ternary_superposition_gate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Ternary Superposition Gate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "Original qiskit contents (used for calibrating frequency / contruction of $\\pi$ pulse) <br>\n",
    "1. [Getting Started](#importing)\n",
    "2. [Finding the qubit Frequency using a Frequency Sweep](#frequencysweep)\n",
    "3. [Calibrating and using a $\\pi$ Pulse](#calib)  \n",
    "--------------------------------------\n",
    "Addition for our project: <br>\n",
    "4. [Attempt on Creating a Ternary Superposition Gate](#sgate) <br>\n",
    "5. [References](#refs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Getting Started <a id=\"importing\"></a>\n",
    "\n",
    "We'll first get our basic dependencies set up and ready to go. Since we want to use real, noisy devices for our calibration experiments in this notebook, we need to load our IBMQ account and set the appropriate backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "from qiskit.tools.jupyter import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "from qiskit import IBMQ\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n",
    "backend = provider.get_backend('ibmq_armonk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We verify that the backend supports Pulse features by checking the backend configuration. The config provides us with general information about the structure of the backend setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "backend_config = backend.configuration()\n",
    "assert backend_config.open_pulse, \"Backend doesn't support Pulse\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance, we can find the sampling time for the backend pulses within the backend configuration. This will be a very useful value to us as we build and execute our calibration routines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling time: 0.2222222222222222 ns\n"
     ]
    }
   ],
   "source": [
    "dt = backend_config.dt\n",
    "print(f\"Sampling time: {dt*1e9} ns\")    # The configuration returns dt in seconds, so multiply by\n",
    "                                        # 1e9 to get nanoseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The backend defaults provide a starting point for how to use the backend. It contains estimates for qubit frequencies and default programs to enact basic quantum operators. We can access them with the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false,
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "backend_defaults = backend.defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Finding the qubit Frequency using a Frequency Sweep <a id=\"frequencysweep\"></a>\n",
    "\n",
    "We begin by searching for the qubit frequency. The qubit frequency is the difference in energy between the ground and excited states, which we label the $\\vert0\\rangle$ and $\\vert1\\rangle$ states, respectively. This frequency will be crucial for creating pulses which enact particular quantum operators on the qubit -- the final goal of our calibration!\n",
    "\n",
    "With superconducting qubits, higher energy levels are also available, but we fabricate the systems to be anharmonic so that we can control which transition we are exciting. That way, we are able to isolate two energy levels and treat each qubit as a basic two-level system, ignoring higher energy states.\n",
    "\n",
    "In a typical lab setting, the qubit frequency can be found by sweeping a range of frequencies and looking for signs of absorption using a tool known as a Network Analyzer. This measurement gives a rough estimate of the qubit frequency. Later on, we will see how to do a more precise measurement using a Ramsey pulse sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define the frequency range that will be swept in search of the qubit. Since this can be arbitrarily broad, we restrict ourselves to a window of 40 MHz around the estimated qubit frequency in `backend_defaults`. We step the frequency in units of 1 MHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "uses-hardware"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qubit 0 has an estimated frequency of 4.971855969373953 GHz.\n",
      "The sweep will go from 4.951855969373953 GHz to 4.991855969373953 GHz in steps of 1.0 MHz.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# unit conversion factors -> all backend properties returned in SI (Hz, sec, etc)\n",
    "GHz = 1.0e9 # Gigahertz\n",
    "MHz = 1.0e6 # Megahertz\n",
    "us = 1.0e-6 # Microseconds\n",
    "ns = 1.0e-9 # Nanoseconds\n",
    "\n",
    "# We will find the qubit frequency for the following qubit.\n",
    "qubit = 0\n",
    "\n",
    "# The sweep will be centered around the estimated qubit frequency.\n",
    "center_frequency_Hz = backend_defaults.qubit_freq_est[qubit]        # The default frequency is given in Hz\n",
    "                                                                    # warning: this will change in a future release\n",
    "print(f\"Qubit {qubit} has an estimated frequency of {center_frequency_Hz / GHz} GHz.\")\n",
    "\n",
    "# scale factor to remove factors of 10 from the data\n",
    "scale_factor = 1e-14\n",
    "\n",
    "# We will sweep 40 MHz around the estimated frequency\n",
    "frequency_span_Hz = 40 * MHz\n",
    "# in steps of 1 MHz.\n",
    "frequency_step_Hz = 1 * MHz\n",
    "\n",
    "# We will sweep 20 MHz above and 20 MHz below the estimated frequency\n",
    "frequency_min = center_frequency_Hz - frequency_span_Hz / 2\n",
