{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bringup Experiments\n", "In this tutorial notebook, we will review common pulse sequences that are used as first steps during the bring up of more complex pulse experiments:\n", "- measure the resonance frequency of a qubit\n", "- calibrate a $\\pi$/2 pulse via Rabi spectroscopy\n", "- measure the $T^*_2$ coherence time with a Ramsey sequence\n", "\n", "You can use either Rigetti's Aspen M-3 or OQC's Lucy device to run this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", "from braket.tracking import Tracker\n", "t = Tracker().start()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first import some packages to construct pulse sequences and analyze results." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from braket.aws import AwsDevice\n", "from braket.pulse import PulseSequence, GaussianWaveform, ConstantWaveform\n", "from braket.parametric import FreeParameter\n", "\n", "## Imports for function fitting\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.optimize\n", "from scipy.fft import fft, fftfreq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will be able to switch from one device to the other by setting the `device_name` to `aspen` or `lucy`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "device_name = \"aspen\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the following configuration to control the different parameters for our experiments across the available devices" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "experiment_configuration = {\n", " \"aspen\": {\n", " \"device_arn\": \"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3\",\n", " \"qubit\": 4,\n", " \"drive_frame\": \"q4_rf_frame\",\n", " \"readout_frame\": \"q4_ro_rx_frame\",\n", " \"spectroscopy_wf\": GaussianWaveform(100e-9, 25e-9, 0.1, True),\n", " \"rabi_wf\": GaussianWaveform(FreeParameter(\"length\"), FreeParameter(\"length\") * 0.25, 0.2, True)\n", " },\n", " \"lucy\": {\n", " \"device_arn\": \"arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy\",\n", " \"qubit\": 0,\n", " \"drive_frame\": \"q0_drive\",\n", " \"readout_frame\": \"r0_measure\",\n", " \"spectroscopy_wf\": ConstantWaveform(25e-9, 0.03),\n", " \"rabi_wf\": ConstantWaveform(FreeParameter(\"length\"), 0.07)\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will first instantiate a device that will provide access to some properties such as frames" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "device = AwsDevice(experiment_configuration[device_name][\"device_arn\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With both devices, frames are predefined in the device capabilities and can be loaded with the Amazon Braket SDK. In this notebook, we will drive a single qubit, which only requires the frames:\n", "- `q4_rf_frame` to drive the qubit and `q4_ro_rx_frame` to measure it. (Rigetti Aspen M-3)\n", "- `q0_drive` to drive the qubit and `r0_measure` to measure it. (OQC Lucy) \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "qubit = experiment_configuration[device_name][\"qubit\"]\n", "drive_frame = device.frames[experiment_configuration[device_name][\"drive_frame\"]]\n", "readout_frame = device.frames[experiment_configuration[device_name][\"readout_frame\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Qubit spectropscopy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Qubit spectroscopy is one of the first step to charaterize a qubit. While this information is already present in the frame properties, we will build a pulse sequence to estimate the transition frequency between the ground state and the first excited state.