In most quantum algorithms/applications, computations are carried out over a 2-dimensional space spanned by $|0\rangle$ and $|1\rangle$. In IBM's hardware, however, there also exist higher energy states which are not typically used. The focus of this section is to explore these states using Qiskit Pulse. In particular, we demonstrate how to excite the $|2\rangle$ state and build a discriminator to classify the $|0\rangle$, $|1\rangle$ and $|2\rangle$ states.
We recommend reviewing the prior chapter before going through this notebook. We also suggest reading the Qiskit Pulse specifications (Ref 1).
We now give some additional background on the physics of transmon qubits, the basis for much of IBM's quantum hardware. These systems contain superconducting circuits composed of a Josephson junction and capacitor. For those unfamilar with superconducting circuits, see the review here (Ref. 2). The Hamiltonian of this system is given by
$$ H = 4 E_C n^2 - E_J \cos(\phi), $$where $E_C, E_J$ denote the capacitor and Josephson energies, $n$ is the reduced charge number operator and $\phi$ is the reduced flux across the junction. We work in units with $\hbar=1$.
Transmon qubits are defined in the regime where $\phi$ is small, so we may expand $E_J \cos(\phi)$ in a Taylor series (ignoring constant terms)
$$ E_J \cos(\phi) \approx \frac{1}{2} E_J \phi^2 - \frac{1}{24} E_J \phi^4 + \mathcal{O}(\phi^6). $$The quadratic term $\phi^2$ defines the standard harmonic oscillator. Each additional term contributes an anharmonicity.
Using the relations $n \sim (a-a^\dagger), \phi \sim (a+a^\dagger)$ (for raising, lowering operators $a^\dagger, a$), it can be shown that the system resembles a Duffing oscillator with Hamiltonian $$ H = \omega a^\dagger a + \frac{\alpha}{2} a^\dagger a^\dagger a a, $$
where $\omega$ gives the $0\rightarrow1$ excitation frequency ($\omega \equiv \omega^{0\rightarrow1}$) and $\alpha$ is the anharmonicity between the $0\rightarrow1$ and $1\rightarrow2$ frequencies ($\alpha \equiv \omega^{1\rightarrow2} - \omega^{0\rightarrow1}$). Drive terms can be added as needed.
If we choose to specialize to the standard 2-dimensional subspace, we can make $|\alpha|$ sufficiently large or use special control techniques to suppress the higher energy states.
We begin by importing dependencies and defining some default variable values. We choose qubit 0 to run our experiments. We perform our experiments on the publicly available single qubit device ibmq_armonk.
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.signal import find_peaks
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
import qiskit.pulse as pulse
import qiskit.pulse.pulse_lib as pulse_lib
from qiskit.compiler import assemble
from qiskit.pulse.commands import SamplePulse
from qiskit.tools.monitor import job_monitor
import warnings
warnings.filterwarnings('ignore')
from qiskit.tools.jupyter import *
%matplotlib inline
from qiskit import IBMQ
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')
backend = provider.get_backend('ibmq_armonk')
backend_config = backend.configuration()
assert backend_config.open_pulse, "Backend doesn't support Pulse"
dt = backend_config.dt
backend_defaults = backend.defaults()
# unit conversion factors -> all backend properties returned in SI (Hz, sec, etc)
GHz = 1.0e9 # Gigahertz
MHz = 1.0e6 # Megahertz
us = 1.0e-6 # Microseconds
ns = 1.0e-9 # Nanoseconds
qubit = 0 # qubit we will analyze
default_qubit_freq = backend_defaults.qubit_freq_est[qubit] # Default qubit frequency in Hz.
print(f"Qubit {qubit} has an estimated frequency of {default_qubit_freq/ GHz} GHz.")
# scale data (specific to each device)
scale_factor = 1e-14
# number of shots for our experiments
NUM_SHOTS = 1024
### Collect the necessary channels
drive_chan = pulse.DriveChannel(qubit)
meas_chan = pulse.MeasureChannel(qubit)
acq_chan = pulse.AcquireChannel(qubit)
We define some additional helper functions.
def get_job_data(job, average):
"""Retrieve data from a job that has already run.
Args:
job (Job): The job whose data you want.
average (bool): If True, gets the data assuming data is an average.
If False, gets the data assuming it is for single shots.
Return:
list: List containing job result data.
"""
job_results = job.result(timeout=120) # timeout parameter set to 120 s
result_data = []
for i in range(len(job_results.results)):
if average: # get avg data
result_data.append(job_results.get_memory(i)[qubit]*scale_factor)
else: # get single data
result_data.append(job_results.get_memory(i)[:, qubit]*scale_factor)
return result_data
def get_closest_multiple_of_16(num):
"""Compute the nearest multiple of 16. Needed because pulse enabled devices require
durations which are multiples of 16 samples.
"""
return (int(num) - (int(num)%16))
Next we include some default parameters for drive pulses and measurement. We pull the measure command from the instruction schedule map (from backend defaults), so that it is updated with new calibrations.
