qiskit/advanced/ignis/1_calibrating_a_qubit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Loading 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 need  to create a `PulseChannelSpec` object from the backend. This object will give you a data set to create a pulse schedule object.\n",
    "\n",
    "- `system.drives` : Returns a list of drive channels.\n",
    "- `system.controls` : Returns a list of control channels.\n",
    "- `system.measures` : Returns a list of measurement channels.\n",
    "- `system.acquires` : Returns a list of acquire channels.\n",
    "- `system.registers` : Returns a list of registers.\n",
    "- `system.memoryslots` : Returns a list of memory slots.\n",
    "\n",
    "Mapping between these channels and qubits is automatically created in the `PulseChannelSpec` instance.\n",
    "\n",
    "- `system.qubits` : Returns a list of qubit object.\n",
    "\n",
    "Each qubit object comprises `.drive`, `.controls`, and `.measure` channels, which are associated with the qubit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we use IBMQ Poughkeepsie, a 20 qubit client-only system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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(\n",
    "    'ibmq_poughkeepsie'\n",
    ")\n",
    "\n",
    "system = pulse.PulseChannelSpec.from_backend(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = backend.configuration()\n",
    "defaults = backend.defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create pulses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "User defined pulses can be created by using the `pulse.function` decorator. A python callback function which returns an `array_like` object can be used to create pulses. The first argument of the function should be `duration` specifing the number of data points. You are also able to get standard pulse templates from `pulse_lib`, and able to find calibrated pulses provided by the backend in `CmdDef`. In this notebook, we use a Gaussian pulse template from `pulse_lib.gaussian` and default measurement pulses from `CmdDef`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmd_def = pulse.CmdDef.from_defaults(defaults.cmd_def, defaults.pulse_library)\n",
    "\n",
    "measure = cmd_def.get('measure', qubits=config.meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate amplitude from complex signal\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 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. A `boxcar` kernel is used to process measurement data (**note**: currently the hardware overrides this specification, to be implemented in future versions). The result is stored in `memory_slot[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, 0.05, exps)\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "\n",
    "# scaling factor for data returned by system\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 `start_time`. The channel information is provided by `PulseChannelSpec`. You can create your `Schedule` by using syntax sugar:  \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",
    "All details are available in [Pulse Schedules](../terra/5_pulse_schedules.ipynb).\n",
    "Created `Schedule`s can be combined into a single job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(system.qubits[qubit].drive)\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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules[-1].draw(channels_to_plot=[system.qubits[qubit].measure, system.qubits[qubit].drive],\n",
    "                   scaling=10.0, label=True, plot_range=(0, 400))"
   ]
  },
  {
   "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=1` to get the default kernel integrated results from the system, and we automatically average them with `meas_return='avg'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_qobj = assemble(schedules, backend, meas_level=1, meas_return='avg', 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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d69e108edb7530011bcd1a1'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.RUNNING: 'job is actively running'>"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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 `memory_slot[qubit]`. The returned data are the integrated values of the received and mixed down stimulus signal applied to the measured resonator averaged over all shots. 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 correspond to the qubit being periodically driven to its excited state. This is how we control our qubits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we extract the data from the returned system `memory`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi Amplitude 0.016340\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,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, drive_amps, rabi_amp_data, [7.0,0.0,0.03,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "pi_amp = (np.pi-fitparams[3])*fitparams[2]/2/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, 0.05)\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 `single` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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(system.qubits[qubit].drive)\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": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excited_exp_qobj = assemble(excited_exp_schedules, backend, meas_level=1,\n",
    "                            meas_return='single', shots=shots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excited_exp_job = backend.run(excited_exp_qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.RUNNING: 'job is actively running'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excited_exp_job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512,)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excited_exp_result.get_memory(0)[:, 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "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": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Q (a.u.)')"
