Qubit Rabi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.pulse import *\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "from qiskit.visualization import plot_histogram\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "import qiskit.tools.jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pranavgokhale/anaconda3/lib/python3.7/site-packages/qiskit/providers/ibmq/ibmqfactory.py:181: UserWarning: Credentials are already in use. The existing account in the session will be replaced.\n",
      "  warnings.warn('Credentials are already in use. The existing '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-ornl', group='bes-qis', project='argonne')>]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account() # Load account from disk\n",
    "IBMQ.providers()    # List all available providers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(group='bes-qis')\n",
    "backend = provider.get_backend('ibmq_boeblingen')\n",
    "system = pulse.PulseChannelSpec.from_backend(backend)\n",
    "config = backend.configuration()\n",
    "defaults = backend.defaults()\n",
    "cmd_def = pulse.CmdDef.from_defaults(backend.defaults().cmd_def, backend.defaults().pulse_library)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a5027a8cf78400abfe2563944821f46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(VBox(layout=Layout(max_width='610px', min_width='610px')),), layout=Layout(max_height='500…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$('div.job_widget')\n",
       "        .detach()\n",
       "        .appendTo($('#header'))\n",
       "        .css({\n",
       "            'z-index': 999,\n",
       "             'position': 'fixed',\n",
       "            'box-shadow': '5px 5px 5px -3px black',\n",
       "            'opacity': 0.95,\n",
       "            'float': 'left,'\n",
       "        })\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%qiskit_job_watcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.    0.005 0.01  0.015 0.02  0.025 0.03  0.035 0.04  0.045 0.05 ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 11\n",
    "drive_amps = np.linspace(0, 0.05, num_drive_amps)\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "measure = cmd_def.get('measure', qubits=config.meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "qobj = assemble(schedules, backend, shots=512)\n",