    "frequency_max = center_frequency_Hz + frequency_span_Hz / 2\n",
    "# Construct an np array of the frequencies for our experiment\n",
    "frequencies_GHz = np.arange(frequency_min / GHz, \n",
    "                            frequency_max / GHz, \n",
    "                            frequency_step_Hz / GHz)\n",
    "\n",
    "print(f\"The sweep will go from {frequency_min / GHz} GHz to {frequency_max / GHz} GHz \\\n",
    "in steps of {frequency_step_Hz / MHz} MHz.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the pulses we will use for our experiment. We will start with the drive pulse, which is a Gaussian pulse.\n",
    "\n",
    "Remember the value `dt` from earlier? All durations in pulse are given in terms of `dt`. In the next cell, we define the length of the drive pulse in terms of `dt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# samples need to be multiples of 16\n",
    "def get_closest_multiple_of_16(num):\n",
    "    return int(num + 8 ) - (int(num + 8 ) % 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "from qiskit import pulse            # This is where we access all of our Pulse features!\n",
    "from qiskit.pulse import Play\n",
    "# This Pulse module helps us build sampled pulses for common pulse shapes\n",
    "from qiskit.pulse import library as pulse_lib\n",
    "\n",
    "\n",
    "# Drive pulse parameters (us = microseconds)\n",
    "drive_sigma_us = 0.075                     # This determines the actual width of the gaussian\n",
    "drive_samples_us = drive_sigma_us*8        # This is a truncating parameter, because gaussians don't have \n",
    "                                           # a natural finite length\n",
    "\n",
    "drive_sigma = get_closest_multiple_of_16(drive_sigma_us * us /dt)       # The width of the gaussian in units of dt\n",
    "drive_samples = get_closest_multiple_of_16(drive_samples_us * us /dt)   # The truncating parameter in units of dt\n",
    "drive_amp = 0.05\n",
    "# Drive pulse samples\n",
    "drive_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                 sigma=drive_sigma,\n",
    "                                 amp=drive_amp,\n",
    "                                 name='freq_sweep_excitation_pulse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to properly measure our qubits, we need to check the measurement map. This is a hardware constraint. When acquisition is done for one qubit, it is also done on other qubits. We have to respect this constraint when building our program in Pulse. Let's check which group of qubits our qubit is in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# Find out which group of qubits need to be acquired with this qubit\n",
    "meas_map_idx = None\n",
    "for i, measure_group in enumerate(backend_config.meas_map):\n",
    "    if qubit in measure_group:\n",
    "        meas_map_idx = i\n",
    "        break\n",
    "assert meas_map_idx is not None, f\"Couldn't find qubit {qubit} in the meas_map!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define our measurement pulse. Rather than hard coding the pulse, we can obtain a calibrated measurement pulse from the backend default instruction schedule map. Because it is frequently calibrated, it is more accurate than defining a measurement pulse ourselves. This measurement pulse also includes acquisition so we do not need to add that in manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "inst_sched_map = backend_defaults.instruction_schedule_map\n",
    "measure = inst_sched_map.get('measure', qubits=backend_config.meas_map[meas_map_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we specify the channels on which we will apply our pulses. Drive, measure, and acquire channels are indexed by qubit index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "### Collect the necessary channels\n",
    "drive_chan = pulse.DriveChannel(qubit)\n",
    "meas_chan = pulse.MeasureChannel(qubit)\n",
    "acq_chan = pulse.AcquireChannel(qubit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the pulse parameters have been defined, and we have created the pulse shapes for our experiments, we can proceed to create the pulse schedules.\n",
    "\n",
    "\n",
    "\n",
    "At each frequency, we will send a drive pulse of that frequency to the qubit and measure immediately after the pulse. The pulse envelopes are independent of frequency, so we will build a reusable `schedule`, and we will specify the drive pulse frequency with a frequency configuration array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "uses-hardware"
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   "outputs": [],
   "source": [
    "# Create the base schedule\n",
    "# Start with drive pulse acting on the drive channel\n",
    "schedule = pulse.Schedule(name='Frequency sweep')\n",
    "schedule += Play(drive_pulse, drive_chan)\n",
    "# The left shift `<<` is special syntax meaning to shift the start time of the schedule by some duration\n",
    "schedule += measure << schedule.duration\n",
    "\n",
    "# Create the frequency settings for the sweep (MUST BE IN HZ)\n",
    "frequencies_Hz = frequencies_GHz*GHz\n",
    "schedule_frequencies = [{drive_chan: freq} for freq in frequencies_Hz]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, it's always a good idea to look at the pulse schedule. This is done using `schedule.draw()` as shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false,
