) # Print the results
# inst.span(2e8)
# scanfreqamp(inst, 20, True)
inst.close() # Closes the instrument
def __init__(self, connection_info):
self.connection_info = connection_info
port = ""
if self.connection_info[
"gateway_port"] is not None and self.connection_info[
"gateway_port"] != "":
port = self.connection_info["gateway_port"]
ip = ""
if self.connection_info[
"gateway_ip"] is not None and self.connection_info[
"gateway_ip"] != "":
ip = self.connection_info["gateway_ip"]
if ip != "" and port != "":
self.qmm = QuantumMachinesManager(host=ip, port=port)
else:
self.qmm = QuantumMachinesManager()
self.qmObj = None
self.job = None
def _connect(self) -> QuantumMachinesManager:
return QuantumMachinesManager()
def optimize():
"""
This function optimizes the DC offsets of the quantum controller to minimize the leakage at 7GHz
:return: Prints the ideal values of the DC offsets and correction matrix variables
"""
# Connects to the quantum machine through the network
qmManager = QuantumMachinesManager(host='132.77.48.245')
# Initial guess for the best offsets
# Initial guess for correction variables corvars = [0, 1]
offsets = [-0.08712208, 0.02583852]
DC_I = offsets[0]
DC_Q = offsets[1]
# correction = calc_cmat(corvars[0], corvars[1])
# Searching parameters, range of parameters for brute force, num of step in the brute force and max iteration fmin
# OFFSETS
nstepbruteoffset = 20 # Num of steps in the initial brute force stage(N^2)
# Range to look in the inital brute force stage
rangebruteoffset = [(-0.1, 0.1), (-0.1, 0.1)]
maxiterfminoffset = 100 # maximum number of iteration in the fmin stage
# CORRECTION VARIABLES
# Num of steps in the initial brute force stage(N^2)
nstepbrutecorvars = 20
# Range to look in the inital brute force stage
rangebrutecorvars = [(-0.3, 0.3), (0.6, 1.3)]
maxiterfmincorvars = 100 # maximum number of iteration in the fmin stage
# Try to initialize the spectrum analyzer
try:
# Initializes the instrument
inst = setinstrument(7e9, 25e5)
except Exception as e:
print(
"An error has occurred trying to initialize the instrument.\nThe Error:\n",
e)
exit()
try:
# The program that will run on the quantum machine
with program() as prog:
with infinite_loop_():
play('pulse1', 'qe1')
# The configuration of the quantum program, this is a dictionary, see documentation
config = setconf(DC_I, DC_Q, correction)
# Open quantum machine from configuration and force execute it
qm1 = qmManager.open_qm(config)
job = qm1.execute(prog, forceExecution=True)
offsets = brute(power,
rangebruteoffset,
args=(corvars, qm1, inst, 'offset'),
Ns=nstepbruteoffset,
finish=None)
print('\n Initial guess for the offsets [DC_I, DC_Q]: ', offsets,
"\n\n")
# Using fmin function to find the best offsets to minimize the leakage
xopt = fmin(power,
offsets, (corvars, qm1, inst, 'offset'),
maxiter=maxiterfminoffset)
# If there's an error trying to use the "power" function
except Exception as e:
print("An error has occurred in the 'power' function. \nThe Error:\n",
e)
inst.close() # Closes the spectrum analyzer
exit()
# Redefine the offsets to the values we found
offsets = xopt
print("\nOptimal offsets [DC_I, DC_Q]: " + str(offsets) + "\n\n")
# Define the spectrum analyzer frequency to the left spike frequency
inst.frequency(7e9 - 25e6)
try:
corvars = brute(powerdiffargs,
rangebrutecorvars,
args=(offsets, qm1, inst),
Ns=nstepbrutecorvars,
finish=None)
correction = calc_cmat(corvars[0], corvars[1])
print("\n Initial guess from brute force [th, k]: " + str(corvars) +
"\n\n")
xopt = fmin(powerdiffargs,
corvars, (offsets, qm1, inst),
maxiter=maxiterfmincorvars)
except Exception as e:
print(
"An error has occurred trying to find optimal correction matrix.\nThe Error:\n",
e)
exit()
corvars = xopt
print("\nOptimal correction variables [theta, k]: " + str(corvars))
inst.close() # Closes the instrument
print(
"\n--------------------------------------------------------------------------------\n\n"
"Final results: \n"
"Offsets [DC_I, DC_Q]: " + str(offsets) +
"\nCorrection variables [theta, k]: " + str(corvars) +
"\n\n--------------------------------------------------------------------------------\n"
)
