def test_assemble_memory_slots_for_schedules(self):
"""Test assembling schedules with different memory slots."""
acquire = pulse.Acquire(5)
n_memoryslots = [10, 5, 7]
schedules = []
for n_memoryslot in n_memoryslots:
schedule = acquire(self.backend_config.acquire(0),
mem_slots=pulse.MemorySlot(n_memoryslot-1))
schedules.append(schedule)
qobj = assemble(schedules,
qubit_lo_freq=self.default_qubit_lo_freq,
meas_lo_freq=self.default_meas_lo_freq,
meas_map=[[0], [1]])
validate_qobj_against_schema(qobj)
self.assertEqual(qobj.config.memory_slots, max(n_memoryslots))
self.assertEqual(qobj.experiments[0].header.memory_slots, n_memoryslots[0])
self.assertEqual(qobj.experiments[1].header.memory_slots, n_memoryslots[1])
self.assertEqual(qobj.experiments[2].header.memory_slots, n_memoryslots[2])
def freq_sweep_schedule(qbit, backend_config, drive_chan,
meas_samples=1000,):
dt = backend_config.dt
print(f"Sampling time: {dt} ns")
drive_pulse = make_drive_pulse(dt)
meas_pulse = make_meas_pulse(dt, samples=meas_samples)
### Construct the acquire pulse to trigger the acquisition
# Acquire pulse samples
acq_cmd = pulse.Acquire(duration=meas_samples)
# Find out which group of qubits need to be acquired with this qubit
meas_map_idx = None
for i, measure_group in enumerate(backend_config.meas_map):
if qbit in measure_group:
meas_map_idx = i
break
assert meas_map_idx is not None, f"Couldn't find qubit {qbit} in the meas_map!"
### Collect the necessary channels
#drive_chan = pulse.DriveChannel(qbit)
meas_chan = pulse.MeasureChannel(qbit)
#acq_chan = pulse.AcquireChannel(qbit)
schedule = pulse.Schedule(name='Frequency sweep')
schedule += drive_pulse(drive_chan)
measure_schedule = meas_pulse(meas_chan)
# Trigger data acquisition, and store measured values into respective memory slots
measure_schedule += acq_cmd([pulse.AcquireChannel(i) for i
in backend_config.meas_map[meas_map_idx]],
[pulse.MemorySlot(i) for i
in backend_config.meas_map[meas_map_idx]])
# shift the start time of the schedule by some duration
schedule += measure_schedule << schedule.duration
schedule += drive_pulse(drive_chan)<< schedule.duration - drive_pulse.duration
schedule += measure_schedule << drive_pulse.duration
return schedule
def cr_drive_experiments(drive_idx,
target_idx,
flip_drive_qubit = False,
#cr_drive_amps=np.linspace(0, 0.9, 16),
#cr_drive_samples=800,
#cr_drive_sigma=4,
#pi_drive_samples=128,
#pi_drive_sigma=16
#meas_amp = Dims/128*0.025/2
#meas_width = int(rows/128*1150/2)
cr_drive_amps=np.linspace(0, 0.9, meas_width**2),
cr_drive_samples=sum(label1[:] == label2[:]),
cr_drive_sigma=meas_sigma,
pi_drive_samples=sum((label1[:] ==1)*(label2[:] ==1)), #label1[:] ==1 and label2[:] ==1
pi_drive_sigma=cr_drive_sigma**2):
"""Generate schedules corresponding to CR drive experiments.