# Drive pulse parameters (us = microseconds)
drive_sigma_us = 0.075 # This determines the actual width of the gaussian
drive_samples_us = drive_sigma_us*8 # This is a truncating parameter, because gaussians don't have
# a natural finite length
drive_sigma = get_closest_multiple_of_16(drive_sigma_us * us /dt) # The width of the gaussian in units of dt
drive_samples = get_closest_multiple_of_16(drive_samples_us * us /dt) # The truncating parameter in units of dt
# Find out which measurement map index is needed for this qubit
meas_map_idx = None
for i, measure_group in enumerate(backend_config.meas_map):
if qubit in measure_group:
meas_map_idx = i
break
assert meas_map_idx is not None, f"Couldn't find qubit {qubit} in the meas_map!"
# Get default measurement pulse from instruction schedule map
inst_sched_map = backend_defaults.instruction_schedule_map
measure = inst_sched_map.get('measure', qubits=backend_config.meas_map[meas_map_idx])
In this section, we build a discriminator for our standard $|0\rangle$ and $|1\rangle$ states. The job of the discriminator is to take meas_level=1 complex data and classify it into the standard $|0\rangle$ and $|1\rangle$ states (meas_level=2). This will replicate much of the work of the prior chapter. These results are necessary for exciting the higher energy states which are the focus of this notebook.
The first step in building a discriminator is to calibrate our qubit frequency, as done in the prior chapter.
def create_ground_freq_sweep_program(freqs, drive_power):
"""Builds a program that does a freq sweep by exciting the ground state.
Depending on drive power this can reveal the 0->1 frequency or the 0->2 frequency.
Args:
freqs (np.ndarray(dtype=float)): Numpy array of frequencies to sweep.
drive_power (float) : Value of drive amplitude.
Raises:
ValueError: Raised if use more than 75 frequencies; currently, an error will be thrown on the backend
if you try to do this.
Returns:
Qobj: Program for ground freq sweep experiment.
"""
if len(freqs) > 75:
raise ValueError("You can only run 75 schedules at a time.")
# print information on the sweep
print(f"The frequency sweep will go from {freqs[0] / GHz} GHz to {freqs[-1]/ GHz} GHz \
using {len(freqs)} frequencies. The drive power is {drive_power}.")
# Define the drive pulse
ground_sweep_drive_pulse = pulse_lib.gaussian(duration=drive_samples,
sigma=drive_sigma,
amp=drive_power,
name='ground_sweep_drive_pulse')
# Create the base schedule
schedule = pulse.Schedule(name='Frequency sweep starting from ground state.')
schedule |= ground_sweep_drive_pulse(drive_chan)
schedule |= measure << schedule.duration
# define frequencies for the sweep
schedule_freqs = [{drive_chan: freq} for freq in freqs]
# assemble the program
# Note: we only require a single schedule since each does the same thing;
# for each schedule, the LO frequency that mixes down the drive changes
# this enables our frequency sweep
ground_freq_sweep_program = assemble(schedule,
backend=backend,
meas_level=1,
meas_return='avg',
shots=NUM_SHOTS,
schedule_los=schedule_freqs)
return ground_freq_sweep_program
# We will sweep 40 MHz around the estimated frequency, with 75 frequencies
num_freqs = 75
ground_sweep_freqs = default_qubit_freq + np.linspace(-20*MHz, 20*MHz, num_freqs)
ground_freq_sweep_program = create_ground_freq_sweep_program(ground_sweep_freqs, drive_power=0.3)
ground_freq_sweep_job = backend.run(ground_freq_sweep_program)
print(ground_freq_sweep_job.job_id())
job_monitor(ground_freq_sweep_job)
# Get the job data (average)
ground_freq_sweep_data = get_job_data(ground_freq_sweep_job, average=True)
We fit our data to a Lorentzian curve and extract the calibrated frequency.
def fit_function(x_values, y_values, function, init_params):
"""Fit a function using scipy curve_fit."""
fitparams, conv = curve_fit(function, x_values, y_values, init_params)
y_fit = function(x_values, *fitparams)
return fitparams, y_fit
# do fit in Hz
(ground_sweep_fit_params,
ground_sweep_y_fit) = fit_function(ground_sweep_freqs,
ground_freq_sweep_data,
lambda x, A, q_freq, B, C: (A / np.pi) * (B / ((x - q_freq)**2 + B**2)) + C,
[7, 4.975*GHz, 1*GHz, 3*GHz] # initial parameters for curve_fit
)
# Note: we are only plotting the real part of the signal
plt.scatter(ground_sweep_freqs/GHz, ground_freq_sweep_data, color='black')
plt.plot(ground_sweep_freqs/GHz, ground_sweep_y_fit, color='red')
plt.xlim([min(ground_sweep_freqs/GHz), max(ground_sweep_freqs/GHz)])
plt.xlabel("Frequency [GHz]", fontsize=15)
plt.ylabel("Measured Signal [a.u.]", fontsize=15)
plt.title("0->1 Frequency Sweep", fontsize=15)
plt.show()
_, cal_qubit_freq, _, _ = ground_sweep_fit_params
print(f"We've updated our qubit frequency estimate from "
f"{round(default_qubit_freq/GHz, 7)} GHz to {round(cal_qubit_freq/GHz, 7)} GHz.")
Next, we perform a Rabi experiment to compute the $0\rightarrow1 ~ \pi$ pulse amplitude. Recall, a $\pi$ pulse is a pulse that takes us from the $|0\rangle$ to $|1\rangle$ state (a $\pi$ rotation on the Bloch sphere).