from qm.QuantumMachinesManager import QuantumMachinesManager
from qm.qua import *
from qm import SimulationConfig
from configuration import *
# Open communication with the server.
QMm = QuantumMachinesManager()
# Create a quantum machine based on the configuration.
QM1 = QMm.open_qm(config)
with program() as prog:
play('playOp', 'qe1')
job = QM1.simulate(prog,
SimulationConfig(int(300)))
samples = job.get_simulated_samples()
samples.con1.plot()
int_freq=qubit_IF,
mixer=mixer_qubit)
lb_qubit = LabBrick(name="lb_qubit",
serial_number=25331,
frequency=qubit_LO,
power=15)
########################################################################################
########################################################################################
# initialize mixer_rr, rr and the rr's LO
mixer_rr = QuantumElement(
name="mixer_rr",
i_offset=rr_mixer_offsets["I"],
q_offset=rr_mixer_offsets["Q"],
gain_offset=rr_mixer_offsets["G"],
phase_offset=rr_mixer_offsets["P"],
)
rr = QuantumElement(name="rr", lo_freq=rr_LO, int_freq=rr_IF, mixer=mixer_rr)
lb_rr = LabBrick(name="lb_rr", serial_number=25335, frequency=rr_LO, power=15)
########################################################################################
########################################################################################
# initialize QM with the updated config file
qmm = QuantumMachinesManager()
qm = qmm.open_qm(config)
########################################################################################
########################################################################################
class QuantumMachine(object):
def __init__(self, connection_info):
self.connection_info = connection_info
port = ""
if self.connection_info[
"gateway_port"] is not None and self.connection_info[
"gateway_port"] != "":
port = self.connection_info["gateway_port"]
ip = ""
if self.connection_info[
"gateway_ip"] is not None and self.connection_info[
"gateway_ip"] != "":
ip = self.connection_info["gateway_ip"]
if ip != "" and port != "":
self.qmm = QuantumMachinesManager(host=ip, port=port)
else:
self.qmm = QuantumMachinesManager()
self.qmObj = None
self.job = None
def connect(self):
"""
Already connected in the constructor
:return:
"""
pass
def connected(self):
"""Return whether or not commands can be sent to the instrument
"""
try:
print(self.qmObj.list_controllers())
except Exception:
return False
return True
def close_connection(self):
if self.qmObj:
self.qmObj.close()
def set_config(self, config):
self.qmObj = self.qmm.open_qm(config)
def execute_program(self, prog, duration_limit, data_limit):
self.job = self.qmObj.execute(prog,
duration_limit=duration_limit,
data_limit=data_limit,
force_execution=True)
def resume(self):
self.job.resume()
def set_output_dc_offset_by_qe(self, element, input, offset):
self.qmObj.set_output_dc_offset_by_element(element, input, offset)
def set_input_dc_offset_by_qe(self, element, output, offset):
self.qmObj.set_input_dc_offset_by_element(element, output, offset)
def get_results(self):
return self.job.get_results()
def set_io_values(self, io1_value, io2_value):
self.qmObj.set_io_values(io1_value, io2_value)
def get_io_values(self):
return self.qmObj.get_io_values()
def set_mixer_correction(self, mixer, intermediate_frequency, lo_frequency,
values):
self.qmObj.set_mixer_correction(mixer, intermediate_frequency,
lo_frequency, values)
def set_intermediate_frequency(self, qe, intermediate_frequency):
self.qmObj.set_intermediate_frequency(qe, intermediate_frequency)