      ]
     },
     "execution_count": 99,
     "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": [
    "# Measuring CR Rabi oscillation\n",
    "\n",
    "In this section, we measure cross resonance Rabi oscillation on qubit0 and qubit1. The basic idea of cross resonance is applying a microwave pulse to a control qubit, but the pulse frequency is identical to a resonance frequency of target qubit. Because the oscillator frequency of drive channel used in previous experiments is adjusted to be at the resonance frequency of corresponding qubit, a cross resonance pulse should be applied from another channel to have different oscillator frequency. This is realized by a control channel (`U channel`) in this experiment.  \n",
    "It is to be noted that a role of control channels is not only applying CR pulses, but also anything as described in `config.hamiltonian`, and it strongly depends on the design of quantum computing system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qubit to use for exeperiment\n",
    "control_qubit = 0\n",
    "target_qubit = 1\n",
    "uchannel = 0\n",
    "\n",
    "# exp configuration\n",
    "exps = 30\n",
    "shots = 512\n",
    "\n",
    "# Rabi pulse\n",
    "cr_amps = np.linspace(0, 0.2, exps)\n",
    "cr_samples = 200\n",
    "cr_sigma = 4\n",
    "cr_rise_fall = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check if your control qubit and target qubit are connected by bus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[control_qubit, target_qubit] in config.coupling_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually we use a qubit with higher frequency as a control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaults.qubit_freq_est[control_qubit] > defaults.qubit_freq_est[target_qubit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For above qubit configuration, control channel 0 is available to realize CR pulse. For other configurations, you can refere to `CmdDef` to find proper control channel index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[UchannelLO(q=1, scale=(1+0j))]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.u_channel_lo[uchannel]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this experiment, we use two pulse echoed CR sequence to eliminate unwanted interaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "schedules = []\n",
    "for ii, cr_amp in enumerate(cr_amps):\n",
    "    # drive pulse\n",
    "    cr_rabi_pulse_p = pulse_lib.gaussian_square(duration=cr_samples,\n",
    "                                                amp=cr_amp,\n",
    "                                                sigma=cr_sigma,\n",
    "                                                risefall=cr_rise_fall,\n",
    "                                                name='cr_rabi_pulse_p%d' % ii)\n",
    "    cr_rabi_pulse_m = pulse_lib.gaussian_square(duration=cr_samples,\n",
    "                                                amp=-cr_amp,\n",
    "                                                sigma=cr_sigma,\n",
    "                                                risefall=cr_rise_fall,\n",
    "                                                name='cr_rabi_pulse_m%d' % ii)\n",
    "    pi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                  amp=pi_amp,\n",
    "                                  sigma=drive_sigma,\n",
    "                                  name='pi_pulse')\n",
    "    \n",
    "    # add commands to schedule\n",
    "    schedule = pulse.Schedule(name='CR Rabi Experiment at drive amp = %s' % cr_amp)\n",
    "    \n",
    "    schedule |= cr_rabi_pulse_p(system.controls[uchannel])\n",
    "    schedule |= pi_pulse(system.qubits[control_qubit].drive) << schedule.duration\n",
    "    schedule |= cr_rabi_pulse_m(system.controls[uchannel]) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules[1].draw(channels_to_plot=[system.qubits[control_qubit].drive,\n",
    "                                    system.qubits[target_qubit].drive,\n",
    "                                    system.qubits[control_qubit].measure,\n",
    "                                    system.qubits[target_qubit].measure,\n",
    "                                    system.controls[uchannel]],\n",
    "                   scaling=20.0, label=True, plot_range=(0, 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_rabi_qobj = assemble(schedules, backend, meas_level=1, meas_return='avg', shots=shots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(cr_rabi_qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d69e89e45953e0012355f06'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.RUNNING: 'job is actively running'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_rabi_result = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "control_qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "\n",
    "for i in range(exps):\n",
    "    target_qubit_rabi_data[i] = cr_rabi_result.get_memory(i)[target_qubit]*scale_factor\n",
    "    control_qubit_rabi_data[i] = cr_rabi_result.get_memory(i)[control_qubit]*scale_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can observe oscillation of measured signal only of the target qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CR Rabi oscillation')"
      ]
     },
     "execution_count": 41,
     "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": [
    "target_rabi_amp_data = get_amplitude(target_qubit_rabi_data)\n",
    "control_rabi_amp_data = get_amplitude(control_qubit_rabi_data)\n",
    "\n",
    "fit_func = lambda x,A,B,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, cr_amps, target_rabi_amp_data, [3.0,0.0,0.1,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "cr_pi_2_amp = (np.pi-fitparams[3])*fitparams[2]/4/np.pi\n",
    "\n",
    "plt.plot(cr_amps, fit_func(cr_amps, *fitparams), color='red')\n",
    "plt.axvline(cr_pi_2_amp, color='black', linestyle='dashed')\n",
    "plt.scatter(cr_amps, target_rabi_amp_data, label='target qubit')\n",
    "plt.scatter(cr_amps, control_rabi_amp_data, label='control qubit')\n",
    "plt.xlim(0, 0.2)\n",
    "plt.ylim(-5, 5)\n",
    "plt.legend()\n",
    "plt.xlabel('CR pulse amplitude, a.u.', fontsize=20)\n",
    "plt.ylabel('Signal, a.u.', fontsize=20)\n",
    "plt.title('CR Rabi oscillation', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the fit we obtain the CR Rabi oscillation frequency. We may use this to extract the amplitude of a CR π/2 pulse, which is the foundation of the CNOT gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029068217547629217"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_pi_2_amp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.3</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</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 Aug 21 21:29:36 2019 EDT</td></tr></table>"
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       "<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>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
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