qiskit/advanced/ignis/1a_calibrating_a_qubit.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Calibrating a qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from qiskit import IBMQ\n",
    "import qiskit.pulse as pulse\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "from qiskit.compiler import assemble\n",
    "from qiskit.qobj.utils import MeasLevel, MeasReturnType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Loading a pulse backend\n",
    "To run an OpenPulse experiment, at first we need to load a backend object of your quantum computing system with `open_pulse = True`. Then you will be able to program a `pulse` experiment using the pulse module. For more information on how to use the `pulse` module see this [tutorial](../terra/5_pulse_schedules.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we use `ibmq_armonk`, a one qubit system publicly available on the IBM Q Experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "IBMQ.load_account()\n",
    "ibmq_provider = IBMQ.get_provider(\n",
    "    hub='provide_your_hub_name_here',\n",
    "    group='provide_your_group_name_here',\n",
    "    project='provide_your_project_name_here'\n",
    ")\n",
    "backend = ibmq_provider.get_backend('ibmq_armonk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = backend.configuration()\n",
    "defaults = backend.defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating our pulses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "User defined pulses can be created from the standard pulse library found in `pulse_lib`, and able to find calibrated pulses provided by the backend in `defaults.circuit_instruction_map`. In this notebook, we use a Gaussian pulse template from `pulse_lib.gaussian` and default measurement pulses from the `circuit_instruction_map`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "circ_inst_map = defaults.circuit_instruction_map\n",
    "\n",
    "measure = circ_inst_map.get('measure', qubits=config.meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auto-phase the output measurement signal\n",
    "\n",
    "def get_amplitude(vec):\n",
    "    i_signal = np.imag(vec)\n",
    "    r_signal = np.real(vec)\n",
    "\n",
    "    mvec = [np.mean(r_signal), np.mean(i_signal)]\n",
    "\n",
    "    src_mat = np.vstack((r_signal - mvec[0], i_signal - mvec[1])).T\n",
    "    (_, _, v_mat) = np.linalg.svd(src_mat)\n",
    "\n",
    "    dvec = v_mat[0, 0:2]\n",
    "\n",
    "    if dvec.dot(mvec) < 0:\n",
    "        dvec = -dvec\n",
    "\n",
    "    return src_mat.dot(dvec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we apply the backend default setting for drive and measurement pulse frequency. These frequencies are calibrated to be centered at resonanse frequency of corresponding qubit and readout resonator. To change this setting, you can refer to the [qiskit community tutorials](https://github.com/Qiskit/qiskit-community-tutorials)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Running a Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this experiment, we apply a Rabi drive pulse to qubit0 and measure a signal from the qubit by changing the amplitude of the pulse (`drive_amps`) from 0 to 0.05. The returned signal is integrated and averaged over 512 shot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# qubit to use for exeperiment\n",
    "qubit = 0\n",
    "\n",
    "# exp configuration\n",
    "exps = 64\n",
    "shots = 512\n",
    "\n",
    "# Rabi pulse\n",
    "drive_amps = np.linspace(0, 1.0, exps)\n",
    "drive_samples = 2048\n",
    "drive_sigma = 256\n",
    "\n",
    "# scaling factor for data returned by backend\n",
    "# note: You may have to adjust this for the backend you use\n",
    "scale_factor=1e-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Pulse commands are registered to `Schedule`s with information of `channel` and the timestep they occur at. You can create your `Schedule` by using schedule composition methods or their syntactic sugar as shown below:  \n",
    "\n",
    "- `|` (`union`): combine two `Schedule`s in parallel.\n",
    "- `+` (`append`): add a `Schedule` right after a `Schedule` to be appended.\n",
    "- `<<` (`shift`): shift `start_time` of `Schedule`.\n",
    "\n",
    "Once create, `Schedule`s can be combined to form a composite `Schedule`. More details are available in this [tutorial](../terra/5_pulse_schedules.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "schedules = []\n",
    "for ii, drive_amp in enumerate(drive_amps):\n",
    "    # drive pulse\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples, amp=drive_amp, sigma=drive_sigma, name='rabi_pulse_%d' % ii)\n",
    "    \n",
    "    # add commands to schedule\n",
    "    schedule = pulse.Schedule(name='Rabi Experiment at drive amp = %s' % drive_amp)\n",
    "    \n",
    "    schedule |= rabi_pulse(pulse.DriveChannel(qubit))\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may plot any of the experiment schedules with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules[-1].draw(channels=[pulse.DriveChannel(qubit), pulse.MeasureChannel(qubit)],\n",
    "                   label=True, plot_range=(0, 4096))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "After `Schedule`s are now build for the Rabi experiment. We now assemble the Qobj to be sent to the quantum computing system. We use `meas_level=MeasLevel.KERNELED` (otherwise known as measurement level `meas_level=1`)  to get the default kernel integrated results from the system, and we automatically average them with `meas_return=MeasReturnType.AVERAGE` (otherwise known as `meas_return='avg'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_qobj = assemble(schedules, backend, \n",
    "                     meas_level=MeasLevel.KERNELED, \n",
    "                     meas_return=MeasReturnType.AVERAGE, \n",