def set_digital_delay(self, qe, digital_input, delay):
self.qmObj.set_digital_delay(qe, digital_input, delay)
def set_digital_buffer(self, qe, digital_input, buffer):
self.qmObj.set_digital_buffer(qe, digital_input, buffer)
"type": "arbitrary",
"samples": gaussian_derivative_fn(0.2 * 1.9164, 0.046 * 288.5, 175, 4),
},
},
"mixers": {
"mixer_qubit": [
{
"intermediate_frequency": int(-50e6),
"lo_frequency": int(5e9),
"correction": (1.0, 0.0, 0.0, 1.0),
},
],
},
}
qmm = QuantumMachinesManager()
with program() as test1:
play("drag1" * amp(0.0, 0.0, 0.0, 0.046), "qubit")
with program() as test2:
play("drag2", "qubit")
sim_config = SimulationConfig(duration=250, include_analog_waveforms=True)
job1 = qmm.simulate(config, test1, sim_config)
samples1 = job1.get_simulated_samples()
i_samples1, q_samples1 = samples1.con1.analog["1"], samples1.con1.analog["2"]
job2 = qmm.simulate(config, test2, sim_config)
samples2 = job2.get_simulated_samples()
class QuantumMachine(BaseInstrument):
caching_permissions = {}
def __init__(self,
connection_info,
caching_allowed=True,
caching_permissions={},
auto_open=True):
super(QuantumMachine, self).__init__(connection_info, caching_allowed,
caching_permissions, auto_open)
self.connection_info = connection_info
port = ""
if connection_info[
"gateway_port"] and connection_info["gateway_port"] != "":
port = connection_info["gateway_port"]
ip = ""
if connection_info[
"gateway_ip"] and connection_info["gateway_ip"] != "":
ip = connection_info["gateway_ip"]
if ip != "" and port != "":
self.qmm = QuantumMachinesManager(host=ip, port=port)
else:
self.qmm = QuantumMachinesManager()
self.qmObj = None
self.job = None
def connect(self):
"""
Already connected in the constructor
:return:
"""
pass
def connected(self):
"""Return whether or not commands can be sent to the instrument
"""
try:
print(self.qmObj.list_controllers())
except Exception:
return False
return True
def close_connection(self):
if self.qmObj:
self.qmObj.close()
def clear_all_job_results(self):
self.qmm.clear_all_job_results()
def set_config(self, config):
self.qmObj = self.qmm.open_qm(config, close_other_machines=True)
@requires_config
def execute_program(self, prog, duration_limit=0, data_limit=0):
"""Create a job on the OPX to execute a program.
The duration_limit and data_limit arguments specify the
maximum time and data size that the job can use before getting
stopped by the server. Those limits are disabled by default.
"""
self.job = self.qmObj.execute(prog,
duration_limit=duration_limit,
data_limit=data_limit,
force_execution=True)
@requires_config
def simulate_program(self, prog, duration):
""" Simulate the program on the OPX
The duration parameter specifies the number of FPGA cycles
of the simulation (4ns/cycle).
This functions opens a matplotlib popup with the results.
"""
self.job = self.qmObj.simulate(
prog,
SimulationConfig(duration=duration, include_analog_waveforms=True))
samples = self.job.get_simulated_samples()
samples.con1.plot(digital_ports=(0, ))
import matplotlib.pyplot as plt
plt.show()
def is_paused(self):
return self.job.is_paused()
def resume(self):
self.job.resume()
@requires_config
def set_output_dc_offset_by_qe(self, element, input, offset):
self.qmObj.set_output_dc_offset_by_element(element, input, offset)
@requires_config
def set_input_dc_offset_by_qe(self, element, output, offset):
self.qmObj.set_input_dc_offset_by_element(element, output, offset)
@requires_config
def wait_for_all_results(self, ):
"""Wait for the current job to be completed.