    "job = backend.run(qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d890be14a13e1001863a9a6'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a258bb3c8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSZuAV6hKUgN5y19Jy/K+61ub5S7p+3hV9NbnYRlJ38erorc+y13S9/Gq6K3Pcpf0fbwqeuuz3CV9H6+K3vpcUO0izy5QU3hV9NZnuXeJZxeoabwqemvzsEyXeHaBpM3Ecu8Szy6QtJlY7l3i2QWSNhPLvUs8u0DSZuKCapd4doGkzcRy7yLPLpC0WXhYRpIayHKXpAay3CWpgSx3SWogy12SGqijck+yP8lUkukkR5d5/g1JPtV6/gtJdnY7qCSpcyuWe5IB4DhwL7AXeCDJ3iXTfgF4oar+KvBx4GPdDipJ6lwne+73ANNVdamqXgZOAgeWzDkAfLL1+Ang7yZJ92JKklajk3IfAZ5r277cGlt2TlVdA/4MeNPSF0ryYJLJJJOzs7NrSyxJWlFfr1CtqhPACYAks0n+tMNvvQP4Vs+Cdd9Wywtm7hcz98dWy7yavG/pZFIn5T4D3Nm2vaM1ttycy0luAf4y8PzrvWhVDXcSECDJZFWNdjp/o221vGDmfjFzf2y1zL3I28lhmXPA7iS7ktwKHALOLJlzBvhg6/H7gc9WVXUvpiRpNVbcc6+qa0kOAxPAAPCJqrqY5BFgsqrOAP8F+K9JpoFvs/APgCRpg3R0zL2qzgJnl4w91Pb4u8A/6G60RU708LV7YavlBTP3i5n7Y6tl7nreePREkprH2w9IUgNtinJPcnuSTyf5SuvP215j3gdbc76S5INt4/8+yXNJXuxD1jXfiiHJsdb4VJKxXmddb+Ykb0ryuSQvJvmNfuVdZ+b3JnkyyYXWn39nC2S+J8lTra8vJfn7mzlv2/M/1vrZ+Eg/8q4nc5KdSebbPuff3OyZW8+9PckfJ7nY+pl+Y8dvXFUb/gU8BhxtPT4KfGyZObcDl1p/3tZ6fFvruZ8G3gy82OOcA8BXgR8HbgW+BOxdMudfAL/ZenwI+FTr8d7W/DcAu1qvM9CHz3Y9mX8Q+JvAh4Hf6OPPw3oy7wO2tx6/DZjZApn/EnBL6/GbgW/e2N6MeduefwL4HeAjW+Az3gn8Sb9+hruU+RbgaeBvtLbftJrO2BR77iy+fcEngYPLzBkDPl1V366qF4BPA/sBqurzVfWNPuRcz60YDgAnq+qlqvoaMN16vU2buar+oqr+CPhuH3K2W0/m81V1pTV+ERhM8oZNnvk7tXBlN8AbgX4shK3rtiJJDgJfY+Ez7peteCuU9WT+GeDpqvoSQFU9X1XXO33jzVLuP9pWzv8X+NFl5nRyG4ReW8+tGDYqf9duH9FH3cr8PuCLVfVSj3Ium6dlVZmTvDPJReAC8OG2st90eZP8EPBvgV/tccal1vtzsSvJ+ST/I8nf6nXYpXlaVpP5rUAlmUjyxST/ZjVv3LfbDyT5A+CvLPPUR9s3qqqSeAqP1iXJXSzcnfRnNjpLJ6rqC8BdSf468Mkkv18LpxhvRg8DH6+qFzd2p3hVvgH8WFU9n+QngdNJ7qqqP9/oYK/jFhYOi/4U8B3gM0merKrPdPrNfVFV73mt55L8vyRvrqpvJLlxzHGpGeDdbds7gD/sasiVredWDJ18by/05PYRPbauzEl2AL8L/OOq+mrv4y7Kc8OaPueqejYLJwa8DZjsXdx15X0n8P4kjwFDwPeSfLeqer3ovubMtXDQ+iWAqnoyyVdZ2DPu5We8rsws7OX/z6r6FkCSs8BPAB2Ve18XF15n0WGcxQuqjy0z53YWjvHd1vr6GnD7kjm9XlC9hYWF3F28ujhy15I5v8jixZHHW4/vYvGC6iX6s6C65sxtz3+I/i6orudzHmrNv7/PP8PrybyLVxdU3wJcAe7YrHmXzHmY/i2oruczHr7x942Fxc2Zpf2xCTPfBnyR1oI78AfAz3b83v34n9LBB/AmFv41+krrP+D21vgo8Ftt8/4pCwuR08A/aRt/jIV/5b7X+vPhHmb9e8CXWVgB/2hr7BHgvtbjN7JwBsE08L+BH2/73o+2vm8KuLePn+96Mn+dhVtKvNj6bPdu5szArwB/ATzV9vUjmzzzP2JhYfKp1l/mg5s575LXeJg+lfs6P+P3LfmMf26zZ2499/Ot3H/CMju9r/flFaqS1ECb5WwZSVIXWe6S1ECWuyQ1kOUuSQ1kuUtSA1nuktRAlrskNZDlLkkN9P8B4wZ0cqvZRaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(drive_amps, frac_ones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Try again, zoomed in to optimal amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.015  0.0155 0.016  0.0165 0.017  0.0175 0.018  0.0185 0.019  0.0195\n",
      " 0.02   0.0205 0.021  0.0215 0.022  0.0225 0.023  0.0235 0.024  0.0245\n",
      " 0.025 ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 21\n",