Args:
drive_idx (int): label of driven qubit
target_idx (int): label of target qubit
flip_drive_qubit (bool): whether or not to start the driven qubit in the ground or excited state
cr_drive_amps (array): list of drive amplitudes to use
cr_drive_samples (int): number samples for each CR drive signal
cr_drive_sigma (float): standard deviation of CR Gaussian pulse
pi_drive_samples (int): number samples for pi pulse on drive
pi_drive_sigma (float): standard deviation of Gaussian pi pulse on drive
Returns:
list[Schedule]: A list of Schedule objects for each experiment
"""
# Construct measurement commands to be used for all schedules
#meas_amp = 0.025
#meas_samples = 1200
#meas_sigma = 4
#meas_width = 1150
meas_amp = 0.025
meas_sigma = 4
ni = int(np.ceil(cols/meas_sigma))
print(ni)
meas_samples = rows*ni
meas_width = int(rows*ni*23/24)
meas_pulse = GaussianSquare(duration=meas_samples, amp=meas_amp/np.linalg.norm(meas_amp),
sigma=meas_sigma, width=meas_width)
acq_sched = pulse.Acquire(meas_samples, pulse.AcquireChannel(0), pulse.MemorySlot(0))
acq_sched += pulse.Acquire(meas_samples, pulse.AcquireChannel(1), pulse.MemorySlot(1))
# create measurement schedule
measure_sched = (pulse.Play(meas_pulse, pulse.MeasureChannel(0)) |
pulse.Play(meas_pulse, pulse.MeasureChannel(1))|
acq_sched)
# Create schedule
schedules = []
for ii, cr_drive_amp in enumerate(cr_drive_amps):
# pulse for flipping drive qubit if desired
pi_pulse = Gaussian(duration=pi_drive_samples, amp=pi_amps[drive_idx], sigma=pi_drive_sigma)
# cr drive pulse
cr_width = cr_drive_samples - 2*cr_drive_sigma*4
cr_rabi_pulse = GaussianSquare(duration=cr_drive_samples,
amp=cr_drive_amp/np.linalg.norm(cr_drive_amp),
sigma=cr_drive_sigma,
width=cr_width)
# add commands to schedule
schedule = pulse.Schedule(name='cr_rabi_exp_amp_%s' % cr_drive_amp)
#schedule = pulse.Schedule(name='cr_rabi_exp_amp_%s' % cr_drive_amp/np.linalg.norm(cr_drive_amp))
# flip drive qubit if desired
if flip_drive_qubit:
schedule += pulse.Play(pi_pulse, pulse.DriveChannel(drive_idx))
# do cr drive
# First, get the ControlChannel index for CR drive from drive to target
cr_idx = two_qubit_model.control_channel_index((drive_idx, target_idx))
schedule += pulse.Play(cr_rabi_pulse, pulse.ControlChannel(cr_idx)) << schedule.duration
schedule += measure_sched << schedule.duration
schedules.append(schedule)
return schedules
#4.1 Constructing the schedules
# list of qubits to be used throughout the notebook
qubits = [0, 1]
# Construct a measurement schedule and add it to an InstructionScheduleMap
meas_amp = 0.025
meas_sigma = 4
ni = int(np.ceil(cols/meas_sigma))
print(ni)
meas_samples = rows*ni
meas_width = int(rows*ni*23/24)
meas_pulse = GaussianSquare(duration=meas_samples, amp=meas_amp,
sigma=meas_sigma, width=meas_width)
acq_sched = pulse.Acquire(meas_samples, pulse.AcquireChannel(0), pulse.MemorySlot(0))
acq_sched += pulse.Acquire(meas_samples, pulse.AcquireChannel(1), pulse.MemorySlot(1))
measure_sched = pulse.Play(meas_pulse, pulse.MeasureChannel(0)) | pulse.Play(meas_pulse, pulse.MeasureChannel(1)) | acq_sched
inst_map = pulse.InstructionScheduleMap()
inst_map.add('measure', qubits, measure_sched)
#Rabi schedules
#recall: Rabii oscillation
# The magnetic moment is thus {\displaystyle {\boldsymbol {\mu }}={\frac {\hbar }{2}}\gamma {\boldsymbol {\sigma }}}{\boldsymbol {\mu }}={\frac {\hbar }{2}}\gamma {\boldsymbol {\sigma }}.