\n", "\n", "For simplicity, we will use Gaussian or Constant waveforms as envelopes of the different pulses. The Gaussian waveforms are parametrized by their amplitude $A$ and the length $d$ of their pulse window. They are positioned at the center of the window ($d$/2) and their width (1/e) will be set to be a quarter of the window length ($d$/4). The Constant waveforms have a complex amplitude $iq$ . \n", "\n", "For qubit spectroscopy, while a prior knowledge of the systems helps choosing these parameters to increase the signal-to-noise ratio, it is not necessary to tune them precisely. \n", "\n", "With Aspen, we will use a pulse length of 100ns, the Gaussian has a width of 25ns and its amplitude is 0.1. With Lucy, We will use a pulse length of 25ns and an amplitude is 0.03. The amplitude unit should be considered as arbitrary, the maximum amplitude can be retrieved from the device capabilities, please see the documentation for more details." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "waveform = experiment_configuration[device_name][\"spectroscopy_wf\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "The pulse sequence below contains three instructions:\n", "-\tThe first sets the frequency of the microwave signal to a chosen frequency among the range of frequency to probe\n", "-\tThe second plays the waveform.\n", "-\tThe third instruction is a readout instruction. Measurements are realized via the predefined function capture_v0() that executes a projective measurement of the qubit and returns the projected eigenstate.\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "frequency = FreeParameter(\"frequency\")\n", "\n", "pulse_sequence = ( \n", " PulseSequence()\n", " .set_frequency(drive_frame, frequency)\n", " .play(drive_frame, waveform)\n", " .capture_v0(readout_frame)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will sweep the frequency over a range that is centered around the expected transition frequency. For that, we use the FreeParameter object that we defined in the previous cell and create a new pulse sequence by binding the FreeParameter to each tested frequency.\n", "\n", "We then run our batch of pulse sequence with `device.run_batch`, which returns a batch of quantum tasks." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "span = 75e6\n", "N_steps = 25\n", "N_shots = 100\n", "frequencies = np.linspace(drive_frame.frequency-span/2, drive_frame.frequency+span/2, N_steps)\n", "\n", "qubit_spectroscopy_sequences = [pulse_sequence(frequency=frequency) for frequency in frequencies]\n", "batch = device.run_batch(qubit_spectroscopy_sequences, shots=N_shots)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After executing this batch of tasks, we are ready to analyze the results. We will use a simple Gaussian fit function to extract the transition frequency." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def resonance_fit(x, A, A0, w, x0):\n", " return A0-A*np.exp(-(x-x0)**2/w**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result format is the same as with circuits, which means that `result()` will return a task result object that includes a counter with the number of occurences for each eigenstate of the measurement basis. Since we have been using a batch, we can quickly construct the probability to measure the state $|0\\rangle$. \n", "\n", "The data are then plotted and fitted with the previously defined fit function." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expected resonance frequency: 4728.33 GHz\n", "Measured resonance frequency: 4726.44 GHz\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "probability_of_zero = [result.measurement_counts['0']/N_shots for result in batch.results()]\n", "\n", "x, y = frequencies, probability_of_zero\n", "\n", "initial_guess=[1/2, 1, 10e6, drive_frame.frequency] # Amplitude, Offset, width, centerFrequency\n", "optimal_params, _ = scipy.optimize.curve_fit(resonance_fit, x, y, p0=initial_guess)\n", "x_fit = np.arange(x[0],x[-1], np.diff(x)[0]/10)\n", "y_fit = resonance_fit(x_fit, *optimal_params)\n", "\n", "plt.figure()\n", "plt.plot(x,y, 'o')\n", "plt.plot(x_fit,y_fit)\n", "plt.xlabel(\"Frequency (Hz)\")\n", "plt.ylabel(\"Population\")\n", "plt.vlines(drive_frame.frequency, min(y_fit), 1)\n", "resonance_frequency = optimal_params[3]\n", "print('Expected resonance frequency:', round(drive_frame.frequency*1e-6,2), 'GHz')\n", "print('Measured resonance frequency:', round(resonance_frequency*1e-6,2), 'GHz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calibrating $\\pi$/2 pulses via Rabi spectroscopy \n", "\n", "Applying an electromagnetic field to a qubit leads to Rabi flopping, a cyclic behavior that is used to calibrate specific pulse parameters. Here, we will determine the optimal pulse length to realize a $\\pi$/2 pulse, an elementary block used to build more complex pulse sequences, such as the Ramsey sequence. \n", "\n", "Below, we will reuse the previous pulse sequence. First, we will fix the driving frequency to the resonance frequency and replace the coarsely-chosen pulse length by a FreeParameter.