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hyunjinkim/opt/anaconda3/envs/qiskit_env/lib/python3.9/site-packages/qiskit/pulse/schedule.py:1534: DeprecationWarning: Legacy pulse drawer is deprecated. Specified arguments label are deprecated. Please check the API document of new pulse drawer `qiskit.visualization.pulse_drawer_v2`.\n",
      "  warnings.warn('Legacy pulse drawer is deprecated. '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x241.987 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule.draw(label=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assemble the `schedules` and `schedule_frequencies` above into a program object, called a Qobj, that can be sent to the quantum device. We request that each schedule (each point in our frequency sweep) is repeated `num_shots_per_frequency` times in order to get a good estimate of the qubit response.\n",
    "\n",
    "We also specify measurement settings. `meas_level=0` returns raw data (an array of complex values per shot), `meas_level=1` returns kerneled data (one complex value per shot), and `meas_level=2` returns classified data (a 0 or 1 bit per shot). We choose `meas_level=1` to replicate what we would be working with if we were in the lab, and hadn't yet calibrated the discriminator to classify 0s and 1s. We ask for the `'avg'` of the results, rather than each shot individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "from qiskit import assemble\n",
    "\n",
    "num_shots_per_frequency = 1024\n",
    "frequency_sweep_program = assemble(schedule,\n",
    "                                   backend=backend, \n",
    "                                   meas_level=1,\n",
    "                                   meas_return='avg',\n",
    "                                   shots=num_shots_per_frequency,\n",
    "                                   schedule_los=schedule_frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may see yet another unit change warning, we can safely ignore this. Finally, we can run the assembled program on the backend using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-b1426b634c8f>:1: DeprecationWarning: Passing a Qobj to Backend.run is deprecated and will be removed in a future release. Please pass in circuits or pulse schedules instead.\n",
      "  job = backend.run(frequency_sweep_program)\n"
     ]
    }
   ],
   "source": [
    "job = backend.run(frequency_sweep_program)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is always a good idea to print the `job_id` for later retrieval, and to monitor the job status by using `job_monitor()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "# print(job.job_id())\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the job is run, the results can be retrieved using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "frequency_sweep_results = job.result(timeout=120) # timeout parameter set to 120 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will extract the results and plot them using `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sweep_values = []\n",
    "for i in range(len(frequency_sweep_results.results)):\n",
    "    # Get the results from the ith experiment\n",
    "    res = frequency_sweep_results.get_memory(i)*scale_factor\n",
    "    # Get the results for `qubit` from this experiment\n",
    "    sweep_values.append(res[qubit])\n",
    "\n",
    "plt.scatter(frequencies_GHz, np.real(sweep_values), color='black') # plot real part of sweep values\n",
    "plt.xlim([min(frequencies_GHz), max(frequencies_GHz)])\n",
    "plt.xlabel(\"Frequency [GHz]\")\n",
    "plt.ylabel(\"Measured signal [a.u.]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, the peak near the center corresponds to the location of the qubit frequency. The signal shows power-broadening, which is a signature that we are able to drive the qubit off-resonance as we get close to the center frequency. To get the value of the peak frequency, we will fit the values to a resonance response curve, which is typically a Lorentzian shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def fit_function(x_values, y_values, function, init_params):\n",
    "    fitparams, conv = curve_fit(function, x_values, y_values, init_params)\n",
    "    y_fit = function(x_values, *fitparams)\n",
    "    \n",
    "    return fitparams, y_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "fit_params, y_fit = fit_function(frequencies_GHz,\n",
    "                                 np.real(sweep_values), \n",
    "                                 lambda x, A, q_freq, B, C: (A / np.pi) * (B / ((x - q_freq)**2 + B**2)) + C,\n",
    "                                 [-5, 4.975, 1, 5] # initial parameters for curve_fit\n",
    "                                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(frequencies_GHz, np.real(sweep_values), color='black')\n",
    "plt.plot(frequencies_GHz, y_fit, color='red')\n",
    "plt.xlim([min(frequencies_GHz), max(frequencies_GHz)])\n",
    "\n",
    "plt.xlabel(\"Frequency [GHz]\")\n",
    "plt.ylabel(\"Measured Signal [a.u.]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We've updated our qubit frequency estimate from 4.97186 GHz to 4.97174 GHz.\n"
     ]
    }
   ],
   "source": [
    "A, rough_qubit_frequency, B, C = fit_params\n",
    "rough_qubit_frequency = rough_qubit_frequency*GHz # make sure qubit freq is in Hz\n",
    "print(f\"We've updated our qubit frequency estimate from \"\n",
    "      f\"{round(backend_defaults.qubit_freq_est[qubit] / GHz, 5)} GHz to {round(rough_qubit_frequency/GHz, 5)} GHz.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calibrating and using a $\\pi$ Pulse <a id=\"calib\"></a>\n",