# The Hamiltonian of this system is then given by {H} =-{{\mu }}\cdot{B} =-{\frac {\hbar }{2}}\omega _{0}\sigma _{z}-{\frac {\hbar }{2}}\omega _{1}(\sigma _{x}\cos \omega t-\sigma _{y}\sin \omega t)}\mathbf {H} =-{\boldsymbol {\mu }}\cdot \mathbf {B} =-{\frac {\hbar }{2}}\omega _{0}\sigma _{z}-{\frac {\hbar }{2}}\omega _{1}(\sigma _{x}\cos \omega t-\sigma _{y}\sin \omega t) where {\displaystyle \omega _{0}=\gamma B_{0}}\omega _{0}=\gamma B_{0} and {\displaystyle \omega _{1}=\gamma B_{1}}\omega _{1}=\gamma B_{1}
# Now, let the qubit be in state {\displaystyle |0\rangle }{\displaystyle |0\rangle } at time {\displaystyle t=0}t=0. Then, at time {\displaystyle t}t, the probability of it being found in state {\displaystyle |1\rangle }|1\rangle is given by {\displaystyle P_{0\to 1}(t)=\left({\frac {\omega _{1}}{\Omega }}\right)^{2}\sin ^{2}\left({\frac {\Omega t}{2}}\right)}{\displaystyle P_{0\to 1}(t)=\left({\frac {\omega _{1}}{\Omega }}\right)^{2}\sin ^{2}\left({\frac {\Omega t}{2}}\right)} where {\displaystyle \Omega ={\sqrt {(\omega -\omega _{0})^{2}+\omega _{1}^{2}}}}\Omega ={\sqrt {(\omega -\omega _{0})^{2}+\omega _{1}^{2}}}
# the qubit oscillates between the {\displaystyle |0\rangle }|0\rang and {\displaystyle |1\rangle }|1\rangle states.
# The maximum amplitude for oscillation is achieved at {\displaystyle \omega =\omega _{0}}\omega =\omega _{0}, which is the condition for resonance.
# At resonance, the transition probability is given by {\displaystyle P_{0\to 1}(t)=\sin ^{2}\left({\frac {\omega _{1}t}{2}}\right)}{\displaystyle P_{0\to 1}(t)=\sin ^{2}\left({\frac {\omega _{1}t}{2}}\right)}
def output(data):
print("We have F: ", data.F, " nT")
N_delta = 2
N = int(math.log(data.T_2 * 10 ** (-6) / data.t_init) / math.log(2))-N_delta
multiplier = 30
# Ramsey experiment parameters
detuning_max_MHz = data.const * data.F_max * data.F_degree / (2 * math.pi) / MHz / multiplier
detuning_min_MHz = data.const * data.F_min * data.F_degree / (2 * math.pi) / MHz / multiplier
detuning_MHz = data.const * data.F * data.F_degree / (2 * math.pi) / MHz / multiplier
delta_min_det_MHz = -0.05 - 0.02 - 0.12 - 0.22
delta_max_det_MHz = -0.05 - 0.05 - 0.05 - 0.12 - 0.20
detuning_MHz = (detuning_min_MHz+delta_min_det_MHz) + (detuning_max_MHz+delta_max_det_MHz - (detuning_min_MHz+delta_min_det_MHz))*(detuning_MHz - detuning_min_MHz)/(detuning_max_MHz-detuning_min_MHz)
times = [data.t_init*2**(i) for i in range(N)]
# Drive parameters
# The drive amplitude for pi/2 is simply half the amplitude of the pi pulse
drive_amp = pi_amp / 2
# x_90 is a concise way to say pi_over_2; i.e., an X rotation of 90 degrees
with pulse.build(backend) as x90_pulse:
drive_duration = get_closest_multiple_of_16(pulse.seconds_to_samples(drive_duration_sec))
drive_sigma = pulse.seconds_to_samples(drive_sigma_sec)
drive_chan = pulse.drive_channel(qubit)
pulse.play(pulse.Gaussian(duration=drive_duration,
amp=drive_amp,
sigma=drive_sigma,
name='x90_pulse'), drive_chan)
# create schedules for Ramsey experiment
ramsey_schedules = []
ramsey_frequency = round(precise_qubit_freq + detuning_MHz * MHz, 6) # need ramsey freq in Hz
for time in times:
with pulse.build(backend=backend, default_alignment='sequential',
name=f"det = {detuning_MHz} MHz") as ramsey_schedule:
drive_chan = pulse.drive_channel(qubit)
pulse.set_frequency(ramsey_frequency, drive_chan)
pulse.call(x90_pulse)
pulse.delay(get_closest_multiple_of_16(pulse.seconds_to_samples(time*multiplier)), drive_chan)
pulse.call(x90_pulse)
pulse.measure(qubits=[qubit], registers=[pulse.MemorySlot(mem_slot)])
ramsey_schedules.append(ramsey_schedule)
# Execution settings
num_shots = data.num_of_repetitions
job = backend.run(ramsey_schedules,
meas_level=1,
meas_return='single',
shots=num_shots)
job_monitor(job)
ramsey_results = job.result(timeout=120)
ramsey_values = {}
for i in range(len(times)):
iq_data = ramsey_results.get_memory(i)[:, qubit] * scale_factor
ramsey_values[times[i]]=int(round(sum(map(classify, iq_data)) / num_shots))
'''
times = [data.t_init * 2 ** (i) for i in range(N, N+N_delta)]
# create schedules for Ramsey experiment
ramsey_schedules = []
ramsey_frequency = round(precise_qubit_freq + detuning_MHz * MHz, 6) # need ramsey freq in Hz
for time in times:
with pulse.build(backend=backend, default_alignment='sequential',
name=f"det = {detuning_MHz} MHz") as ramsey_schedule:
drive_chan = pulse.drive_channel(qubit)
pulse.set_frequency(ramsey_frequency, drive_chan)
pulse.call(x90_pulse)
pulse.delay(get_closest_multiple_of_16(pulse.seconds_to_samples(time * multiplier)), drive_chan)
pulse.call(x90_pulse)
pulse.measure(qubits=[qubit], registers=[pulse.MemorySlot(mem_slot)])
ramsey_schedules.append(ramsey_schedule)
# Execution settings
num_shots = data.num_of_repetitions
job = backend.run(ramsey_schedules,
meas_level=1,
meas_return='single',
shots=num_shots)
job_monitor(job)
ramsey_results = job.result(timeout=120)
for i in range(len(times)):
iq_data = ramsey_results.get_memory(i)[:, qubit] * scale_factor
ramsey_values[times[i]] = int(round(sum(map(classify, iq_data)) / num_shots))
#'''
print(ramsey_values)
return ramsey_values
#print(output(data))
def setUp(self):
"""Just some useful, reusable Parameters, constants, schedules."""