\n", "\n", "We also choose to increase the amplitude of the waveform to increase the rate of the expected oscillations.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "waveform = experiment_configuration[device_name][\"rabi_wf\"]\n", "\n", "rabi_sequence = ( \n", " PulseSequence()\n", " .set_frequency(drive_frame, drive_frame.frequency)\n", " .play(drive_frame, waveform)\n", " .capture_v0(readout_frame)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As for qubit spectroscopy, we will sweep one parameter of our pulse sequence, here the length of the waveform.\n", "\n", "We construct another batch of task with lengths ranging from 12ns to 200ns with a step of 12ns so all the sequences verify the 4-sample constraint (waveforms must have a length multiple of 4ns). " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "start_length=12e-9\n", "end_length=2e-7\n", "lengths = np.arange(start_length, end_length, 12e-9)\n", "N_shots = 100\n", "\n", "pulse_sequences = [rabi_sequence(length=length) for length in lengths]\n", "\n", "batch = device.run_batch(pulse_sequences, shots=N_shots)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To increase the chances of fitting success, we estimate some initial parameters values:\n", "- initial signal mean is taken as the mean of the measurement data\n", "- initial Rabi frequency is set as the position of the maximum value of the measurement data's FFT" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def damped_oscillation_fit(x, A, A0, Tau, f, x0):\n", " return A*np.exp(-x/Tau)*np.cos(2*np.pi*(x-x0)*f)+A0\n", "\n", "def estimate_fit_parameters(x, y):\n", " signal_mean = np.mean(y)\n", " idx_max=np.argmax(np.abs(fft(y-np.mean(y))))\n", " oscillation_frequency_estimate = fftfreq(len(x), np.diff(x)[0])[idx_max]\n", "\n", " return signal_mean, oscillation_frequency_estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The projected state after the qubit measurement reflects the oscillatory dynamics of the qubit that oscillates between the state $|0\\rangle$ and the state $|1\\rangle$. \n", "From the measurement data, one can extract the Rabi frequency as the flipping rate and compute the length of $\\pi/2$ pulse." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rabi frequency: 13.13 MHz\n", "rx(pi/2) length: 17.62 ns\n", "Redefined rx(pi/2) length: 16.0 ns\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "probability_of_zero = [result.measurement_counts['0']/N_shots for result in batch.results()]\n", "x, y = lengths, probability_of_zero\n", "\n", "signal_mean, oscillation_frequency_estimate = estimate_fit_parameters(x,y)\n", "\n", "initial_guess=[1/2, signal_mean, 8e-6, oscillation_frequency_estimate, 0]\n", "optimal_params, _ = scipy.optimize.curve_fit(damped_oscillation_fit, x, y, p0=initial_guess)\n", "x_fit = np.arange(x[0],x[-1], np.diff(x)[0]/10)\n", "y_fit = damped_oscillation_fit(x_fit, *optimal_params)\n", "\n", "plt.plot(x,y, 'o')\n", "plt.plot(x_fit,y_fit)\n", "plt.xlabel(\"Pulse duration (s)\")\n", "plt.ylabel(\"Population\")\n", "\n", "\n", "rabi_frequency = optimal_params[3]\n", "phase_offset = optimal_params[4]\n", "x90_duration=(np.pi/2)/(2*np.pi*rabi_frequency)+phase_offset\n", "plt.plot(x90_duration, damped_oscillation_fit(x90_duration, *optimal_params), 'ks')\n", "\n", "print('Rabi frequency:', round(rabi_frequency*1e-6, 2), ' MHz')\n", "print('rx(pi/2) length: ', round(x90_duration*1e9, 2), ' ns')\n", "\n", "# Pulse duration must be a multiple of 4ns\n", "x90_duration=x90_duration//4e-9*4e-9\n", "plt.plot(x90_duration, damped_oscillation_fit(x90_duration, *optimal_params), 