    "\n",
    "### Calibrating $\\pi$ Pulses using a Rabi Experiment <a id=\"rabi\"></a>\n",
    "\n",
    "Once we know the frequency of our qubit, the next step is to determine the strength of a $\\pi$ pulse. Strictly speaking of the qubit as a two-level system, a $\\pi$ pulse is one that takes the qubit from $\\vert0\\rangle$ to $\\vert1\\rangle$, and vice versa. This is also called the $X$ or $X180$ gate, or bit-flip operator. We already know the microwave frequency needed to drive this transition from the previous frequency sweep experiment, and we now seek the amplitude needed to achieve a $\\pi$ rotation from $\\vert0\\rangle$ to $\\vert1\\rangle$. The desired rotation is shown on the Bloch sphere in the figure below -- you can see that the $\\pi$ pulse gets its name from the angle it sweeps over on a Bloch sphere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/aasfaw/qiskit-intros/blob/master/zero_to_one_X180.png?raw=true\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will change the drive amplitude in small increments and measuring the state of the qubit each time. We expect to see oscillations which are commonly named Rabi oscillations, as the qubit goes from $\\vert0\\rangle$ to $\\vert1\\rangle$ and back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# This experiment uses these values from the previous experiment:\n",
    "    # `qubit`,\n",
    "    # `measure`, and\n",
    "    # `rough_qubit_frequency`.\n",
    "\n",
    "# Rabi experiment parameters\n",
    "num_rabi_points = 50\n",
    "\n",
    "# Drive amplitude values to iterate over: 50 amplitudes evenly spaced from 0 to 0.75\n",
    "drive_amp_min = 0\n",
    "drive_amp_max = 0.75\n",
    "drive_amps = np.linspace(drive_amp_min, drive_amp_max, num_rabi_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# Build the Rabi experiments:\n",
    "#    A drive pulse at the qubit frequency, followed by a measurement,\n",
    "#    where we vary the drive amplitude each time.\n",
    "rabi_schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples, amp=drive_amp, \n",
    "                                    sigma=drive_sigma, name=f\"Rabi drive amplitude = {drive_amp}\")\n",
    "    this_schedule = pulse.Schedule(name=f\"Rabi drive amplitude = {drive_amp}\")\n",
    "    this_schedule += Play(rabi_pulse, drive_chan)\n",
    "    # Reuse the measure instruction from the frequency sweep experiment\n",
    "    this_schedule += measure << this_schedule.duration\n",
    "    rabi_schedules.append(this_schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The schedule will look essentially the same as the frequency sweep experiment. The only difference is that we are running a set of experiments which vary the amplitude of the drive pulse, rather than its modulation frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hyunjinkim/opt/anaconda3/envs/qiskit_env/lib/python3.9/site-packages/qiskit/pulse/schedule.py:1534: DeprecationWarning: Legacy pulse drawer is deprecated. Specified arguments label are deprecated. Please check the API document of new pulse drawer `qiskit.visualization.pulse_drawer_v2`.\n",
      "  warnings.warn('Legacy pulse drawer is deprecated. '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x241.987 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rabi_schedules[-1].draw(label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# Assemble the schedules into a Qobj\n",
    "num_shots_per_point = 1024\n",
    "\n",
    "rabi_experiment_program = assemble(rabi_schedules,\n",
    "                                   backend=backend,\n",
    "                                   meas_level=1,\n",
    "                                   meas_return='avg',\n",
    "                                   shots=num_shots_per_point,\n",
    "                                   schedule_los=[{drive_chan: rough_qubit_frequency}]\n",
    "                                                * num_rabi_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60723cf257095c901f7d82f7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-631b1e53aac5>:2: DeprecationWarning: Passing a Qobj to Backend.run is deprecated and will be removed in a future release. Please pass in circuits or pulse schedules instead.\n",
      "  job = backend.run(rabi_experiment_program)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "print(job.job_id())\n",
    "job = backend.run(rabi_experiment_program)\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "rabi_results = job.result(timeout=120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our results, we will extract them and fit them to a sinusoidal curve. For the range of drive amplitudes we selected, we expect that we will rotate the qubit several times completely around the Bloch sphere, starting from $|0\\rangle$. The amplitude of this sinusoid tells us the fraction of the shots at that Rabi drive amplitude which yielded the $|1\\rangle$ state.  We want to find the drive amplitude needed for the signal to oscillate from a maximum (all $|0\\rangle$ state) to a minimum (all $|1\\rangle$ state) -- this gives the calibrated amplitude that enacts a $\\pi$ pulse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "# center data around 0\n",
    "def baseline_remove(values):\n",