"""
self.job.result_handles.wait_for_all_values()
@requires_config
def get_results(self, path=None):
return self.job.result_handles
@requires_config
def get_execution_report(self, path=None):
return self.job.execution_report()
@requires_config
def set_io_values(self, io1_value, io2_value):
self.qmObj.set_io_values(io1_value, io2_value)
@requires_config
def get_io_values(self):
return self.qmObj.get_io_values()
@requires_config
def set_mixer_correction(self, mixer, intermediate_frequency, lo_frequency,
values):
self.qmObj.set_mixer_correction(mixer, intermediate_frequency,
lo_frequency, values)
@requires_config
def set_intermediate_frequency(self, qe, intermediate_frequency):
self.qmObj.set_intermediate_frequency(qe, intermediate_frequency)
@requires_config
def set_digital_delay(self, qe, digital_input, delay):
self.qmObj.set_digital_delay(qe, digital_input, delay)
@requires_config
def set_digital_buffer(self, qe, digital_input, buffer):
self.qmObj.set_digital_buffer(qe, digital_input, buffer)
class QMQubitConfig:
def __init__(self):
self.qmm = QuantumMachinesManager()
self.qm = None
self.config = None
# Qubit
self.qubit_freq = 1e9
self.QB_IF_freq = 80.0e6
self.QB_LO_freq = self.qubit_freq - self.QB_IF_freq # for the mixer
self.saturation_pulse_len = 50000
self.pi_pulse_len = 48
self.pi_wf = pulses.gauss(0.49, 0.0, 10.0, self.pi_pulse_len)
self.pi2_wf = self.pi_wf / 2
# Readout
self.res_freq = 7e9
self.RR_IF_freq = 62.5e6
self.RR_LO_up_freq = self.res_freq + self.RR_IF_freq
self.RR_LO_down_freq = self.res_freq - self.RR_IF_freq
self.wait_sig = 160 # minimum 32 clock cycles
self.smearing_sig = 0 # multiple period
self.wait_ref = 53 # minimum 32 clock cycles
self.smearing_ref = 0
self.readout_pulse_len = 512 # multiple of 16
self.readout_wf = pulses.const(0.49, self.readout_pulse_len)
self.theta = 0.0 # rotation of IQ plane
# create config and load it
self.load_config()
def set_readout_wf(self, func, args, plot=False):
self.readout_wf = func(*args, self.readout_pulse_len)
self.load_config()
#if plot:
# self.ax.plot(self.readout_wf)
# plt.show()
def set_pi_pulse_wf(self, func, args, plot=False):
self.pi_wf = func(*args, self.pi_pulse_len)
self.pi2_wf = self.pi_wf / 2
self.load_config()
#if plot:
# self.ax.plot(self.pi_wf)
# self.ax.plot(self.pi2_wf)
# plt.show()
def set_theta(self, theta):
self.theta = theta
self.load_config()
# TODO set dc offset mixers
def load_config(self):
opx_one = "Opx"
self.config = {
'version': 1,
'controllers': {
opx_one: {
'type': 'Opx',
'analog_outputs': {
# sweep offset of I,Q to see which combination best suppresses LO leakage
1: {
'offset': 0
}, # I qubit
2: {
'offset': 0
}, # Q qubit
3: {
'offset': 0
}, # RR
},
'analog_inputs': {
1: {
'offset': 0
}, # Reference
2: {
'offset': 0
} # Signal
},
'digital_outputs': {
1: {}
}
},
},
'elements': {
'signal': {
"singleInput": {
"port": (opx_one, 3),
},
'intermediate_frequency': self.RR_IF_freq,
'operations': {
'readout_sig': 'readout_pulse',
'saturation_pulse': 'readout_saturation_pulse',
},
"outputs": {
'out1': (opx_one, 1),
},
'time_of_flight': 4 * self.wait_sig,
'smearing': self.smearing_sig
},
'reference': {
"singleInput": {
"port": (opx_one, 3),
},
'intermediate_frequency': self.RR_IF_freq,
'operations': {
'readout_ref': 'reference_pulse',
},
"outputs": {
'out1': (opx_one, 2), # TODO out2?