    "drive_amps = np.linspace(0.015, 0.025, num_drive_amps)\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "measure = cmd_def.get('measure', qubits=config.meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d891e034a13e1001863a9ba'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qobj = assemble(schedules, backend, shots=1024)\n",
    "job = backend.run(qobj)\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.015, 0.025)"
      ]
     },
     "execution_count": 105,
     "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(drive_amps, frac_ones)\n",
    "plt.xticks(np.arange(0.015, 0.025, step=0.001))\n",
    "plt.xlim(0.015, 0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amplitude is 0.02\n",
      "sigma is 16\n",
      "duration is 128\n",
      "integral is (0.8020699602267553+0j)\n"
     ]
    }
   ],
   "source": [
    "print('amplitude is %s' % drive_amps[10])\n",
    "print('sigma is %s' % drive_sigma)\n",
    "print('duration is %s' % drive_samples)\n",
    "print('integral is %s' % sum(schedules[10].instructions[0][1].command.samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Try a faster (shorter duration) X pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06       0.06444444 0.06888889 0.07333333 0.07777778 0.08222222\n",
      " 0.08666667 0.09111111 0.09555556 0.1       ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 10\n",
    "drive_amps = np.linspace(0.06, 0.10, num_drive_amps)\n",
    "drive_samples = 32\n",
    "drive_sigma = 4\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d89245801bf92001948dc65'"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qobj = assemble(schedules, backend, shots=512)\n",
    "job = backend.run(qobj)\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06, 0.1)"
      ]
     },
     "execution_count": 138,
     "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(drive_amps, frac_ones)\n",
    "plt.xticks(np.arange(0.06, 0.10, step=0.01))\n",
    "plt.xlim(0.06, 0.10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Go even faster (this is the best one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.24       0.25777778 0.27555556 0.29333333 0.31111111 0.32888889\n",
      " 0.34666667 0.36444444 0.38222222 0.4       ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 10\n",
    "drive_amps = np.linspace(0.24, 0.40, num_drive_amps)\n",
    "drive_samples = 8\n",
    "drive_sigma = 1\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d89256048b726001ab3c3a1'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qobj = assemble(schedules, backend, shots=512)\n",
    "job = backend.run(qobj)\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.24, 0.4)"