    "    return np.array(values) - np.mean(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEGCAYAAABsLkJ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeFklEQVR4nO3de3RsdZnm8e+TAwJBwAtHBSEVtVFaWQicNANLx1EabVarMIg6aHTG9pKF7XXaHpUul4My6enW1hFHZ1zRsdVJ2stg2zJogzfQ7ulGCLcDCChIEkFbDiAXCSDn5J0/akcqOVXJruu+PZ+19jqpyq7a76mq1Lv37/1dFBGYmVn1jGQdgJmZZcMJwMysopwAzMwqygnAzKyinADMzCpqj6wD6MSBBx4Y4+PjWYdhZlYol19++R0RsXX9/YVKAOPj48zPz2cdhplZoUhabHV/5k1AkrZIulLS+VnHYmZWJZknAOAdwPVZB2FmVjWZJgBJhwAvBj6TZRxmZlWU9RXAx4B3AyvtdpA0JWle0vyOHTuGFpiZWdlllgAkvQS4PSIu32i/iJiJiImImNi6dbcitpmZdSnLK4DnACdLWgC+BJwgaTbDeCxDc3NzjI+PMzIywvj4OHNzc1mHZFZ6mSWAiDgzIg6JiHHgdOB7EfGarOKx7MzNzTE1NcXi4iIRweLiIlNTU04CZgOWdQ3AjHq9zvLy8pr7lpeXqdfrGUVkVg25SAARcXFEvCTrOCwbS0tLHd1v+eMmvGLKRQKwahsbG+vofssXN+EVlxOAZW56eprR0dE1942OjjI9PZ1RRNaJjZrwfGWQb6VPAP4A5t/k5CQzMzPUajUkUavVmJmZYXJyMuvQLIV2TXWrVwK+MsixiCjMtm3btujE7OxsjI6OBvDbbXR0NGZnZzt6HsvO7Oxs1Gq1kBS1Ws3vXQ7VarU1f2Or25YtW1reX6vVsg65coD5aPGdWuorAPcuKTa3LRdDuya8Xbt2tdx/aWnJV+Z50Sor5HXr9ApAUsszEEkdPY9lo92Zpc8g86fVlVq79+/xj3+8r8yHjDZXAGr8rhgmJiaik/UAxsfHWVzcfRrsWq3GwsJCHyOzQRgZGaHV51MSKyttp4+ynFi9gmu+Ch8dHWWfffbhzjvv3G1//10OjqTLI2Ji/f2lbgJy75Jic/fQYmtX3L/rrrta7u9xH8NX6gTg3iXF5gRefJOTkywsLLCyssLCwgKTk5NO7DlS6gQArT+A4O6hWenkdXcCLycn9hxpVRjI69ZpEbgddw/Nhl/3Yutnl1x37x0uqlgEbsfF4Wz4dS+udgVdX5EVQ7sicCUTgHuXZMOve3E5eRdbJXsBteMiVDb8uheXZ2wtp0omABehstHP191F/OFy8i6pVoWBvG79KgJHuAiVlX687i4mD59f82LDRWArC7dHZ2Nubo56vc7S0hJjY2NMT0+7AFwQLgJbabiYbNYZF4GtNNwebdYfmSUASXtLulTS1ZKuk/SBrGKxYnER36w/srwCeAg4ISKeDRwFnCTpuAzjsYLwFBFm/bFHVgdOKtO/Tm7umWzFKUhYpiYnJ/2Fb9ajTGsAkrZIugq4Hfh2RPywxT5TkuYlze/YsWPoMZqZlVWmCSAidkXEUcAhwLGSjmixz0xETETExNatW4ceo5lZWeWiF1BE3A1cBJyUcShmZpWRZS+grZIek/y8D/BC4Ias4jEzq5rMisDAQcDnJW2hkYi+EhHnZxiPmVmlZHYFEBHbI+LoiDgyIo6IiA9mFYuZ5YMn+RuuLK8AzMx+a/2iM4uLi0xNTQG4y++A5KIInDc+CzEbvnq9vmbFMYDl5WXq9XpGEZWfE8A6q2chi4uLRMRvz0KcBNJzArVueNGZ4XMCWMdnIb1xArVueZK/4XMCWMdnIb1xArVueZK/4XMCWMdnIb1xAi2+rJrwPMlfBlotE5bXrZ9LQrbjpe96U6vV1rx2q1utVss6NEvBn/9yos2SkL4CWMdnIb3xZXyxuQmvWpwAWpicnGRhYYGVlRUWFhb85d+BrBOoeyD1xk141eI1ga001g8kgsbVh6/g0hsfH2dxcXG3+2u1GgsLC8MPyPrCawJb6bn5onduwqsWJwArDTdf9C7rJjwbLjcBWWm4+cKsNTcBWem5+cKsM04AVhpuvjDrjJuAzMxKrl0TUNv1ACQ9LsXzrkRjPV8zMyuYjRaE+XmyaYN9tgCeJMfMrIA2SgDXR8TRGz1Y0pXdHljSocAXgCfSmHNkJiLO6fb5zMysMxslgONTPD7NPu3sBN4VEVdI2g+4XNK3I+JHPTynmZml1LYXUEQ8uNmD0+yzwWN/ERFXJD/fB1wPPLnb5zMzs8501Q1U0vn9DELSOHA08MMWv5uSNC9pfseOHf08rJkVhCf5G4xuxwG8qV8BSHo08FXgnRFx7/rfR8RMRExExMTWrVv7dVgzKwgvMzo4mY4DkLQncD5wYUR8dLP9PQ7ArHo8xUfvOh4H0PTAW2j00lkjIp7aY0AC/heN3kabfvmbWTV5kr/BSdMENAH8XrL9a+DjwGwfjv0c4LXACZKuSrY/7MPz2pC4XdaGwet0D86mCSAi7mzabouIjwEv7vXAEfGPEaGIODIijkq2b/b6vDYcbpe1YfEkf4OzaQKQdEzTNiHpDFI0HVm5efEVGxZP8jc4mxaBJV3UdHMncAvwkYi4cZCBteIicH6MjIzQ6rMjiZWVlQwiMrN2ui4CR8QLBhOSFdnY2FjLnhlulzUrjm4Hgh3T70CKwEXPR7hdtvj8eTYiouMN+HQ3j+t127ZtW2RldnY2RkdHg0aX2ABidHQ0ZmdnM4spa7Ozs1Gr1UJS1Gq1Sr8WRePPc7UA89HiO9ULwqTkwShWJv48V0vXNYDkwY8FDgP2Xr0vIn7Qv/Dyz4NRrEz8eTZI1w30jcAPgAuBDyT/njXYsPLHg1GsTPx5NkhXBH4HjVHAi9HoEXQ0cPcgg8ojFz2tTPx5NkiXAB6MZN5/SXtFxA3AMwYbVv54MIqViT/PBukGgn0N+CPgncAJwK+APSNi6PP2eCCYmVnnehkIdmry41nJqOADgAv6HJ+ZmQ1ZR3P6RMT3BxWImZkNV9sagKQrNntwmn3MzCyfNroC+F1J2zf4vWg0B5mZWQFtlAAOT/H4Xf0KxMzMhqttAoiI3ceJm5lZaXQ1G6iZmRWfE4CZWUVlmgAkfVbS7ZKuzTIOM7Mq2qgb6H2S7m2x3Sfp3j4d/3PASX16LjMz60DbBBAR+0XE/i22/SJi/34cPJlS+q5+PJfZZrwCltlaqUcCS3oCa9cDGMrE4ZKmgCnwVLXWvbm5OaamplheXgZgcXGRqakpAE+AZpWVZjK4k4GPAAcDtwM14PqIeFZfApDGgfMj4ojN9vVkcNYtr4BlVdZuMrg0ReCzgeOAH0fEU4DfBy7pc3xmA+UVsMx2lyYBPBwRdwIjkkYi4iJgt0xilmdeActsd2kSwN2SHk1jWcg5SecA9/fj4JK+CPwz8AxJt0p6Qz+e12w9r4Bltrs0ReBTgAeB/whM0pgA7oP9OHhEvKofz2O2mdVCb71eZ2lpibGxMaanp10AtkrbtAicJy4Cm5l1rusisKSXSfqJpHsGMBDMzMwykqYG8CHg5Ig4oN8DwczMeuUBft1LUwP4ZURcP/BIzMw65AF+vUkzEOwc4EnA3wEPrd4fEX870MhacA3AzJp5gF867WoAaa4A9geWgRc13RfA0BOAmVkzD/DrzaYJICL+aBiBmJl1amxsrOUVgAf4pbNpApD08RZ33wPMR8TX+x+SmVk609PTa2oA4AF+nUjTC2hv4CjgJ8l2JHAI8AZJHxtYZGZmm5icnGRmZoZarYYkarUaMzMzLgCnlKYIfAnwnIjYldzeA/gH4LnANRHxzIFHmXAR2Mysc73MBvpY4NFNt/cFHpckhIdaP6Ra3A/Z8syfT2snTS+gDwFXSboYEPA84M8l7Qt8Z4CxFYL7IVue+fNpG0k1F5Ckg4Bjk5uXRcTPBxpVG3lsAqpCP+S5uTlPolZQVfh82ubaNQG1TQCSDo+IGyQd0+r3EXFFn2PcVB4TwMjICK1eQ0msrKxkEFF/rT+DhEYvCxfaiqHsn09Lp5sEMBMRU5IuavHriIgT+h3kZvKYAMp+hlX2/1/Z+f0z6KIIHBFTyb8vaLEN/cs/r8q+0IhHWhZb2T+f1ps000G/QtJ+yc/vk/S3ko4efGjFUPZ+yF5KsdjK/vm03qQZB7A9Io6U9FzgvwAfBt4fEf9qGAE2y2MTUNm5BmBWfL2MA9iV/PtiYCYivgE8qk9BnSTpRkk3SXpvP57T+stnkGblleYK4HzgNuCFwDHAA8ClEfHsng4sbQF+nDzvrcBlwKsi4kftHuMrADOzzvVyBfBK4ELgDyLibuBxwH/qQ0zHAjdFxE8j4jfAl2gsQG82VB4pa1WVZjroZZrm/o+IXwC/6MOxnwz8rOn2rcBudQVJU8AUuPBo/eeRslZlaa4AMhURMxExERETW7duzTocK5l6vb6mwA2wvLxMvV7PKCKz4ckyAdwGHNp0+5DkPrOh8TgHq7IsE8BlwGGSniLpUcDpwHkZxmMV5HEOVmVtE4Ck+yTd227r9cARsRN4K40C8/XAVyLiul6f16wTHilrVbbRVBD7RcT+wDnAe2kUbQ8B3gN8rB8Hj4hvRsTTI+JpEeG/OBs6j3MoL/fu2lyacQBXr+/z3+q+YfA4ADNLwyPY1+plHMD9kiYlbZE0ImkSuL//IZqZ9Yd7d6WTJgG8msZgsF8m2yuS+8zMcsm9u9JJMxBsAY/QNbMCGRsba7kOgnt3rZVmOuinS/qupGuT20dKet/gQzMz6457d6WTpgno08CZwMMAEbGdRp99M7Nccu+udDZtAgJGI+JSSc337RxQPGZmfTE5Oekv/E2kuQK4Q9LTgACQ9HL6MxmcmZllKM0VwFuAGeBwSbcBtwBOq2ZmBbdhAkgWbfnjiDhR0r7ASETcN5zQzMxskDZMABGxK1kLmIjw4C8zsxJJUwO4UtJ5kl4r6WWr28Ajs6Hz3Clm1ZImAewN3AmcALw02V4yyKBs+FbnTllcXCQifrsylpNAcTiBW6c2nQwuTzwZ3OCMj4+3HDlZq9VYWFgYfkDWEU9+ZhtpNxlcmtlA/5qkC2iziHh9/8JLxwlgcEZGRmj1WZDEyspKBhFZJ5zAbSPtEkCabqDnN/28N3Aq8PN+BWb54LlTis2Tn1k3Nq0BRMRXm7Y5GjOD7pZJrNg8d0qxeWlL60Y3awIfBjyh34FYtjx3SrE5gVs30tQA7mNtDeBfgDMj4quDDKwV1wDM2pubm6Ner7O0tMTY2BjT09NO4Ab0UAQeUDCvAM4Cfhc4NiJSfas7AZiZda7rJSElPSeZBgJJr5H0UUm1HuO5FngZ8IMen8fMzLqUpgbwP4FlSc8G3gXcDHyhl4NGxPURcWMvz2FmZr1JkwB2RqOd6BTgExHxSWC/wYb1CElTkuYlze/YsWNYh+0Lj8w0szxLMw7gPklnAq8BnidpBNhzswdJ+g7wpBa/qkfE19MGGBEzNKajZmJiojDDltePzFydWgFwYc7MciFNL6AnAa8GLouIf5A0Bjw/InpqBkqe+2LgT8tYBPbITDPLi65HAkfEvwAfbbq9RI81gCrwyEwzy7s0vYCOk3SZpF9L+o2kXZLu6eWgkk6VdCtwPPANSRf28nx55JGZZvnk2twj0hSBPwG8CvgJsA/wRuB/9HLQiPhaRBwSEXtFxBMj4g96eb488shMs/zxtOdrpZoKIiJuArZExK6I+GvgpMGGVXyeWsEsf+r1+popswGWl5ep1+sZRZStNEXgHwAnAp+hMQ3EL4DXRcSzBx/eWkUqAptZ/lR12vOuRwIDr032eytwP3AocFp/wzMzGzzX5tZKMx30IiDgoIj4QET8SdIkZGZWKK7NrZWmF9BLgauAC5LbR0k6b8BxmZn1nWtza6VpAjoLOBa4GyAirgKeMrCIzMwGaHJykoWFBVZWVlhYWMj9l/8gu62mmQri4Yi4R1LzfYWZksHMrKgGPaVMmiuA6yS9Gtgi6TBJ/x34p56PbGZmGxp0t9U0CeBtwLOAh4AvAvcC7+zL0c1yzCNGLWuDnlImzVxAy0A92cwqwbO5Wh6MjY21nFSyX91W2w4E26ynT0Sc3JcIOuCBYDYsns3V8mD9iQg0uq122nOpm9lAjwd+RqPZ54c0xgKYVYJnc7U8WP2Sr9frLC0tMTY2xvT0dN+uQjeqATwJ+DPgCOAc4IXAHRHx/Yj4fl+ObpZTeR0x6rpE9Qyy22rbBJBM/HZBRPwH4DjgJuBiSW/t29HNciqPI0Y9k6X124a9gCTtJellwCzwFuDjwNeGEZhZlvI4YtQzWVq/bVQE/gKN5p9vAl+KiGuHGVgrLgJblVV1JkvrXTezgb4GOAx4B/BPku5Ntvsk3TuoQM2stbzWJay4NqoBjETEfsm2f9O2X0TsP8wgzSyfdQkrtlQrgvWbpA9LukHSdklfk/SYLOKoIvciKa481iWs2DZdEWwgB5VeBHwvInZK+kuAiHjPZo9zDaA3/RpUYmbF0suKYH0XEd+KiJ3JzUuAQ7KIo2rci8TMmmWSANZ5PfD37X4paUrSvKT5HTt2DDGs8vHoVjNrNrAEIOk7kq5tsZ3StE8d2Am0bYiOiJmImIiIia1btw4q3EpwLxIzazawBBARJ0bEES22rwNIeh3wEmAysihEZCirQqx7kZhZs6x6AZ0EvBs4OZluujKyHM7vXiRm1iyrXkA3AXsBdyZ3XRIRZ2z2uDL0AvI0w2Y2bN1MBz0wEfE7WRw3D1yINbO8yEMvoEpxIdYsn6o4SNIJYMhciDXLn6yn2s4s+UREYbZt27ZFGczOzkatVgtJUavVYnZ2NuuQzCqtVqsFsNtWq9UGfuzZ2dkYHR1dc9zR0dG+fi8A89HiOzWTInC3ylAENrP8yXKq7WF0DMnVVBBmRVbFtuKyy7I2l2XHECcAsw5k3VZsg5FlbS7L5OMEYNYBT6hXTlkOkswy+bgGYNYBL8togzA3N0e9XmdpaYmxsTGmp6f7mnza1QCcAMw64JHcVkQuApv1gcdxWJk4AZh1wBPqWZk4AZSUuyoOzuTkJAsLC6ysrLCwsOAvfyssJ4AScldFs/4p88mUi8Al5EKlWX+snkw1d/0dHR0tXLOfi8AV4imni6/MZ51FUvZxH04AJeQpp4vNTXj5UfaTKSeAEnJXxWIr+1lnkZT9ZMoJoITcVbHYyn7WWST9PpnKXdNeqzmi87qVZT0As41kOTe97a5f63cMY97/dmizHkAmVwCSzpa0XdJVkr4l6eAs4sij3J0h2NC5CS9f+jXuI5dNe62ywqA3YP+mn98OfCrN48p+BZDlGYLli1eNKx9JLa/sJA382OR1RTBJZwJjEfHmzfYt+zgA9983K68s/75zNw5A0rSknwGTwPs32G9K0ryk+R07dgwvwAy4+Fdsbr6zVa0+C7ls2mt1WdCPDfgOcG2L7ZR1+50JfCDNc5a9Caib4p+bCvKh2+Y7v3/ls9FnIav3mzZNQJn37AHGgGvT7Fv2BNDpl4hrBvnRbfL2+1c+eezFlasEABzW9PPbgHPTPK7sCSCiszPCPH7QqqqbAp/fv3LKstjbTrsEkEkRWNJXgWcAK8AicEZE3LbZ48peBO6UlyfMj24KfH7/yimPnTlyVQSOiNMi4oiIODIiXprmy992V/Zh6kXSTYHP71855bLY206ry4K8blVoAmqnVdOQ25DzpdMCn9+/4mv3nuetuE+eagDdblVNAHnsVWCdKcoXhaVXpATeLgFkPhCsE1WtAeSxTdHSK8uiIrZWkf4u29UAnAAKwMXCYivSF4WlV6S/y1wVga0zLhYWm0d4l1MZ/i6dAAqgUL0KbDdl+KKw3ZXh79IJoAC8wEuxleGLwnZXhr9L1wDMhmBubo56vc7S0hJjY2NMT08X6ovCis1FYDOzinIR2MzM1nACMDOrKCcAM7OKcgIwM6soJwAzs4oqVC8gSTtorB/QjQOBO/oYziDkPca8xwf5jzHv8YFj7Ie8xVeLiK3r7yxUAuiFpPlW3aDyJO8x5j0+yH+MeY8PHGM/5D2+VW4CMjOrKCcAM7OKqlICmMk6gBTyHmPe44P8x5j3+MAx9kPe4wMqVAMwM7O1qnQFYGZmTZwAzMwqqnQJQNJJkm6UdJOk97b4/V6Svpz8/oeSxnMW3/MkXSFpp6SXDzO2DmL8E0k/krRd0ncl1XIY4xmSrpF0laR/lPTMPMXXtN9pkkLS0LsMpngNXydpR/IaXiXpjXmKL9nnlcln8TpJfzPM+NLEKOm/Nb1+P5Z097Bj3FCrleKLugFbgJuBpwKPAq4Gnrlunz8GPpX8fDrw5ZzFNw4cCXwBeHlOX8MXAKPJz28e5mvYQYz7N/18MnBBnuJL9tsP+AFwCTCRw9fwdcAnhv0Z7CC+w4Argccmt5+QtxjX7f824LNZvJ7ttrJdARwL3BQRP42I3wBfAk5Zt88pwOeTn88Ffl+S8hJfRCxExHYgq1Wl08R4UUQsJzcvAQ7JYYz3Nt3cFxhmb4c0n0OAs4G/BB4cYmyr0saYlTTxvQn4ZET8CiAibs9hjM1eBXxxKJGlVLYE8GTgZ023b03ua7lPROwE7gEeP5To0sWXtU5jfAPw9wONaHepYpT0Fkk3Ax8C3j6k2CBFfJKOAQ6NiG8MMa5mad/n05KmvnMlHTqc0IB08T0deLqk/yfpEkknDS26htR/K0kz6VOA7w0hrtTKlgBsiCS9BpgAPpx1LK1ExCcj4mnAe4D3ZR3PKkkjwEeBd2Udyyb+LzAeEUcC3+aRK+e82INGM9DzaZxdf1rSY7IMaAOnA+dGxK6sA2lWtgRwG9B8lnJIcl/LfSTtARwA3DmU6NLFl7VUMUo6EagDJ0fEQ0OKbVWnr+OXgH87yIDW2Sy+/YAjgIslLQDHAecNuRC86WsYEXc2vbefAbYNKTZI9x7fCpwXEQ9HxC3Aj2kkhGHp5HN4Ojlr/gFKVwTeA/gpjUut1aLMs9bt8xbWFoG/kqf4mvb9HNkUgdO8hkfTKH4dluP3+bCmn18KzOcpvnX7X8zwi8BpXsODmn4+FbgkZ/GdBHw++flAGs0xj89TjMl+hwMLJANv87RlHsAA3pQ/pHEmcDNQT+77II0zVYC9gf8D3ARcCjw1Z/H9Ho0zm/tpXJlcl8PX8DvAL4Grku28HMZ4DnBdEt9FG30BZxHfun2HngBSvob/NXkNr05ew8NzFp9oNKX9CLgGOD1vr2Fy+yzgL4YdW5rNU0GYmVVU2WoAZmaWkhOAmVlFOQGYmVWUE4CZWUU5AZiZVZQTgBWCpF3JjIrXSbpa0ruSEbWt9j1Y0rnDjrEbksYlPSDpqgE89z7Ja/YbSQf2+/mt+PbIOgCzlB6IiKMAJD0B+Btgf+A/N+8kaY+I+DmQyVTaXbp59f/WTxHxAHBUMtrYbDe+ArDCicasj1PAW9XwOknnSfoe8N3krPpagGSSsGetPlbSxZImJO0r6bOSLpV0paTdZnGU9OhkvYMrkrUFTknuH5d0g6TPJXO8z0k6MZmU7CeSjk32O0vS/5b0z8n9b0rz/5P0d5IuT652ptrsc/Hq1BGSDvSXvHXDCcAKKSJ+SmM+9ickdx1DY+qMf7Nu1y8DrwSQdBCN6Q3macxj9L2IOJbG+gYflrTvusc+CJwaEcck+3ykaerw3wE+QmOY/+HAq4HnAn8K/FnTcxwJnAAcD7xf0sEp/nuvj4htNCbae7ukYc1WaxXjBGBl8e2IuKvF/V/hkeagV9JYAwLgRcB7k7b3i2lMETK27rEC/lzSdhrTXzwZeGLyu1si4pqIWKExXcJ3ozGs/hoai/qs+npEPBARd9CYTuHYFP+Xt0u6msZaC4cy3AnOrEJcA7BCkvRUYBewugjI/a32i4jbJN0p6Ujg3wFnrD4FcFpE3LjBYSaBrcC2iHg4aWbZO/ld8wyoK023V1j7d7V+rpUN516R9HzgROD4iFiWdHHTMZvt5JETuFa/N9uUrwCscCRtBT5FY7nCNJNZfRl4N3BANFZbA7gQeNtqk46ko1s87gDg9uTL/wVAN2sfnyJp76QZ5/nAZZvsfwDwq+TL/3AaU0W3ssAj0zMXqeBtOeIEYEWx2qXxOhrNMd8CPpDyseeSTP3ddN/ZwJ7A9uQ5z27xuDlgQtI1wL8Hbugi7u00mn4uAc5Oeiht5AJgD0nXA3+RPA4ASZ9pWjPgr4A3S7qSxlTIq/scLOmbXcRpFeTZQM0GRNJZwK8j4q822GccOD8ijhhgHAs0ppu+Y1DHsGLyFYBZtnYBBwxyIBiNK52Vfj+/FZ+vAMzMKspXAGZmFeUEYGZWUU4AZmYV5QRgZlZRTgBmZhX1/wHVrC4tb+eIQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rabi_values = []\n",
    "for i in range(num_rabi_points):\n",
    "    # Get the results for `qubit` from the ith experiment\n",
    "    rabi_values.append(rabi_results.get_memory(i)[qubit]*scale_factor)\n",
    "\n",
    "rabi_values = np.real(baseline_remove(rabi_values))\n",
    "\n",
    "plt.xlabel(\"Drive amp [a.u.]\")\n",
    "plt.ylabel(\"Measured signal [a.u.]\")\n",
    "plt.scatter(drive_amps, rabi_values, color='black') # plot real part of Rabi values\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false,
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_params, y_fit = fit_function(drive_amps,\n",
    "                                 rabi_values, \n",
    "                                 lambda x, A, B, drive_period, phi: (A*np.cos(2*np.pi*x/drive_period - phi) + B),\n",