# experimental configuration
num_rabi_points = 50 # number of experiments (ie amplitudes to sweep out)
# Drive amplitude values to iterate over: 50 amplitudes evenly spaced from 0 to 0.75
drive_amp_min = 0
drive_amp_max = 0.75
drive_amps = np.linspace(drive_amp_min, drive_amp_max, num_rabi_points)
# Create schedule
rabi_01_schedules = []
# loop over all drive amplitudes
for ii, drive_amp in enumerate(drive_amps):
# drive pulse
rabi_01_pulse = pulse_lib.gaussian(duration=drive_samples,
amp=drive_amp,
sigma=drive_sigma,
name='rabi_01_pulse_%d' % ii)
# add commands to schedule
schedule = pulse.Schedule(name='Rabi Experiment at drive amp = %s' % drive_amp)
schedule |= rabi_01_pulse(drive_chan)
schedule |= measure << schedule.duration # shift measurement to after drive pulse
rabi_01_schedules.append(schedule)
# Assemble the schedules into a program
# Note: We drive at the calibrated frequency.
rabi_01_expt_program = assemble(rabi_01_schedules,
backend=backend,
meas_level=1,
meas_return='avg',
shots=NUM_SHOTS,
schedule_los=[{drive_chan: cal_qubit_freq}]
* num_rabi_points)
rabi_01_job = backend.run(rabi_01_expt_program)
print(rabi_01_job.job_id())
job_monitor(rabi_01_job)
# Get the job data (average)
rabi_01_data = get_job_data(rabi_01_job, average=True)
def baseline_remove(values):
"""Center data around 0."""
return np.array(values) - np.mean(values)
# Note: Only real part of data is plotted
rabi_01_data = np.real(baseline_remove(rabi_01_data))
(rabi_01_fit_params,
rabi_01_y_fit) = fit_function(drive_amps,
rabi_01_data,
lambda x, A, B, drive_01_period, phi: (A*np.cos(2*np.pi*x/drive_01_period - phi) + B),
[4, -4, 0.5, 0])
plt.scatter(drive_amps, rabi_01_data, color='black')
plt.plot(drive_amps, rabi_01_y_fit, color='red')
drive_01_period = rabi_01_fit_params[2]
# account for phi in computing pi amp
pi_amp_01 = (drive_01_period/2/np.pi) *(np.pi+rabi_01_fit_params[3])
plt.axvline(pi_amp_01, color='red', linestyle='--')
plt.axvline(pi_amp_01+drive_01_period/2, color='red', linestyle='--')
plt.annotate("", xy=(pi_amp_01+drive_01_period/2, 0), xytext=(pi_amp_01,0), arrowprops=dict(arrowstyle="<->", color='red'))
plt.annotate("$\pi$", xy=(pi_amp_01-0.03, 0.1), color='red')
plt.xlabel("Drive amp [a.u.]", fontsize=15)
plt.ylabel("Measured signal [a.u.]", fontsize=15)
plt.title('0->1 Rabi Experiment', fontsize=15)
plt.show()
print(f"Pi Amplitude (0->1) = {pi_amp_01}")
Using these results, we define our $0\rightarrow1$ $\pi$ pulse.
pi_pulse_01 = pulse_lib.gaussian(duration=drive_samples,
amp=pi_amp_01,
sigma=drive_sigma,
name='pi_pulse_01')
Now that we have our calibrated frequency and $\pi$ pulse, we can build a discriminator for $|0\rangle$ and $1\rangle$ states. The discriminator works by taking meas_level=1 data in the IQ plane and classifying it into a $|0\rangle$ or a $1\rangle$.
The $|0\rangle$ and $|1\rangle$ states form coherent circular "blobs" in the IQ plane, which are known as centroids. The center of the centroid defines the exact, no-noise IQ point for each state. The surrounding cloud shows the variance in the data, which is generated from a variety of noise sources.
We apply a machine learning technique, Linear Discriminant Analysis, to discriminate (distinguish) between $|0\rangle$ and $|1\rangle$. This is a common technique for classifying qubit states.
Our first step is to get the centroid data. To do so, we define two schedules (recalling that our system is in the $|0\rangle$ state to start):
- Measure the $|0\rangle$ state directly (obtain $|0\rangle$ centroid).
- Apply a $\pi$ pulse and then measure (obtain $|1\rangle$ centroid).
# Create the two schedules
# Ground state schedule
zero_schedule = pulse.Schedule(name="zero schedule")
zero_schedule |= measure
# Excited state schedule
one_schedule = pulse.Schedule(name="one schedule")
one_schedule |= pi_pulse_01(drive_chan)
one_schedule |= measure << one_schedule.duration
# Assemble the schedules into a program
IQ_01_program = assemble([zero_schedule, one_schedule],
backend=backend,
meas_level=1,
meas_return='single',
shots=NUM_SHOTS,
schedule_los=[{drive_chan: cal_qubit_freq}] * 2)
IQ_01_job = backend.run(IQ_01_program)
print(IQ_01_job.job_id())
job_monitor(IQ_01_job)
# Get job data (single); split for zero and one
IQ_01_data = get_job_data(IQ_01_job, average=False)
zero_data = IQ_01_data[0]
one_data = IQ_01_data[1]
def IQ_01_plot(x_min, x_max, y_min, y_max):
"""Helper function for plotting IQ plane for |0>, |1>. Limits of plot given
as arguments."""