      ]
     },
     "execution_count": 145,
     "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(drive_amps, frac_ones)\n",
    "plt.xticks(np.arange(0.24, 0.40, step=0.01))\n",
    "plt.xlim(0.24, 0.40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Super fast (too fast):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.44       0.45333333 0.46666667 0.48       0.49333333 0.50666667\n",
      " 0.52       0.53333333 0.54666667 0.56      ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 10\n",
    "drive_amps = np.linspace(0.44, 0.56, num_drive_amps)\n",
    "drive_samples = 4\n",
    "drive_sigma = 0.5\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d892dc348b726001ab3c3ad'"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qobj = assemble(schedules, backend, shots=512)\n",
    "job = backend.run(qobj)\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.44, 0.56)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD8CAYAAACYebj1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAFAhJREFUeJzt3X+Q3Hd93/Hni5MFAkMkYkGxbCy5yKK0UAsuTgl14yFj5GkmtgkzIJtOLNrG0wEzDQ1q7eaPUDOdEJSUZAbPdEwG8mMGNMTjaJRAcrgYl5baMzrFsoXkkS2LNL4zUwvbauOixrJ494/93pf1+Xy30u7e7emej5md2+9nv9/v+/Pdu9vX7ue73+83VYUkSQCvWOoOSJJGh6EgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKk1qql7sBsF1xwQW3cuHGpuyFJy8r+/ft/UFXr+13PyIXCxo0bmZycXOpuSNKykuR/DmI9Dh9JklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp1VMoJLkmyZEkR5PcOsfjn0tyoLk9muTErMdfl2QqyecH1XFJ0uAteD2FJGPAHcDVwBSwL8neqjo8M09VfaJr/o8DW2et5tPAtwfSY0nS0PTySeEK4GhVHauq54HdwHXzzH8D8JWZiSTvAt4IfKOfjkqShq+XUNgAPNE1PdW0vUSSS4BNwL3N9CuA3wY+OV+BJDcnmUwyefz48V76LUkagkHvaN4O3FVVp5vpjwJfr6qp+Raqqjuraryqxtev7/sSo5Kks9TLNZqngYu7pi9q2uayHfhY1/S7gSuTfBQ4H1id5LmqesnOaknS0uslFPYBm5NsohMG24EbZ8+U5K3AOuD+mbaq+nDX4zuAcQNBkkbXgsNHVfUCcAswATwCfLWqDiW5Pcm1XbNuB3ZXVQ2nq5KkYcuovYaPj4/X5OTkUndDkpaVJPurarzf9XhEsySpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklq9nDpbI2bPg9PsmjjCkydOcuHaNezctoXrt855MTxJOiOGwjKz58Fpbrv7ICdPdS5uN33iJLfdfRDAYJDUN4ePlpldE0faQJhx8tRpdk0cWaIeSTqXGArLzJMnTp5RuySdCUNhmblw7ZozapekM2EoLDM7t21hzXljL2pbc94YO7dtWaIeSTqXuKN5mZnZmey3jyQNg6GwDF2/dYMhIGkoHD6SJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLU8olnnLC9GJJ05Q0HnJC9GJJ0dh490TvJiRNLZMRR0TvJiRNLZMRR0TvJiRNLZ6SkUklyT5EiSo0lunePxzyU50NweTXKiab88yf1JDiV5OMmHBr0B0ly8GJF0dhbc0ZxkDLgDuBqYAvYl2VtVh2fmqapPdM3/cWBrM/lD4Jeq6rEkFwL7k0xU1YlBboQ0mxcjks5OL98+ugI4WlXHAJLsBq4DDr/M/DcAvw5QVY/ONFbVk0meAtYDhoKGzosRSWeul+GjDcATXdNTTdtLJLkE2ATcO8djVwCrgcfneOzmJJNJJo8fP95LvyVJQzDoHc3bgbuq6kXfBUzyJuCPgI9U1Y9mL1RVd1bVeFWNr1+/fsBdkiT1qpdQmAYu7pq+qGmby3bgK90NSV4HfA34tap64Gw6KUlaHL2Ewj5gc5JNSVbTeeHfO3umJG8F1gH3d7WtBv4E+MOqumswXZYkDcuCO5qr6oUktwATwBjwxao6lOR2YLKqZgJiO7C7qqpr8Q8C/wT4ySQ7mrYdVXVgYFsg6ZzkuauWRl78Gr70xsfHa3Jycqm7IWkJzT53FXSOM/mNX3y7wfAykuyvqvF+1+MRzZJGjueuWjqGgqSR47mrlo6hIGnkeO6qpWMoSBo5nrtq6XiRHUkjx3NXLR1DQdJI8txVS8PhI0lSy08KC/AAGkkriaEwDy/+LmmlcfhoHh5AI2mlMRTm4QE0klYaQ2EeHkAjaaUxFObhATSSVhp3NM/DA2gkrTSGwgI8gEbSSuLwkSSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklojFwoHp/837/nMvex5cHqpuyJJK87IhQL8+LoFBoMkLa6RDAXwugWStBRGNhTA6xZI0mIb6VDwugWStLhGNhS8boEkLb6RPHX2Bq9bIElLYuRC4e0bfoLv3Prepe6GJK1IIzt8JElafIaCJKllKEiSWj2FQpJrkhxJcjTJrXM8/rkkB5rbo0lOdD12U5LHmttNg+y8JGmwFtzRnGQMuAO4GpgC9iXZW1WHZ+apqk90zf9xYGtz//XArwPjQAH7m2WfHehWSJIGopdPClcAR6vqWFU9D+wGrptn/huArzT3twH3VNUzTRDcA1zTT4clScPTSyhsAJ7omp5q2l4iySXAJuDeM1k2yc1JJpNMHj9+vJd+S5KGYNA7mrcDd1XV6TNZqKrurKrxqhpfv379gLskSepVL6EwDVzcNX1R0zaX7fx46OhMl5UkLbFeQmEfsDnJpiSr6bzw7509U5K3AuuA+7uaJ4D3JVmXZB3wvqZNkjSCFvz2UVW9kOQWOi/mY8AXq+pQktuByaqaCYjtwO6qqq5ln0nyaTrBAnB7VT0z2E2QJA1Kul7DR8L4+HhNTk4udTckaVlJsr+qxvtdj0c0S5JahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJaq5a6A9JKsufBaXZNHOHJEye5cO0adm7bwvVbNyx1t0a2X6NmJTxPhoK0SPY8OM1tdx/k5KnTAEyfOMltdx8EWNIXllHt16hZKc+Tw0fSItk1caR9QZlx8tRpdk0cWaIedYxqv0bNSnmeDAVpkTx54uQZtS+WUe3XqFkpz5OhIC2SC9euOaP2xTKq/Ro1K+V5MhSkRbJz2xbWnDf2orY1542xc9uWJepRx6j2a9SslOfJHc3SIpnZGTlq314Z1X6NmpXyPKWqFp4puQb4XWAM+L2q+swc83wQ+BRQwENVdWPT/lng5+l8KrkH+Nc1T9Hx8fGanJw88y2RpBUsyf6qGu93PQt+UkgyBtwBXA1MAfuS7K2qw13zbAZuA95TVc8meUPT/jPAe4B3NLP+d+Bngfv67bgkafB62adwBXC0qo5V1fPAbuC6WfP8MnBHVT0LUFVPNe0FvApYDbwSOA/4X4PouCRp8HoJhQ3AE13TU01bt8uAy5J8J8kDzXATVXU/8C3g+81toqoemV0gyc1JJpNMHj9+/Gy2Q5I0AIP69tEqYDNwFXAD8IUka5O8Bfh7wEV0guS9Sa6cvXBV3VlV41U1vn79+gF1SZJ0pnoJhWng4q7pi5q2blPA3qo6VVXfAx6lExLvBx6oqueq6jngz4F3999tSdIw9BIK+4DNSTYlWQ1sB/bOmmcPnU8JJLmAznDSMeCvgZ9NsirJeXR2Mr9k+EiSNBoWDIWqegG4BZig84L+1ao6lOT2JNc2s00ATyc5TGcfws6qehq4C3gcOAg8ROerqn86hO2QJA1AT8cpLCaPU5CkMzeo4xQ8zYUkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqeXlOCVpGdvz4DS7Jo6w+u+85V2DWJ+hIEnL1J4Hp7nt7oOcPHV6YOt0+EiSlqldE0cGGghgKEjSsvXkiZMDX6ehIEnL1IVr1wx8nYaCJC1TO7dtYc15YwNdpzuaJWmZun7rBqCzb+H7A1qnF9mRpHOAF9mRJA2coSBJahkKkqSWoSBJavntIw3EzPlXnjxxkgvXrmHnti3tNyMkLR+Ggvo2+/wr0ydOctvdBwEMBmmZcfhIfZvr/CsnT51m18SRJeqRpLNlKKhvL3f+lWGcl0XScBkK6tvLnX9lGOdlkTRchoL6Ntf5V9acN8bObVuWqEeSzpY7mtW37vOv+O0jaXkzFDQQ12/dYAhI5wCHjyRJLUNBktQyFCRJLUNBktQyFCRJrZ5CIck1SY4kOZrk1peZ54NJDic5lOTLXe1vTvKNJI80j28cTNclSYO24FdSk4wBdwBXA1PAviR7q+pw1zybgduA91TVs0ne0LWKPwT+Y1Xdk+R84EcD3QJJ0sD08knhCuBoVR2rqueB3cB1s+b5ZeCOqnoWoKqeAkjyNmBVVd3TtD9XVT8cWO8lSQPVSyhsAJ7omp5q2rpdBlyW5DtJHkhyTVf7iSR3J3kwya7mk8eLJLk5yWSSyePHj5/NdkiSBmBQO5pXAZuBq4AbgC8kWdu0Xwl8Evgp4FJgx+yFq+rOqhqvqvH169cPqEuSpDPVSyhMAxd3TV/UtHWbAvZW1amq+h7wKJ2QmAIONENPLwB7gHf2321J0jD0Egr7gM1JNiVZDWwH9s6aZw+dTwkkuYDOsNGxZtm1SWbe/r8XOIwkaSQtGArNO/xbgAngEeCrVXUoye1Jrm1mmwCeTnIY+Baws6qerqrTdIaOvpnkIBDgC8PYEElS/1JVS92HFxkfH6/Jycml7oYkLStJ9lfVeL/r8YhmSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVIrVbXUfXiRJH8DHFnEkhcAP7Ce9ax3ztc7l7cNYEtVvbbflawaRE8G7EhVjS9WsSST1rOe9c79eufyts3UG8R6HD6SJLUMBUlSaxRD4U7rWc961lvmtZZtvZHb0SxJWjqj+ElBkrREFjUUklyT5EiSo0lunWe+DySpJOOz2t+c5LkknxxmvSQbk5xMcqC5/edhb1+SdyS5P8mhJAeTvGqI2/fhrm07kORHSS4fYr3zkvxBs12PJLltoVp91lud5EtNvYeSXNVvrSQ7khzves7+ZddjNyV5rLndNIhtW6DeXyQ5keTPeqnVT70kl3f9XT6c5ENDrndJkr9s2g4l+VfDrNf1+OuSTCX5/LDrJTnd1b53Eeq9Ock3mv+9w0k2zlusqhblBowBjwOXAquBh4C3zTHfa4FvAw8A47Meuwv4Y+CTw6wHbAS+u1jbR+erwQ8D/7CZ/klgbNjPZ/P424HHh7x9NwK7m/uvBv4K2DjEeh8DvtTcfwOwH3hFP7WAHcDn51j29cCx5ue65v66frft5eo1j/0c8AvAnw3qb3Oe7bsM2NzcvxD4PrB2iPVWA69s7p/f/K1cOMzns3n8d4EvzzfPAH9/z/XyextgvfuAq7ue01fPV28xPylcARytqmNV9TywG7hujvk+Dfwm8P+6G5NcD3wPOLQY9c5CP/XeBzxcVQ8BVNXTVXV6iPW63dAsu5B+6hXwmiSrgDXA88D/GWK9twH3AlTVU8AJYL7vi/daay7bgHuq6pmqeha4B7hmgWX6qUdVfRP4m17n76deVT1aVY81958EngLWD7He81X1t83kK+ltNKOv5zPJu4A3At/ocZG+6p2Fs66X5G3Aqqq6B6CqnquqH863zGKGwgbgia7pqaatleSdwMVV9bVZ7ecD/w74D4tRr7EpyYNJ/muSK4dc7zKgkkw0H53/7ZDrdfsQ8JUh17sL+L903mX+NfBbVfXMEOs9BFybZFWSTcC7gIv7qdX4QDOEcleSmfX1uuyg6p2NgdRLcgWdd6qPD7NekouTPNys4zebMBpKvSSvAH4b6GlIut96jVclmUzyQPNmd5j1LgNOJLm7eT3blWRsvmIjs6O5+eX8J+BX53j4U8Dnquq5Rar3feDNVbUV+DfAl5O8boj1VgH/GPhw8/P9SX5uiPVm5vlp4IdV9d1+avVQ7wrgNJ3hh03Arya5dIj1vkjnH2cS+B3gfzT1+/GndIa83kHn08Af9Lm+ZVUvyZuAPwI+UlU/Gma9qnqiaX8LcFOSNw6x3keBr1fV1ABq9FIP4JLqHOl8I/A7Sf7uEOutAq6kE3o/RWcIasd8K1rMUJjmxe/WLmraZrwW+AfAfUn+CvhHwN50dh7+NPDZpv1XgH+f5JZh1auqv62qpwGqaj+dd0aXDXH7poBvV9UPmo92XwfeOcR6M7bT26eEfuvdCPxFVZ1qhnO+w/zDOX3Vq6oXquoTVXV5VV0HrAUe7aPWzJDezLDG79H59NHTsgOudzb6qte8Ifoa8GtV9cCw63XN8yTwXTovasOq927gluZv6LeAX0rymSHWo6qmm5/H6Iz3bx1ivSngQDP09AKwh4VeW85kh0c/NzqJdYzOO8WZnSV/f57572PuHaOforcdzWddj86Y6Vhz/9LmF/D6IdZbB/wlnZ2wq4D/Avz8MJ9POm8IpoFLh/37ozP096Xm/muAw8A7hljv1cBrmvtX0wncvmoBb+q6/37ggeb+6+ns61rX3L43iL+Vl6vX1XYVve9o7mf7VgPfBH6ll1oDqHcRsKbr/+JR4O3Dfj6b9h30tqO5n+1bx493pF8APMYcX6AYYL2xZv71zfSXgI/NW6/XX/QgbsA/bX7Jj9N51wFwO3DtHPPeRx+h0E894AN0dmgfoPNi/QvD3j7gnzU1vwt8dhHqXTXXP8aQns/z6Xxr7BCdQNg55Hob6Zxp9xE6AXtJv7WA32j6/xDwLeCtXcv+c+Boc/vIILZtgXr/DTgOnKTzTnDbsOo1f5en6PwvzNwuH2K9q+l8E++h5ufNw34+u9axgx5Coc/t+xngYNN+EPgXi/D3MvOcHgR+H1g9Xy2PaJYktUZmR7MkaekZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKk1v8H2Tb6WZwXmO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(drive_amps, frac_ones)\n",
    "plt.xticks(np.arange(0.44, 0.56, step=0.01))\n",
