community/hello_world/quantum_world.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../../images/qiskit-heading.gif\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*Quantum World*_ \n",
    "\n",
    "The latest version of this notebook is available on https://github.com/QISKit/qiskit-tutorial.\n",
    "\n",
    "***\n",
    "### Contributors\n",
    "Jay Gambetta and Ismael Faro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:48:16.163745Z",
     "start_time": "2018-09-25T16:48:15.794756Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit\n",
    "from qiskit import execute\n",
    "from qiskit import IBMQ\n",
    "from qiskit.backends.ibmq import least_busy\n",
    "from qiskit.wrapper.jupyter import *\n",
    "# import basic plot tools\n",
    "from qiskit.tools.visualization import plot_histogram, circuit_drawer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "IBMQ.load_accounts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQBackend('ibmqx4') from IBMQ()>,\n",
       " <IBMQBackend('ibmqx5') from IBMQ()>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_qasm_simulator') from IBMQ()>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:48:28.738800Z",
     "start_time": "2018-09-25T16:48:19.990885Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The least busy backend is ibmq_16_melbourne\n"
     ]
    }
   ],
   "source": [
    "backend = least_busy(IBMQ.backends(simulator=False))\n",
    "print(\"The least busy backend is \" + backend.name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:48:40.095856Z",
     "start_time": "2018-09-25T16:48:39.667560Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cb9fcf5333f4bb4b1ee3f399b180b54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<p style='font-size:16px;'>Job Status : job is being initialized </p>\"),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%qiskit_job_status\n",
    "q = QuantumRegister(2)\n",
    "c = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(q, c)\n",
    "qc.h(q[0])\n",
    "qc.cx(q[0], q[1])\n",
    "qc.measure(q, c)\n",
    "job_exp = execute(qc, backend=backend, shots=1024, max_credits=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:57:55.451595Z",
     "start_time": "2018-09-25T16:57:54.981066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10860d978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have made entanglement!\n"
     ]
    }
   ],
   "source": [
    "plot_histogram(job_exp.result().get_counts(qc))\n",
    "print('You have made entanglement!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The circuit that was run on the machine is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:57:57.758850Z",