    "                     shots=shots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After building our rabi experiment Qobj we now submit it to the backend to be run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(rabi_qobj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We receive a job object for our experiment which we may be used to monitor and get the results of the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5df01a6e60dba60011df07e4'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.VALIDATING: 'job is being validated'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now ask for the result from the quantum computing system, which will block until completion (or timeout)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "rabi_result = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analyze the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The result for our experiment is stored in `pulse.MemorySlot(qubit)`. Looking at the signal amplitude, we will observe an oscillation of the signal as a function of the drive amplitude. This is known as a Rabi oscillation and corresponds to the qubit being periodically driven to its excited state and back again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we extract the data from the returned system `memory`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "for i in range(exps):\n",
    "    qubit_rabi_data[i] = rabi_result.get_memory(i)[qubit]*scale_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot and fit the extracted signal to a sinusoid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi Amplitude 0.305795\n"
     ]
    },
    {
     "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": [
    "rabi_amp_data = get_amplitude(qubit_rabi_data)\n",
    "\n",
    "fit_func = lambda x,A,B,Omega,phi: (A*np.cos(2*np.pi*x/Omega+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, drive_amps, rabi_amp_data, [7.0,0.0,1.0,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "pi_amp = (np.pi-fitparams[3])*fitparams[2]/4/np.pi\n",
    "\n",
    "plt.scatter(drive_amps, rabi_amp_data)\n",
    "plt.plot(drive_amps, fit_func(drive_amps, *fitparams), color='red')\n",
    "plt.axvline(pi_amp, color='black', linestyle='dashed')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(-8, 8)\n",
    "plt.xlabel('Pulse amplitude, a.u.', fontsize=20)\n",
    "plt.ylabel('Signal, a.u.', fontsize=20)\n",
    "plt.title('Rough Pi Amplitude Calibration', fontsize=20)\n",
    "\n",
    "print('Pi Amplitude %f'%(pi_amp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the fit we obtain the Rabi oscillation frequency. We may use this to extract the amplitude of a $\\pi$ pulse. This is the required amplitude to drive the qubit from the ground state ($|0\\rangle$) to the excited state ($|1\\rangle$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring the IQ distribution from the |0> and |1> states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this experiment, we observe a change of measured signals depending on a quantum state of qubit0. By setting `drive_amp` to `pi_amp` we previously obtained, we can apply a $\\pi$-pulse to the qubit0. To observe a distribution on the IQ plane, we set `meas_return` to `MeasReturnType.SINGLE` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                amp=pi_amp,\n",
    "                                sigma=drive_sigma,\n",
    "                                name='pi_pulse')\n",
    "    \n",
    "# excited\n",
    "excited_exp = pulse.Schedule(name='pi_exp')\n",
    "excited_exp |= rabi_pulse(pulse.DriveChannel(qubit))\n",
    "excited_exp |= measure << rabi_pulse.duration\n",
    "\n",
    "# ground\n",
    "ground_exp = pulse.Schedule(name='no_pi_exp')\n",
    "ground_exp |= measure << rabi_pulse.duration\n",
    "\n",
    "excited_exp_schedules = [ground_exp, excited_exp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "excited_exp_qobj = assemble(excited_exp_schedules, backend, meas_level=MeasLevel.KERNELED, \n",
    "                            meas_return=MeasReturnType.SINGLE, shots=shots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "excited_exp_job = backend.run(excited_exp_qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.VALIDATING: 'job is being validated'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excited_exp_job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "excited_exp_result = excited_exp_job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_data = excited_exp_result.get_memory(0)[:, qubit]*scale_factor\n",
    "excited_data = excited_exp_result.get_memory(1)[:, qubit]*scale_factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Q (a.u.)')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(np.real(ground_data), np.imag(ground_data), s=5, cmap='viridis',c='blue',alpha=0.5, label='|0>')\n",
    "plt.scatter(np.real(excited_data), np.imag(excited_data), s=5, cmap='viridis',c='green',alpha=0.5, label='|1>')\n",
    "ground_center = np.mean(ground_data)\n",
    "excited_center = np.mean(excited_data)\n",
    "\n",
    "plt.scatter(np.real(ground_center), np.imag(ground_center), c='blue', s=200, alpha=1.0)\n",
    "plt.scatter(np.real(excited_center), np.imag(excited_center), c='green', s=200, alpha=1.0)\n",
    "\n",
    "plt.title('Kernel integrated I-Q scatter plot', fontsize=20)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('I (a.u.)', fontsize=16)\n",
    "plt.ylabel('Q (a.u.)', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step in controlling a quantum computer with pulse is to calibrate a two-qubit gate. For more information on how to do this see the next tutorial [Calibrating a Two Qubit Gate](1b_calibrating_a_two_qubit_gate.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T01:29:36.602627Z",
     "start_time": "2019-08-22T01:29:36.594016Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Dec 11 12:02:58 2019 EST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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