# zero data plotted in blue
plt.scatter(np.real(zero_data), np.imag(zero_data),
s=5, cmap='viridis', c='blue', alpha=0.5, label=r'$|0\rangle$')
# one data plotted in red
plt.scatter(np.real(one_data), np.imag(one_data),
s=5, cmap='viridis', c='red', alpha=0.5, label=r'$|1\rangle$')
# Plot a large dot for the average result of the zero and one states.
mean_zero = np.mean(zero_data) # takes mean of both real and imaginary parts
mean_one = np.mean(one_data)
plt.scatter(np.real(mean_zero), np.imag(mean_zero),
s=200, cmap='viridis', c='black',alpha=1.0)
plt.scatter(np.real(mean_one), np.imag(mean_one),
s=200, cmap='viridis', c='black',alpha=1.0)
plt.xlim(x_min, x_max)
plt.ylim(y_min,y_max)
plt.legend()
plt.ylabel('I [a.u.]', fontsize=15)
plt.xlabel('Q [a.u.]', fontsize=15)
plt.title("0-1 discrimination", fontsize=15)
Below, we display the IQ plot. The blue centroid denotes the $|0\rangle$ state, while the red centroid denotes the $|1\rangle$ state. (Note: If the plot looks off, rerun the notebook)
x_min = -5
x_max = 15
y_min = -5
y_max = 10
IQ_01_plot(x_min, x_max, y_min, y_max)
Now it is time to actually build the discriminator. As mentioned above, we will use a machine learning technique called Linear Discriminant Analysis (LDA). LDA classifies an arbitrary data set into a set of categories (here $|0\rangle$, $|1\rangle$) by maximizing the distance between the means of each category and minimizing the variance within each category. For further detail, see here (Ref. 3).
LDA generates a line called a separatrix. Depending on which side of the separatrix a given data point is on, we can determine which category it belongs to. In our example, one side of the separatrix corresponds to $|0\rangle$ states and the other to $|1\rangle$ states.
We train our model using the first half of our data and test it on the second half. We use scikit.learn for an implementation of LDA; in a future release, this functionality will be added released directly into Qiskit-Ignis (see here).
We begin by reshaping our result data into a format suitable for discrimination.
def reshape_complex_vec(vec):
"""Take in complex vector vec and return 2d array w/ real, imag entries. This is needed for the learning.
Args:
vec (list): complex vector of data
Returns:
list: vector w/ entries given by (real(vec], imag(vec))
"""
length = len(vec)
vec_reshaped = np.zeros((length, 2))
for i in range(len(vec)):
vec_reshaped[i]=[np.real(vec[i]), np.imag(vec[i])]
return vec_reshaped
# Create IQ vector (split real, imag parts)
zero_data_reshaped = reshape_complex_vec(zero_data)
one_data_reshaped = reshape_complex_vec(one_data)
IQ_01_data = np.concatenate((zero_data_reshaped, one_data_reshaped))
print(IQ_01_data.shape) # verify IQ data shape
Next, we split our training and testing data. We test using a state vector with our expected results (an array of 0's for the ground schedule and 1s for the excited schedule).
# construct vector w/ 0's and 1's (for testing)
state_01 = np.zeros(NUM_SHOTS) # shots gives number of experiments
state_01 = np.concatenate((state_01, np.ones(NUM_SHOTS)))
print(len(state_01))
# Shuffle and split data into training and test sets
IQ_01_train, IQ_01_test, state_01_train, state_01_test = train_test_split(IQ_01_data, state_01, test_size=0.5)
Finally, we set up our model and train it. The accuracy of our fit is printed.
# Set up the LDA
LDA_01 = LinearDiscriminantAnalysis()
LDA_01.fit(IQ_01_train, state_01_train)
# test on some simple data
print(LDA_01.predict([[0,0], [10, 0]]))
# Compute accuracy
score_01 = LDA_01.score(IQ_01_test, state_01_test)
print(score_01)
The last step is to plot the separatrix.
# Plot separatrix on top of scatter
def separatrixPlot(lda, x_min, x_max, y_min, y_max, shots):
nx, ny = shots, shots
xx, yy = np.meshgrid(np.linspace(x_min, x_max, nx),
np.linspace(y_min, y_max, ny))
Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z[:, 1].reshape(xx.shape)
plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='black')
IQ_01_plot(x_min, x_max, y_min, y_max)
separatrixPlot(LDA_01, x_min, x_max, y_min, y_max, NUM_SHOTS)
We see how each side of the separatrix corresponds to a centroid (and hence a state). Given a point in the IQ plane, our model checks which side of the separatrix it lies on and returns the corresponding state.
Now that we have calibrated the $0, 1$ discriminator, we move on to exciting higher energy states. Specifically, we focus on exciting the $|2\rangle$ state and building a discriminator to classify $|0\rangle$, $|1\rangle$ and $2\rangle$ states from their respective IQ data points. The procedure for even higher states ($|3\rangle$, $|4\rangle$, etc) should be similar, but we have not tested them explicitly.