     "start_time": "2018-09-25T16:57:56.558305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J+jEJHh4e0dHR8El7AAAWi0Wj0eh0+pMnT+AqVaampnyHiRgZGWVnZ8PRkdnZ2VevXoVdh/PmzXv27BnPKCpdXV0Oh9PT08NTCZx6IScr2YjUot7odPqkSZMcHR1lF6sEhO9GlJM1HBEZ+PDhw/z580NCQr7++mvYDyhhhXl5eefPnxd1rW8dHZ3Y2Fg6nX7v3j17e3s4howQf8148fHxcXd3z83N1dLSmjRpEhyi+fbt2/r6etDvF8PQ0PDhw4c5OTnJycmNjY3Gxsaurq7YdXpvMP29ffuW5zY8HGE+4B1JHqmpqSKVF57wLcLU1tbeuXNHIebSAwCamppgxtfS0rK0tOy/MOEdjohs1NfXL1iwoKio6Pr160uWLMGlTknW+vb19Z06daqbm9v06dOTkpII+W3+exq1jo4OfEwKRvizBhqNNuBNT3Nz8xcvXjAYDJ7kWFBQQKFQ4D1r+SFMizAXL14kkUiKsqogNmvQ3t5+wPl26MxxKCgvL58zZ05zc/ODBw+mTZuGS51wre/o6GixJ8NMnjw5IyPDzc3N1dX11q1bPNlJBvr7bmDfIly+GO7u7qDXkHKIwWA0NDS4urrK82rpA6LT6QsXLsRr8TRpE35lRg6Hg436RMlxEPvtt996enoeP36MV2bEa63vMWPG/PHHH25ubm/fvsUlMJH0lxzhWYMwQ+GE4ebmpqamFh0d3dHRge2Et03xOo0nxJMnT4qLixVieCMk/Mngq1ev4HJhZmZmfEfSIIPDrl27ysvLJ0+ejFeF2KhvyZcC0dHRSUhIWLNmDS6BiURg6FwuF5412NjY4LJ6h7a29p49e968eePt7V1WVgbX4IuMjLSwsFi3bp3k9ROFTqcbGBgsWLCA6ECEhQ0MWLZsWf8lra2tYUl5GXeGSA2OCxp1d3cHBwe7ubnJ/1rf/RP4aPaioqIPHz4AXHvig4ODm5qaTp06lZSUBPdMmTLlxo0bOC6dJGNMJjM+Pn7Tpk3oIfcIApFIpIULFwYGBhIdiKQEfqXx7XCElJSUTp48uWXLluTkZCaT+cknn7i5ucnzGnwDSkhIYDKZ/UybQZChRkVF5cyZM0RHgQOByXHVqlWenp4AANxP6ywtLQccQaIooqOjp02bNmiagyAIRmByVFFRkf0SLLJhYmKC12O/ampqQkNDcakKQRC5MhR7yrCZxZIjcPg+giBSpcD9fQiCINKDkiOCIAgfKDkiCILwMRT7HAcfeH/p4MGDenp6RMcCAAAVFRUKPR8UQQBKjoPDiBEjNDQ0MjMz5WTQaFdXl4JONwwICGCxWNijKBXFmzdvbGxs9u/fL/wh79+/r62t3bdvH45hsNnsFy9e0Gg0+JDF7OxsU1PTYcOG4fgW6enpMnvUDIkr+vOREYRAFhYWrq6up0+flkbl8KERAz46Qt4wmUwjI6Py8nLhD3F2dn727Bm+LWWz2R8+fNDW1oYTxpqbmzU0NER9Jmr/uru7NTQ0GhoacKxTEHTmiCB/09XV9fHx6fsQITm3aNEikZ6zCgBIS0vDPYy8vDxbW9vExMTZs2d3dnaqq6uHhYXB56AoIrm4CkMQBJE3KDkiCILwgZIjgiAIH0MxObJYLLxueMGngyEIMvgMxeS4adMmvJbXpVKpp06dwqUqBOHBYDBWrVrVe+V8RJaGYnKsq6urq6vDpSo7O7sTJ07gUhWC8Ni1a1daWhp8iDwie/gkRzabXVJSkpKSUlhYyOFwcKmTWEK2yNfXt6ioiOepYQgiubS0tKSkpEOHDinuOvmKTtLkyOFwIiIixo0bZ2ZmNmvWLGtraxMTE4U+mRKpRUuWLBkxYgSdTpdxkEMQh8Oprq7OzMxsbm5mMBj5+fnv378nOihp4XA4u3fvtrS0RIvME0ii5NjV1eXl5bVz586amhoAgIaGBgCgsrJy+/bt3t7eingKKWqLVFVVv/zyy7i4uPb2dgLCHQK4XO79+/cDAgLGjh1rbGzs4ODQ2Nj48OFDGxubkSNH2traBgUFFRYWEh0mzi5dupSVlRUeHo6eTUQgiZJjcHBwYmIiAMDf37+uro7JZObn53t5eQEAEhISjhw5gk+MMiRGi/z8/D58+HDt2jVZxzoEPHr0aNq0aXPnzr158+aiRYtiY2MfP35MpVK9vb0fPHhw9uzZiRMnhoeH29jYrFmzRqTJc/Kso6MjKCjI1dV1/vz5RMcytHHFVVxcDH/W1q5d23s/m8328PAAACgrK5eVlYldv/QsWrTI2dm5736xW0Sj0VxcXKQV7pDU3d391VdfAQAmT55848YNNpuNvWRubh4QEID9s729/fjx48OHD1dTUzt37pyE72toaLhnzx4JK5FQSEgImUzOysoS/pCFCxfOmjVLeiEJ6eXLlwCA+/fvc7lceJM9MjKS6KDEJ/6Z45kzZ1gsFoVC4ZmISiaTw8PDlZSUWCzWpUuXxE/bMid2i/z8/NLS0gbNmQvhmpqa5s6d+9///jcsLCwvL2/JkiX9rDakrq6+a9eu0tJSDw+PDRs