    "plt.xlim(0.44, 0.56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## A little slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.34       0.36444444 0.38888889 0.41333333 0.43777778 0.46222222\n",
      " 0.48666667 0.51111111 0.53555556 0.56      ]\n"
     ]
    }
   ],
   "source": [
    "num_drive_amps = 10\n",
    "drive_amps = np.linspace(0.34, 0.56, num_drive_amps)\n",
    "drive_samples = 6\n",
    "drive_sigma = 0.75\n",
    "\n",
    "print(drive_amps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = []\n",
    "for drive_amp in drive_amps:\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                                    amp=drive_amp,\n",
    "                                    sigma=drive_sigma,\n",
    "                                    name='rabi_pulse_with_drive_amp_%s' % drive_amp)\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[0].drive)\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5d893832a807ac0018bac7b1'"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qobj = assemble(schedules, backend, shots=512)\n",
    "job = backend.run(qobj)\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error checking job status using websocket, retrying using HTTP.\n"
     ]
    }
   ],
   "source": [
    "frac_ones = []\n",
    "for i in range(num_drive_amps):\n",
    "    counts = job.result().get_counts(i)\n",
    "    num_ones = sum({freq for (bitstring, freq) in counts.items() if bitstring.endswith('1')})\n",
    "    frac_ones.append(num_ones / 512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.34, 0.56)"
      ]
     },
     "execution_count": 173,
     "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(drive_amps, frac_ones)\n",
    "plt.xticks(np.arange(0.34, 0.56, step=0.01))\n",
    "plt.xlim(0.34, 0.56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Compare to calibrated X gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule = pulse.Schedule()\n",
    "schedule |= cmd_def.get('x', qubits=[0])\n",
    "schedule |= measure << schedule.duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5d8949641765500018c3e487\n"
     ]
    }
   ],
   "source": [
    "job = execute(schedule, backend=backend)\n",
    "print(job.job_id())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.87109375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = job.result().get_counts()\n",
    "counts = {key: value for key, value in counts.items()}\n",
    "num_ones = sum({key: value for (key,value) in counts.items() if key[-1] == '1'}.values())\n",
    "print(num_ones/1024)\n",
    "plot_histogram(counts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}