The process for building the higher state discriminator is as follows:
- Compute the $1\rightarrow2$ frequency.
- Conduct a Rabi experiment to obtain the $\pi$ pulse amplitude for $1\rightarrow2$. To do this, we first apply a $0\rightarrow1$ $\pi$ pulse to get from the $|0\rangle$ to the $|1\rangle$ state. Then, we do a sweep of drive amplitudes at the $1\rightarrow2$ frequency obtained above.
- Construct 3 schedules:\ a. Zero schedule: just measure the ground state.\ b. One schedule: apply a $0\rightarrow1$ $\pi$ pulse and measure.\ c. Two schedule: apply a $0\rightarrow1$ $\pi$ pulse, then a $1\rightarrow2$ $\pi$ pulse and measure.
- Separate the data from each schedule into training and testing sets and construct an LDA model for discrimination.
The first step in our calibration is to compute the frequency needed to go from the $1\rightarrow2$ state. There are two methods to do this:
- Do a frequency sweep from the ground state and apply very high power. If the applied power is large enough, two peaks should be observed. One at the $0\rightarrow1$ frequency found in section 1 and one at the $0\rightarrow2$ frequency. The $1\rightarrow2$ frequency can be obtained by taking the difference of the two. Unfortunately, for
ibmq_armonk, the maximum drive power of $1.0$ is not sufficient to see this transition. Instead, we turn to the second method. - Excite the $|1\rangle$ state by applying a $0\rightarrow1$ $\pi$ pulse. Then perform the frequency sweep over excitations of the $|1\rangle$ state. A single peak should be observed at a frequency lower than the $0\rightarrow1$ frequency which corresponds to the $1\rightarrow2$ frequency.
We follow the second method described above. To drive the $0\rightarrow 1$ $\pi$ pulse, we require a local oscillator (LO) frequency given by the calibrated $0\rightarrow1$ frequency cal_qubit_freq (see construction of the Rabi $\pi$ pulse in section 1). To sweep the range for the $1\rightarrow2$ frequency, however, we require varying the LO frequency. Unfortunately, the Pulse specification requires a single LO frequency per schedule.
To resolve this, we set the LO frequency to cal_qubit_freq and multiply a sine function onto the $1\rightarrow2$ pulse at freq-cal_qubit_freq, where freq is the desired scan frequency. Applying the sinusoidal sideband, as it's known, enables us to change the LO frequency without manually setting it when assembling the program.
def apply_sideband(pulse, freq):
"""Apply a sinusoidal sideband to this pulse at frequency freq.
Args:
pulse (SamplePulse): The pulse of interest.
freq (float): LO frequency for which we want to apply the sweep.
Return:
SamplePulse: Pulse with a sideband applied (oscillates at difference between freq and cal_qubit_freq).
"""
# time goes from 0 to dt*drive_samples, sine arg of form 2*pi*f*t
t_samples = np.linspace(0, dt*drive_samples, drive_samples)
sine_pulse = np.sin(2*np.pi*(freq-cal_qubit_freq)*t_samples) # no amp for the sine
# create sample pulse w/ sideband applied
# Note: need to make sq_pulse.samples real, multiply elementwise
sideband_pulse = SamplePulse(np.multiply(np.real(pulse.samples), sine_pulse), name='sideband_pulse')
return sideband_pulse
We wrap the logic for assembling the program in a method and run our program.
def create_excited_freq_sweep_program(freqs, drive_power):
"""Builds a program that does a freq sweep by exciting the |1> state.
This allows us to obtain the 1->2 frequency. We get from the |0> to |1>
state via a pi pulse using the calibrated qubit frequency. To do the
frequency sweep from |1> to |2>, we use a sideband method by tacking
a sine factor onto the sweep drive pulse.
Args:
freqs (np.ndarray(dtype=float)): Numpy array of frequencies to sweep.
drive_power (float) : Value of drive amplitude.
Raises:
ValueError: Thrown if use more than 75 frequencies; currently, an error will be thrown on the backend
if you try more than 75 frequencies.
Returns:
Qobj: Program for freq sweep experiment.
"""
if len(freqs) > 75:
raise ValueError("You can only run 75 schedules at a time.")
print(f"The frequency sweep will go from {freqs[0] / GHz} GHz to {freqs[-1]/ GHz} GHz \
using {len(freqs)} frequencies. The drive power is {drive_power}.")