    "                                 [3, 0.1, 0.5, 0])\n",
    "\n",
    "plt.scatter(drive_amps, rabi_values, color='black')\n",
    "plt.plot(drive_amps, y_fit, color='red')\n",
    "\n",
    "drive_period = fit_params[2] # get period of rabi oscillation\n",
    "\n",
    "plt.axvline(drive_period/2, color='red', linestyle='--')\n",
    "plt.axvline(drive_period, color='red', linestyle='--')\n",
    "plt.annotate(\"\", xy=(drive_period, 0), xytext=(drive_period/2,0), arrowprops=dict(arrowstyle=\"<->\", color='red'))\n",
    "plt.annotate(\"$\\pi$\", xy=(drive_period/2-0.03, 0.1), color='red')\n",
    "\n",
    "plt.xlabel(\"Drive amp [a.u.]\", fontsize=15)\n",
    "plt.ylabel(\"Measured signal [a.u.]\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi Amplitude = 0.28438430261479486\n"
     ]
    }
   ],
   "source": [
    "pi_amp = abs(drive_period / 2)\n",
    "print(f\"Pi Amplitude = {pi_amp}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our $\\pi$ pulse!\n",
    "Let's define our pulse, with the amplitude we just found, so we can use it in later experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "uses-hardware"
    ]
   },
   "outputs": [],
   "source": [
    "pi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                              amp=pi_amp, \n",
    "                              sigma=drive_sigma,\n",
    "                              name='pi_pulse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attempt on Creating a Ternary Superposition Gate <a id=\"sgate\"></a>\n",
    "\n",
    "Constructed x90_pulse and used it to transform 0 state to another state between 0 and 1. Then used Rabi experiment to test different amplitudes that might put the qutrit on superposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get x90 pulse using half of pi amplitude\n",
    "x90_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                               amp=pi_amp/2, \n",
    "                               sigma=drive_sigma,\n",
    "                               name='x90_pulse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4971744383.921157\n"
     ]
    }
   ],
   "source": [
    "# s_freq = GHz / (pi_amp)\n",
    "s_freq = rough_qubit_frequency\n",
    "# print(s_freq)\n",
    "# print(rough_qubit_frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rabi experiment parameters\n",
    "num_rabi_points = 75\n",
    "\n",
    "# Drive amplitude values to iterate over: 50 amplitudes evenly spaced from 0 to 0.75\n",
    "drive_amp_min = 0\n",
    "drive_amp_max = 1\n",
    "drive_amps = np.linspace(drive_amp_min, drive_amp_max, num_rabi_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.pulse.library import Waveform\n",
    "\n",
    "def apply_sideband(pulse, freq):\n",
    "    \"\"\"Apply a sinusoidal sideband to this pulse at frequency freq.\n",
    "    Args:\n",
    "        pulse (SamplePulse): The pulse of interest.\n",
    "        freq (float): LO frequency for which we want to apply the sweep.\n",
    "    Return:\n",
    "        SamplePulse: Pulse with a sideband applied (oscillates at difference between freq and cal_qubit_freq).\n",
    "    \"\"\"\n",
    "    # time goes from 0 to dt*drive_samples, sine arg of form 2*pi*f*t\n",
    "    t_samples = np.linspace(0, dt*drive_samples, drive_samples)\n",
    "    sine_pulse = np.sin(2*np.pi*(freq-rough_qubit_frequency)*t_samples) # no amp for the sine\n",
    "    \n",
    "    # create sample pulse w/ sideband applied\n",
    "    # Note: need to make sq_pulse.samples real, multiply elementwise\n",
    "    sideband_pulse = Waveform(np.multiply(np.real(pulse.samples), sine_pulse), name='sideband_pulse')\n",
    "    \n",
    "    return sideband_pulse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "rabi_s_schedules = []\n",
    "\n",
    "# loop over all drive amplitudes\n",
    "for ii, drive_amp in enumerate(drive_amps):\n",
    "    base_s_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                       sigma=drive_sigma,\n",
    "                                       amp=drive_amp,\n",
    "                                       name='base_s_pulse')\n",
    "    # apply sideband at the 1->2 frequency\n",
    "    rabi_s_pulse = apply_sideband(base_s_pulse, s_freq)\n",
    "    \n",
    "    # add commands to schedule\n",
    "    schedule2 = pulse.Schedule(name='Rabi Experiment at drive amp = %s' % drive_amp)\n",
    "    schedule2 |= pulse.Play(x90_pulse, drive_chan) # 0-> h\n",
    "    schedule2 |= pulse.Play(rabi_s_pulse, drive_chan) << schedule.duration # h -> s Rabi pulse\n",
    "    schedule2 |= measure << schedule2.duration # shift measurement to after drive pulse\n",
    "    \n",
    "    rabi_s_schedules.append(schedule2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assemble the schedules into a program\n",
    "rabi_s_expt_program = assemble(rabi_s_schedules,\n",
    "                                backend=backend,\n",
    "                                meas_level=1,\n",
    "                                meas_return='avg',\n",
    "                                shots=1024,\n",
    "                                schedule_los=[{drive_chan: s_freq}]\n",
    "                                               * num_rabi_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-116-5240ce57bb94>:1: DeprecationWarning: Passing a Qobj to Backend.run is deprecated and will be removed in a future release. Please pass in circuits or pulse schedules instead.\n",