super().setUp()
self.amp1_1 = Parameter("amp1_1")
self.amp1_2 = Parameter("amp1_2")
self.amp2 = Parameter("amp2")
self.amp3 = Parameter("amp3")
self.dur1 = Parameter("dur1")
self.dur2 = Parameter("dur2")
self.dur3 = Parameter("dur3")
self.parametric_waveform1 = pulse.Gaussian(duration=self.dur1,
amp=self.amp1_1 +
self.amp1_2,
sigma=self.dur1 / 4)
self.parametric_waveform2 = pulse.Gaussian(duration=self.dur2,
amp=self.amp2,
sigma=self.dur2 / 5)
self.parametric_waveform3 = pulse.Gaussian(duration=self.dur3,
amp=self.amp3,
sigma=self.dur3 / 6)
self.ch1 = Parameter("ch1")
self.ch2 = Parameter("ch2")
self.ch3 = Parameter("ch3")
self.d1 = pulse.DriveChannel(self.ch1)
self.d2 = pulse.DriveChannel(self.ch2)
self.d3 = pulse.DriveChannel(self.ch3)
self.phi1 = Parameter("phi1")
self.phi2 = Parameter("phi2")
self.phi3 = Parameter("phi3")
self.meas_dur = Parameter("meas_dur")
self.mem1 = Parameter("s1")
self.reg1 = Parameter("m1")
self.context_dur = Parameter("context_dur")
# schedule under test
subroutine = pulse.ScheduleBlock(alignment_context=AlignLeft())
subroutine += pulse.ShiftPhase(self.phi1, self.d1)
subroutine += pulse.Play(self.parametric_waveform1, self.d1)
sched = pulse.Schedule()
sched += pulse.ShiftPhase(self.phi3, self.d3)
long_schedule = pulse.ScheduleBlock(alignment_context=AlignEquispaced(
self.context_dur),
name="long_schedule")
long_schedule += subroutine
long_schedule += pulse.ShiftPhase(self.phi2, self.d2)
long_schedule += pulse.Play(self.parametric_waveform2, self.d2)
long_schedule += pulse.Call(sched)
long_schedule += pulse.Play(self.parametric_waveform3, self.d3)
long_schedule += pulse.Acquire(
self.meas_dur,
pulse.AcquireChannel(self.ch1),
mem_slot=pulse.MemorySlot(self.mem1),
reg_slot=pulse.RegisterSlot(self.reg1),
)
self.test_sched = long_schedule
acq_cmd = pulse.Acquire(duration=meas_samples)
# In[59]:
meas_pulse = pulse_lib.gaussian_square(duration=meas_samples,
sigma=meas_sigma,
amp=meas_amp,
risefall=meas_risefall,
name='measurement_pulse')
measure_schedule = meas_pulse(meas_chan)<<measure_time
measure_schedule += acq_cmd([pulse.AcquireChannel(i) for i in backend_config.meas_map[meas_map_idx]],
[pulse.MemorySlot(i) for i in backend_config.meas_map[meas_map_idx]])
# In[64]:
measure_schedule.draw(plot_range=[0,25000],channels_to_plot=[drive_chan, meas_chan], label=True, scaling=1.0)
# In[65]:
schedule = pulse.Schedule(name='Readout_Discrim')
schedule += x_gate
schedule += measure_schedule << measure_time
# Import backend configurations
backend_config = backend.configuration()
# Set measurement channels
meas_chan = pulse.MeasureChannel(qubit)
acq_chan = pulse.AcquireChannel(qubit)
# Add a measurement stimulus on the measure channel pulse to trigger readout
measure_schedule = pulse.Play(measurement_pulse, meas_chan)
# Trigger data acquisition, and store measured values into respective memory slots
measure_schedule += pulse.Acquire(
measurement_pulse.duration,
pulse.AcquireChannel(backend_config.meas_map[meas_map_idx][0]),
pulse.MemorySlot(backend_config.meas_map[meas_map_idx][0]),
)
# Run 0-1 state discrimination experiment
gnd_exc_experiment_result = ground_excited_experiment(
qubit, backend, num_shots_with_training, measure_schedule)
# Train and classify data with Q-CTRL's discriminator
gnd_exc_results = train_discriminator(gnd_exc_experiment_result,
test_sample_size)[2]
# Store results
measurement_data.append(gnd_exc_results)
silhouette_time_list.append(intercluster_distance(gnd_exc_results))
silhouette_list.append(silhouette_time_list)
#4.1 Constructing the schedules
# list of qubits to be used throughout the notebook
qubits = [0, 1]
# Construct a measurement schedule and add it to an InstructionScheduleMap
meas_amp = 0.025
meas_samples = 1200
meas_sigma = 4
meas_width = 1150
meas_pulse = GaussianSquare(duration=meas_samples,
amp=meas_amp,
sigma=meas_sigma,
width=meas_width)
acq_sched = pulse.Acquire(meas_samples, pulse.AcquireChannel(0),
pulse.MemorySlot(0))
acq_sched += pulse.Acquire(meas_samples, pulse.AcquireChannel(1),
pulse.MemorySlot(1))
measure_sched = pulse.Play(meas_pulse, pulse.MeasureChannel(0)) | pulse.Play(
meas_pulse, pulse.MeasureChannel(1)) | acq_sched
inst_map = pulse.InstructionScheduleMap()
inst_map.add('measure', qubits, measure_sched)
#Rabi schedules
#recall: Rabii oscillation
# The magnetic moment is thus {\displaystyle {\boldsymbol {\mu }}={\frac {\hbar }{2}}\gamma {\boldsymbol {\sigma }}}{\boldsymbol {\mu }}={\frac {\hbar }{2}}\gamma {\boldsymbol {\sigma }}.