base_12_pulse = pulse_lib.gaussian(duration=drive_samples,
sigma=drive_sigma,
amp=drive_power,
name='base_12_pulse')
schedules = []
for jj, freq in enumerate(freqs):
# add sideband to gaussian pulse
freq_sweep_12_pulse = apply_sideband(base_12_pulse, freq)
# add commands to schedule
schedule = pulse.Schedule(name="Frequency = {}".format(freq))
# Add 0->1 pulse, freq sweep pulse and measure
schedule |= pi_pulse_01(drive_chan)
schedule |= freq_sweep_12_pulse(drive_chan) << schedule.duration
schedule |= measure << schedule.duration # shift measurement to after drive pulses
schedules.append(schedule)
num_freqs = len(freqs)
# draw a schedule
display(schedules[-1].draw(channels_to_plot=[drive_chan, meas_chan], label=True, scaling=1.0))
# assemble freq sweep program
# Note: LO is at cal_qubit_freq for each schedule; accounted for by sideband
excited_freq_sweep_program = assemble(schedules,
backend=backend,
meas_level=1,
meas_return='avg',
shots=NUM_SHOTS,
schedule_los=[{drive_chan: cal_qubit_freq}]
* num_freqs)
return excited_freq_sweep_program
# sweep 400 MHz below 0->1 frequency to catch the 1->2 frequency
num_freqs = 75
excited_sweep_freqs = cal_qubit_freq + np.linspace(-400*MHz, 30*MHz, num_freqs)
excited_freq_sweep_program = create_excited_freq_sweep_program(excited_sweep_freqs, drive_power=0.3)
# Plot an example schedule to make sure it's valid
excited_freq_sweep_job = backend.run(excited_freq_sweep_program)
print(excited_freq_sweep_job.job_id())
job_monitor(excited_freq_sweep_job)
# Get job data (avg)
excited_freq_sweep_data = get_job_data(excited_freq_sweep_job, average=True)
# Note: we are only plotting the real part of the signal
plt.scatter(excited_sweep_freqs/GHz, excited_freq_sweep_data, color='black')
plt.xlim([min(excited_sweep_freqs/GHz)+0.01, max(excited_sweep_freqs/GHz)]) # ignore min point (is off)
plt.xlabel("Frequency [GHz]", fontsize=15)
plt.ylabel("Measured Signal [a.u.]", fontsize=15)
plt.title("1->2 Frequency Sweep (first pass)", fontsize=15)
plt.show()
We see a minimum around $4.64$ GHz. There are a few spurious maxima, but they are too large to be the $1\rightarrow2$ frequency. The minimum corresponds the $1\rightarrow2$ frequency.
Using a relative minima function, we computes the value of this point exactly. This gives an estimate for the $1\rightarrow2$ frequency.
# Prints out relative minima frequencies in output_data; height gives lower bound (abs val)
def rel_minima(freqs, output_data, height):
"""
Prints out relative minima frequencies in output_data (can see peaks); height gives upper bound (abs val).
Be sure to set the height properly or the peak will be ignored!
Args:
freqs (list): frequency list
output_data (list): list of resulting signals
height (float): upper bound (abs val) on a peak
Returns:
list: List containing relative minima frequencies
"""
peaks, _ = find_peaks(-1*output_data, height)
print("Freq. dips: ", freqs[peaks])
return freqs[peaks]
minima = rel_minima(excited_sweep_freqs, np.real(excited_freq_sweep_data), 10)
approx_12_freq = minima[0]
We now use the estimate obtained above to do a refined sweep (ie much smaller range). This will allow us to obtain a more accurate value for the $1\rightarrow2$ frequency. We sweep $20$ MHz in each direction.
# smaller range refined sweep
num_freqs = 75
refined_excited_sweep_freqs = approx_12_freq + np.linspace(-20*MHz, 20*MHz, num_freqs)
refined_excited_freq_sweep_program = create_excited_freq_sweep_program(refined_excited_sweep_freqs, drive_power=0.3)
refined_excited_freq_sweep_job = backend.run(refined_excited_freq_sweep_program)
print(refined_excited_freq_sweep_job.job_id())
job_monitor(refined_excited_freq_sweep_job)
# Get the refined data (average)
refined_excited_freq_sweep_data = get_job_data(refined_excited_freq_sweep_job, average=True)
Let's plot and fit the refined signal, using the standard Lorentzian curve.
# do fit in Hz
(refined_excited_sweep_fit_params,
refined_excited_sweep_y_fit) = fit_function(refined_excited_sweep_freqs,
refined_excited_freq_sweep_data,
lambda x, A, q_freq, B, C: (A / np.pi) * (B / ((x - q_freq)**2 + B**2)) + C,
[-12, 4.625*GHz, 0.05*GHz, 3*GHz] # initial parameters for curve_fit
)
# Note: we are only plotting the real part of the signal
plt.scatter(refined_excited_sweep_freqs/GHz, refined_excited_freq_sweep_data, color='black')
plt.plot(refined_excited_sweep_freqs/GHz, refined_excited_sweep_y_fit, color='red')
plt.xlim([min(refined_excited_sweep_freqs/GHz), max(refined_excited_sweep_freqs/GHz)])
plt.xlabel("Frequency [GHz]", fontsize=15)
plt.ylabel("Measured Signal [a.u.]", fontsize=15)
plt.title("1->2 Frequency Sweep (refined pass)", fontsize=15)
plt.show()
_, qubit_12_freq, _, _ = refined_excited_sweep_fit_params
print(f"Our updated estimate for the 1->2 transition frequency is "
f"{round(qubit_12_freq/GHz, 7)} GHz.")
Now that we have a good estimate for the $1\rightarrow2$ frequency, we perform a Rabi experiment to obtain the $\pi$ pulse amplitude for the $1\rightarrow2$ transition. To do so, we apply a $0\rightarrow1$ $\pi$ pulse and then sweep over drive amplitudes at the $1\rightarrow2$ frequency (using the sideband method).