},
'time_of_flight': 4 * self.wait_ref,
'smearing': self.smearing_ref
},
'qubit': {
"mixInputs": {
"I": (opx_one, 1),
"Q": (opx_one, 2),
"mixer": "mixer_QB",
"lo_frequency": self.QB_LO_freq
},
'intermediate_frequency': self.QB_IF_freq,
'operations': {
'saturation_pulse': 'saturation_pulse',
'pi_pulse': 'pi_pulse',
'pi/2_pulse': 'pi/2_pulse',
}
},
},
"pulses": {
'readout_saturation_pulse': {
'operation': 'control',
'length': self.saturation_pulse_len,
'waveforms': {
'single': 'const_wf',
},
},
'saturation_pulse': {
'operation': 'control',
'length': self.saturation_pulse_len,
'waveforms': {
'I': 'const_wf',
'Q': 'zero_wf',
}
},
'pi_pulse': {
'operation': 'control',
'length': self.pi_pulse_len,
'waveforms': {
'I': 'pi_wf',
'Q': 'zero_wf'
},
},
'pi/2_pulse': {
'operation': 'control',
'length': self.pi_pulse_len,
'waveforms': {
'I': 'pi/2_wf',
'Q': 'zero_wf'
},
},
'readout_pulse': {
'operation': 'measurement',
'length': self.readout_pulse_len,
'waveforms': {
'single': 'readout_wf',
},
'digital_marker': 'ON',
'integration_weights': {
'integW_Is': 'integW_Is',
'integW_Qs': 'integW_Qs'
},
},
'reference_pulse': {
'operation': 'measurement',
'length': self.readout_pulse_len,
'waveforms': {
'single': 'zero_wf',
},
'digital_marker': 'ON',
'integration_weights': {
'integW_Ir': 'integW_Ir',
'integW_Qr': 'integW_Qr',
}
},
},
'waveforms': {
'zero_wf': {
'type': 'constant',
'sample': 0
},
'const_wf': {
'type': 'constant',
'sample': 0.49
},
'readout_wf': {
'type': 'arbitrary',
'samples': self.readout_wf.tolist()
},
'pi_wf': {
'type': 'arbitrary',
'samples': self.pi_wf.tolist()
},
'pi/2_wf': {
'type': 'arbitrary',
'samples': self.pi2_wf.tolist()
}
},
'digital_waveforms': {
'ON': {
'samples': [(1, 0)]
},
},
'integration_weights': {
'integW_Is': {
'cosine': [np.cos(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_sig),
'sine': [np.sin(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_sig),
},
'integW_Qs': {
'cosine': [np.sin(-self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_sig),
'sine': [np.cos(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_sig),
},
'integW_Ir': {
'cosine': [np.cos(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_ref),
'sine': [np.sin(-self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_ref),
},
'integW_Qr': {
'cosine': [np.sin(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_ref),
'sine': [np.cos(self.theta / 360 * np.pi)] *
int(self.readout_pulse_len / 4 + self.smearing_ref),
},
},
'mixers': {
'mixer_QB': [{
'intermediate_frequency': self.QB_IF_freq,
'lo_frequency': self.QB_LO_freq,
'correction': [1, 0, 0, 1]
}],
},
}
self.qm = self.qmm.open_qm(self.config)
# TODO make it nice
def print_config(self):
print(self.config)
n = declare(int)
t = declare(int)
with for_(n, 0, n < 10, n + 1):
with for_(t, 10, t < 1000, t + 10):
play("pi", "qubit")
wait(t, 'qubit', 'rr')
align('qubit', 'rr')
measure('readout', 'rr', None, demod.full('integW1', I),
demod.full('integW2', Q))
save(I, I_stream)
save(Q, Q_stream)
with stream_processing():
I_stream.save_all("I")
Q_stream.save_all("Q")
qmm = QuantumMachinesManager(host="18.193.150.71")
qm = qmm.open_qm(config)
job = qm.simulate(experiment, SimulationConfig(2000))
samps = job.get_simulated_samples()
fig, axes = plt.subplots(4)
labels = ['I_q1', 'Q_q1', 'I_rr1', 'Q_rr1']
for i in range(4):
axes[i].plot(samps.con1.analog.get(str(i + 1)))
axes[i].set_title(labels[i])
plt.tight_layout()
plt.show()