      "  rabi_s_job = backend.run(rabi_s_expt_program)\n"
     ]
    }
   ],
   "source": [
    "rabi_s_job = backend.run(rabi_s_expt_program)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6072a274b8a8751c31ee8f39\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "print(rabi_s_job.job_id())\n",
    "job_monitor(rabi_s_job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_job_data(job, average):\n",
    "    \"\"\"Retrieve data from a job that has already run.\n",
    "    Args:\n",
    "        job (Job): The job whose data you want.\n",
    "        average (bool): If True, gets the data assuming data is an average.\n",
    "                        If False, gets the data assuming it is for single shots.\n",
    "    Return:\n",
    "        list: List containing job result data. \n",
    "    \"\"\"\n",
    "    job_results = job.result(timeout=120) # timeout parameter set to 120 s\n",
    "    result_data = []\n",
    "    for i in range(len(job_results.results)):\n",
    "        if average: # get avg data\n",
    "            result_data.append(job_results.get_memory(i)[qubit]*scale_factor) \n",
    "        else: # get single data\n",
    "            result_data.append(job_results.get_memory(i)[:, qubit]*scale_factor)  \n",
    "    return result_data\n",
    "\n",
    "def get_closest_multiple_of_16(num):\n",
    "    \"\"\"Compute the nearest multiple of 16. Needed because pulse enabled devices require \n",
    "    durations which are multiples of 16 samples.\n",
    "    \"\"\"\n",
    "    return (int(num) - (int(num)%16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the job data (average)\n",
    "rabi_s_data = get_job_data(rabi_s_job, average=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: We only plot the real part of the signal.\n",
    "rabi_s_data = np.real(baseline_remove(rabi_s_data))\n",
    "(rabi_s_fit_params, \n",
    " rabi_s_y_fit) = fit_function(drive_amps,\n",
    "                            rabi_s_data, \n",
    "                            lambda x, A, B, drive_s_period, phi: (A*np.cos(2*np.pi*x/drive_s_period - phi) + B),\n",
    "                            [3, 0.5, 0.9, 0])\n",
    "\n",
    "plt.scatter(drive_amps, rabi_s_data, color='black')\n",
    "plt.plot(drive_amps, rabi_s_y_fit, color='red')\n",
    "\n",
    "drive_s_period = rabi_s_fit_params[2]\n",
    "# account for phi in computing pi amp\n",
    "pi_amp_s = (drive_s_period/2/np.pi) *(np.pi+rabi_s_fit_params[3])\n",
    "\n",
    "plt.axvline(pi_amp_s, color='red', linestyle='--')\n",
    "plt.axvline(pi_amp_s+drive_s_period/2, color='red', linestyle='--')\n",
    "plt.annotate(\"\", xy=(pi_amp_s+drive_s_period/2, 0), xytext=(pi_amp_s,0), arrowprops=dict(arrowstyle=\"<->\", color='red'))\n",
    "plt.annotate(\"$\\pi$\", xy=(pi_amp_s-0.03, 0.1), color='red')\n",
    "\n",
    "plt.xlabel(\"Drive amp [a.u.]\", fontsize=15)\n",
    "plt.ylabel(\"Measured signal [a.u.]\", fontsize=15)\n",
    "plt.title('Rabi Experiment (0->S)', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our updated estimate for the s transition frequency is 4.9717444 GHz.\n",
      "Pi Amplitude (1->2) = 0.564997162956454\n"
     ]
    }
   ],
   "source": [
    "print(f\"Our updated estimate for the s transition frequency is \"\n",
    "      f\"{round(s_freq/GHz, 7)} GHz.\")\n",
    "print(f\"Pi Amplitude (1->2) = {pi_amp_s}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_pulse_s = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                 amp=pi_amp_s, \n",
    "                                 sigma=drive_sigma,\n",
    "                                 name='pi_pulse_s')\n",
    "# make sure this pulse is sidebanded\n",
    "pi_pulse_s = apply_sideband(pi_pulse_s, s_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation of pulse into a quantum circuit as a superposition gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 253.926x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import Gate\n",
    "\n",
    "circ = QuantumCircuit(1, 1)\n",
    "s_gate = Gate('s_gate', 1, [])\n",
    "circ.append(s_gate, [0])\n",
    "circ.measure(0, 0)\n",
    "\n",
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 253.926x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import transpile\n",
    "circ.add_calibration(s_gate, [0], pi_pulse_s)\n",
    "circ = transpile(circ, backend)\n",
    "circ.draw('mpl', idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References <a id=\"refs\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Team, The Qiskit. Calibrating Qubits with Qiskit Pulse, Data 100 at UC Berkeley, 6 Apr. 2021, qiskit.org/textbook/ch-quantum-hardware/calibrating-qubits-pulse.html. \n",
    "2. Team, The Qiskit. Accessing Higher Energy States, Data 100 at UC Berkeley, 6 Apr. 2021, qiskit.org/textbook/ch-quantum-hardware/accessing_higher_energy_states.html. \n",
    "2. Mandal, Sudhindu Bikash, et al. “Synthesis of Ternary Grover's Algorithm.” 2014 IEEE 44th International Symposium on Multiple-Valued Logic, May 2014, doi:10.1109/ismvl.2014.40. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "qiskit.__qiskit_version__"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qiskit_env",
   "language": "python",
   "name": "qiskit_env"
  },
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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       "placeholder": "​",
       "style": "IPY_MODEL_1cc3f7e696b9480580dbb69a1a1c875f",
       "value": "<font style='color:#34BC6E'>DONE</font>"
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