# experimental configuration
num_rabi_points = 75 # number of experiments (ie amplitudes to sweep out)
# Drive amplitude values to iterate over: 75 amplitudes evenly spaced from 0 to 1.0
drive_amp_min = 0
drive_amp_max = 1.0
drive_amps = np.linspace(drive_amp_min, drive_amp_max, num_rabi_points)
# Create schedule
rabi_12_schedules = []
# loop over all drive amplitudes
for ii, drive_amp in enumerate(drive_amps):
base_12_pulse = pulse_lib.gaussian(duration=drive_samples,
sigma=drive_sigma,
amp=drive_amp,
name='base_12_pulse')
# apply sideband at the 1->2 frequency
rabi_12_pulse = apply_sideband(base_12_pulse, qubit_12_freq)
# add commands to schedule
schedule = pulse.Schedule(name='Rabi Experiment at drive amp = %s' % drive_amp)
schedule |= pi_pulse_01(drive_chan) # 0->1
schedule |= rabi_12_pulse(drive_chan) << schedule.duration # 1->2 Rabi pulse
schedule |= measure << schedule.duration # shift measurement to after drive pulse
rabi_12_schedules.append(schedule)
# Assemble the schedules into a program
# Note: The LO frequency is at cal_qubit_freq to support the 0->1 pi pulse;
# it is modified for the 1->2 pulse using sidebanding
rabi_12_expt_program = assemble(rabi_12_schedules,
backend=backend,
meas_level=1,
meas_return='avg',
shots=NUM_SHOTS,
schedule_los=[{drive_chan: cal_qubit_freq}]
* num_rabi_points)
rabi_12_job = backend.run(rabi_12_expt_program)
print(rabi_12_job.job_id())
job_monitor(rabi_12_job)
# Get the job data (average)
rabi_12_data = get_job_data(rabi_12_job, average=True)
We plot and fit our data as before.
# Note: We only plot the real part of the signal.
rabi_12_data = np.real(baseline_remove(rabi_12_data))
(rabi_12_fit_params,
rabi_12_y_fit) = fit_function(drive_amps,
rabi_12_data,
lambda x, A, B, drive_12_period, phi: (A*np.cos(2*np.pi*x/drive_12_period - phi) + B),
[3, 0.5, 0.9, 0])
plt.scatter(drive_amps, rabi_12_data, color='black')
plt.plot(drive_amps, rabi_12_y_fit, color='red')
drive_12_period = rabi_12_fit_params[2]
# account for phi in computing pi amp
pi_amp_12 = (drive_12_period/2/np.pi) *(np.pi+rabi_12_fit_params[3])
plt.axvline(pi_amp_12, color='red', linestyle='--')
plt.axvline(pi_amp_12+drive_12_period/2, color='red', linestyle='--')
plt.annotate("", xy=(pi_amp_12+drive_12_period/2, 0), xytext=(pi_amp_12,0), arrowprops=dict(arrowstyle="<->", color='red'))
plt.annotate("$\pi$", xy=(pi_amp_12-0.03, 0.1), color='red')
plt.xlabel("Drive amp [a.u.]", fontsize=15)
plt.ylabel("Measured signal [a.u.]", fontsize=15)
plt.title('Rabi Experiment (1->2)', fontsize=20)
plt.show()
print(f"Pi Amplitude (1->2) = {pi_amp_12}")
With this information, we can define our $1\rightarrow2$ $\pi$ pulse (making sure to add a sideband at the $1\rightarrow2$ frequency).
pi_pulse_12 = pulse_lib.gaussian(duration=drive_samples,
amp=pi_amp_12,
sigma=drive_sigma,
name='pi_pulse_12')
# make sure this pulse is sidebanded
pi_pulse_12 = apply_sideband(pi_pulse_12, qubit_12_freq)
Finally, we build our discriminator for the $|0\rangle$, $|1\rangle$ and $|2\rangle$ states. The procedure is analagous to section 1, however now we add an additional schedule for the $|2\rangle$ state.
As a review, our three schedules are (again, recalling that our system starts in the $|0\rangle$ state):
- Measure the $|0\rangle$ state directly (obtain $|0\rangle$ centroid).
- Apply $0\rightarrow1$ $\pi$ pulse and then measure (obtain $|1\rangle$ centroid).
- Apply $0\rightarrow1$ $\pi$ pulse, then $1\rightarrow2$ $\pi$ pulse, then measure (obtain $|2\rangle$ centroid).
# Create the three schedules
# Ground state schedule
zero_schedule = pulse.Schedule(name="zero schedule")
zero_schedule |= measure
# Excited state schedule
one_schedule = pulse.Schedule(name="one schedule")
one_schedule |= pi_pulse_01(drive_chan)
one_schedule |= measure << one_schedule.duration
# Excited state schedule
two_schedule = pulse.Schedule(name="two schedule")
two_schedule |= pi_pulse_01(drive_chan)
two_schedule |= pi_pulse_12(drive_chan) << two_schedule.duration
two_schedule |= measure << two_schedule.duration
We construct the program and plot the centroids in the IQ plane.