'rs')\n", "print('Redefined rx(pi/2) length: ', round(x90_duration*1e9, 0), ' ns')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "experiment_configuration[\"aspen\"][\"x90_wf\"] = GaussianWaveform(x90_duration, x90_duration/4, 0.2, True)\n", "experiment_configuration[\"lucy\"][\"x90_wf\"] = ConstantWaveform(x90_duration, 0.07)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ramsey sequence - $T^*_2$ Measurement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following, we implement a Ramsey sequence that allows you to measure the coherence time of a qubit. The Ramsey sequence consists of two $\\pi$/2 pulses separated by a varying gap. Starting from the $|0\\rangle$ state, the first pulse creates an equal superposition of $|0\\rangle$ and $|1\\rangle$ which will decay back to a mixed state after a characteristic time called $T^*_2$. \n", "\n", "For better visualization, the carrier frequency is shifted away from the resonance frequency by some arbitraryily-chosen detuning which causes the frame of the driving to rotate with respect to the qubit frame. This results in oscillations at a rate equal to the detuning on top of the decoherence decay. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "detuning=250e3\n", "x90 = experiment_configuration[device_name][\"x90_wf\"]\n", "\n", "delay = FreeParameter(\"delay\")\n", "ramsey_spectroscopy = ( \n", " PulseSequence()\n", " .set_frequency(drive_frame, drive_frame.frequency - detuning)\n", " .play(drive_frame, x90)\n", " .delay(drive_frame, delay)\n", " .play(drive_frame, x90)\n", " .capture_v0(readout_frame)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, we will sweep a parameter of the sequence which is the time gap between the two $\\pi$/2-pulses. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "start_delay=12e-9\n", "end_delay=40000e-9\n", "delays = np.arange(start_delay, end_delay, 512e-9)\n", "N_shots=100\n", "\n", "pulse_sequences = [ramsey_spectroscopy(delay=delay) for delay in delays]\n", "\n", "batch = device.run_batch(pulse_sequences, shots=N_shots)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting and fitting are not different from what we have seen with Rabi oscillations. We can now experimentally verify that the oscillations correspond to the frequency detuning of our pulses, and we can extract the coherence time $T^*_2$ of the qubit " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Detuning: 258.53 kHz\n", "T2: 33.33 us\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "probability_of_zero = [result.measurement_counts['0']/N_shots for result in batch.results()]\n", "x, y = delays, probability_of_zero\n", "plt.plot(x,y, 'o-')\n", "\n", "signal_mean, oscillation_frequency_estimate = estimate_fit_parameters(x,y)\n", "initial_guess=[0.5, signal_mean, 2e-5, oscillation_frequency_estimate, 0]\n", "\n", "optimal_params, _ = scipy.optimize.curve_fit(damped_oscillation_fit, x, y, p0=initial_guess)\n", "x_fit = np.arange(x[0],x[-1], np.diff(x)[0]/10)\n", "y_fit = damped_oscillation_fit(x_fit, *optimal_params)\n", "plt.plot(x_fit,y_fit)\n", "plt.xlabel(\"Delay (s)\")\n", "plt.ylabel(\"Population\")\n", "print('Detuning:', round(optimal_params[3]*1e-3, 2), ' kHz')\n", "print('T2: ', round(np.abs(optimal_params[2])*1e6, 2), ' us')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Task Summary\n", "{'arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-3': {'shots': 12000, 'tasks': {'COMPLETED': 120}}}\n", "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", "Estimated cost to run this example: 40.200 USD\n" ] } ], "source": [ "print(\"Task Summary\")\n", "print(t.quantum_tasks_statistics())\n", "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", "print(f\"Estimated cost to run this example: {t.qpu_tasks_cost() + t.simulator_tasks_cost():.3f} USD\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.14", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" }, "vscode": { "interpreter": { "hash": "e8fe7b1d737818ec041fd05b4c8bbd1804e351a931e38c7cd860a34c69554183" } } }, "nbformat": 4, "nbformat_minor": 2 }