# The Hamiltonian of this system is then given by {H} =-{{\mu }}\cdot{B} =-{\frac {\hbar }{2}}\omega _{0}\sigma _{z}-{\frac {\hbar }{2}}\omega _{1}(\sigma _{x}\cos \omega t-\sigma _{y}\sin \omega t)}\mathbf {H} =-{\boldsymbol {\mu }}\cdot \mathbf {B} =-{\frac {\hbar }{2}}\omega _{0}\sigma _{z}-{\frac {\hbar }{2}}\omega _{1}(\sigma _{x}\cos \omega t-\sigma _{y}\sin \omega t) where {\displaystyle \omega _{0}=\gamma B_{0}}\omega _{0}=\gamma B_{0} and {\displaystyle \omega _{1}=\gamma B_{1}}\omega _{1}=\gamma B_{1}
# Now, let the qubit be in state {\displaystyle |0\rangle }{\displaystyle |0\rangle } at time {\displaystyle t=0}t=0. Then, at time {\displaystyle t}t, the probability of it being found in state {\displaystyle |1\rangle }|1\rangle is given by {\displaystyle P_{0\to 1}(t)=\left({\frac {\omega _{1}}{\Omega }}\right)^{2}\sin ^{2}\left({\frac {\Omega t}{2}}\right)}{\displaystyle P_{0\to 1}(t)=\left({\frac {\omega _{1}}{\Omega }}\right)^{2}\sin ^{2}\left({\frac {\Omega t}{2}}\right)} where {\displaystyle \Omega ={\sqrt {(\omega -\omega _{0})^{2}+\omega _{1}^{2}}}}\Omega ={\sqrt {(\omega -\omega _{0})^{2}+\omega _{1}^{2}}}
# the qubit oscillates between the {\displaystyle |0\rangle }|0\rang and {\displaystyle |1\rangle }|1\rangle states.
# Creare il programma di base
# Start with drive pulse acting on the drive channel
freq = Parameter('freq')
with pulse.build(backend=backend, default_alignment='sequential',
name='Frequency sweep') as sweep_sched:
drive_duration = get_closest_multiple_of_16(pulse.seconds_to_samples(drive_duration_sec))
drive_sigma = pulse.seconds_to_samples(drive_sigma_sec)
drive_chan = pulse.drive_channel(qubit)
pulse.set_frequency(freq, drive_chan)
# Drive pulse samples
pulse.play(pulse.Gaussian(duration=drive_duration,
sigma=drive_sigma,
amp=drive_amp,
name='freq_sweep_excitation_pulse'), drive_chan)
# Define our measurement pulse
pulse.measure(qubits=[qubit], registers=[pulse.MemorySlot(mem_slot)])
# Create the frequency settings for the sweep (MUST BE IN HZ)
frequencies_Hz = frequencies_GHz*GHz
schedules = [sweep_sched.assign_parameters({freq : f}, inplace=False)
for f in frequencies_Hz]
schedules[0].draw() #Argomento backend=backend
num_shots_per_frequency = 1024
job = backend.run(schedules,
meas_level=1,
meas_return='avg',
shots=num_shots_per_frequency)