# Assemble the schedules into a program
IQ_012_program = assemble([zero_schedule, one_schedule, two_schedule],
backend=backend,
meas_level=1,
meas_return='single',
shots=NUM_SHOTS,
schedule_los=[{drive_chan: cal_qubit_freq}] * 3)
IQ_012_job = backend.run(IQ_012_program)
print(IQ_012_job.job_id())
job_monitor(IQ_012_job)
# Get job data (single); split for zero, one and two
IQ_012_data = get_job_data(IQ_012_job, average=False)
zero_data = IQ_012_data[0]
one_data = IQ_012_data[1]
two_data = IQ_012_data[2]
def IQ_012_plot(x_min, x_max, y_min, y_max):
"""Helper function for plotting IQ plane for 0, 1, 2. Limits of plot given
as arguments."""
# zero data plotted in blue
plt.scatter(np.real(zero_data), np.imag(zero_data),
s=5, cmap='viridis', c='blue', alpha=0.5, label=r'$|0\rangle$')
# one data plotted in red
plt.scatter(np.real(one_data), np.imag(one_data),
s=5, cmap='viridis', c='red', alpha=0.5, label=r'$|1\rangle$')
# two data plotted in green
plt.scatter(np.real(two_data), np.imag(two_data),
s=5, cmap='viridis', c='green', alpha=0.5, label=r'$|2\rangle$')
# Plot a large dot for the average result of the 0, 1 and 2 states.
mean_zero = np.mean(zero_data) # takes mean of both real and imaginary parts
mean_one = np.mean(one_data)
mean_two = np.mean(two_data)
plt.scatter(np.real(mean_zero), np.imag(mean_zero),
s=200, cmap='viridis', c='black',alpha=1.0)
plt.scatter(np.real(mean_one), np.imag(mean_one),
s=200, cmap='viridis', c='black',alpha=1.0)
plt.scatter(np.real(mean_two), np.imag(mean_two),
s=200, cmap='viridis', c='black',alpha=1.0)
plt.xlim(x_min, x_max)
plt.ylim(y_min,y_max)
plt.legend()
plt.ylabel('I [a.u.]', fontsize=15)
plt.xlabel('Q [a.u.]', fontsize=15)
plt.title("0-1-2 discrimination", fontsize=15)
x_min = -20
x_max = 10
y_min = -10
y_max = 5
IQ_012_plot(x_min, x_max, y_min, y_max)
We now observe a third centroid corresponding to the $|2\rangle$ state. (Note: If the plot looks off, rerun the notebook)
With this data, we can build our discriminator. Again, we use scikit.learn and Linear Discriminant Analysis (LDA).
We begin by shaping the data for LDA.
# Create IQ vector (split real, imag parts)
zero_data_reshaped = reshape_complex_vec(zero_data)
one_data_reshaped = reshape_complex_vec(one_data)
two_data_reshaped = reshape_complex_vec(two_data)
IQ_012_data = np.concatenate((zero_data_reshaped, one_data_reshaped, two_data_reshaped))
print(IQ_012_data.shape) # verify IQ data shape
Next, we split our training and testing data (again, half and half). The testing data is a vector containing an array of 0's (for the zero schedule, 1's (for the one schedule) and 2's (for the two schedule).
# construct vector w/ 0's, 1's and 2's (for testing)
state_012 = np.zeros(NUM_SHOTS) # shots gives number of experiments
state_012 = np.concatenate((state_012, np.ones(NUM_SHOTS)))
state_012 = np.concatenate((state_012, 2*np.ones(NUM_SHOTS)))
print(len(state_012))
# Shuffle and split data into training and test sets
IQ_012_train, IQ_012_test, state_012_train, state_012_test = train_test_split(IQ_012_data, state_012, test_size=0.5)
Finally, we set up our model and train it. The accuracy of our fit is printed.
# Set up the LDA
LDA_012 = LinearDiscriminantAnalysis()
LDA_012.fit(IQ_012_train, state_012_train)
# test on some simple data
print(LDA_012.predict([[0, 0], [-10, 0], [-15, -5]]))
# Compute accuracy
score_012 = LDA_012.score(IQ_012_test, state_012_test)
print(score_012)
The last step is to plot the separatrix.
IQ_012_plot(x_min, x_max, y_min, y_max)
separatrixPlot(LDA_012, x_min, x_max, y_min, y_max, NUM_SHOTS)
Now that we have 3 centroids, the separatrix is no longer a line, but rather a curve containing a combination of two lines. In order to discriminate between $|0\rangle$, $|1\rangle$ and $|2\rangle$ states, our model checks where the IQ point lies relative to the separatrix and classifies the point accordingly.
- D. C. McKay, T. Alexander, L. Bello, M. J. Biercuk, L. Bishop, J. Chen, J. M. Chow, A. D. C ́orcoles, D. Egger, S. Filipp, J. Gomez, M. Hush, A. Javadi-Abhari, D. Moreda, P. Nation, B. Paulovicks, E. Winston, C. J. Wood, J. Wootton, and J. M. Gambetta, “Qiskit backend specifications for OpenQASM and OpenPulse experiments,” 2018, https://arxiv.org/abs/1809.03452.
- Krantz, P. et al. “A Quantum Engineer’s Guide to Superconducting Qubits.” Applied Physics Reviews 6.2 (2019): 021318, https://arxiv.org/abs/1904.06560.
- Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011, https://scikit-learn.org/stable/modules/lda_qda.html#id4.
import qiskit.tools.jupyter
%qiskit_version_table