27Nq1SxH7czANDQ1hYWHr16+3s7MjOpahTvzkGBMTAwBwcnIaPXo0z0tUKtXJyQkAcPnyZUmCkzGxW7R69WpVVdXz589LP8bBr7u7293dPScn59atW7t371ZSUhLmKF1d3ZiYmJCQkIiIiAMHDkg7SOkJCgrq6en5/vvviQ4EETc5lpWVwVsWtra2fAvA/QwG4927d2IHJ0uStGj48OHu7u7nz59X6HMWObF169aMjIxff/113rx5Ih1IIpG+++6777777ujRo1euXJFSeFJVVFR07ty5PXv2jB07luhYEHGTY25uLtwQlEosLCzgBuyGkH8StsjX17eysjIlJUU60Q0Vt27dOnv27E8//eTm5iZeDQcPHnR3d9+8eXNjYyO+scnA7t27hw8f/s033xAdCAJA3+TI5XKTk5N9fHwcHBxmz5797bff1tbWAgDgqCsrKys4m7ipqQmWNzU15Vsvtl9KPXFRUVGBgYGBgYF4DXaTsEVz5841NjaOjo7GJZihic1m79u3z97efuvWrWJXQiKRIiMj2Wx2SEgIjrHJQFpa2q1bt0JDQ7W1tYmOBQGAZ7Hb6urqNWvW9F4F8+HDh6dOnbp+/XpSUtLr16+nTJkC1/VtaWmBBQStJIw9QqStrU0KYYO4uDgYZ0BAwIgRIySvUMIWkclkHx+f8PDwlpYWXV1dyeMZgq5du1ZQUJCSkiLhhLkxY8Z8/fXXYWFh+/fv79t9LJ/QqG859PeZY2lpqb29Pcw4Ghoaq1evDg0N9ff3ZzKZHh4excXFAAAHBwdYGEsl8GERfamoqMANKSVH3EneIl9f387OztjYWClENyQkJCRMnjzZxcVF8qo2b97c09Nz8+ZNyauSjYsXL6JR3/Lmr79Ea2vr7Nmz4RX0zJkz4+PjDQ0N4Uuurq6rV6+G21OnToUbzc3NcGPAVAIfjNcXm83u6OjQ0tISL+6goKCAgAAAgIGBgXg18JC8RaampjNnzqTT6TAwRCRdXV337t3btm0bLrWNGTPG3t7+xo0bcLCknOvo6AgODkajvuXNX2eOO3bsqKioAABs2LAhJSUFy4wAgJUrV06YMAFuY2eO2HqIgu7PYvtZLFbfVysqKlxcXM6dOyd23C4uLitWrFixYoXY6ZWHhC2C/Pz8MjMzCwoKcAlpSCkoKGhra5s1axZeFc6ZM+fPP//EqzapOn78eFVVVVhYGNGBIP9ABgDk5eVduHABAGBmZhYZGclzYk8ikeC9CHV1dSsrK7gTS0mCVo3F9uvo6GA74+Litm3b5urqOnHixMePH+PcFMmI1yIeXl5eWlpa6LaMGKqrqwEA48ePx6vCsWPHtra2CjrNlx9o1LfcUgYAHDt2DP4jKiqK73MUX716BQD49NNPsbyJ3VATKZWcPn26pKTE2tp6+vTp6enp+LQAJ+K1iIempqaXl1dMTMyxY8fEvqtQXV39P//zPzJ7OG9vTU1N79+/NzMzk/1bMxgMAMDGjRsHHPVdVVWVlJRUUlLSfzG4ZOesWbNEem5yU1MTvISSUHV1NYPBYDAYJSUlLS0tLS0tTCazu7tbU1OTQqHo6OgYGBiYmpqampqeOXNG8lHflZWV1dXVc+bMkTxyScCfosDAwLq6OiqVCgA4ceLEb7/9RmxUzc3NjY2N5ubmIh01efJkZTabDfutP/nkE1dX176Fqqurq6qqQK9ratArlQi6OwG7L8E/U0lqaipMGfv375fb5ChSi/rq7u5WUVGR5H6rkpKSiooKIcmRRCIJOSMFd/1MEBQPl8sFAIj6hyCRSGJH8vr164cPH/7xxx+PHj3CVlPW19cfOXKkpqamrq4uiURqa2urqqpqb2+vqqrCPmkjRoz4/vvvv/jii7lz54p3e52ovxpf8FMkP2v0ivepplAoyi9evGhtbQUACLpLiK3k2js5jhw5Em6UlpbOmDGj71GlpaVwo/e4Fvn5/9WXeC3i0drampiYuGvXLkkiMTQ0vH79uiQ1KKKkpKTFixf/8ssvA563WlhYuLq6nj59uv9ip0+f3rx5c2pqqkjPrR89evS4ceOELw8AqKysjI2NvXLlSl5eHolEsrKy8vT0dHR0tLCwMDMz6+fTUl9fv2zZsvz8/Dlz5qSkpNDpdDKZ7OTktHLlSk9PT5EGqBkZGY0YMeL+/fsiRY47+NzqH3/8EXtu9bZt2xT4udVv3ryBW2PGjOFbAkuO2K1qAICNjQ3cgEN8+sJSCdZNKedwaVFsbGxHR8f69evxjm7wgx8/7NMoucrKSj09PZEyo6geP368ePFiExOTAwcOGBsbX7p0qaGhIT8//+TJk2vWrLG3t+9/xOv//u///vnnn8ePH//111/Ly8srKiqOHz/e3t4eEBBgZGS0cePGoqIi6QWPDIiMzTDBxrLwgMlRX18fdiJA1tbWcPX2rKwsvkfl5+cDAPT09AjpwBIDLi2i0+lOTk6C5tgg/ZgyZYqenl5ycjJeFd67d2/mzJl41cbj9u3b06dP/+KLLwoLC8PDw2tqam7durVmzRrs+mNAfUd9jx8//uuvv3769Glpaek333xz7do1Kysrd3f3vLw8KbUC6R9ZX18fbvGdMlxVVfX8+XPwz2tqAICamtrixYsBAI8ePeo7iRU7aunSpfJ8Kd2b5C0qLCx8/vy5n5+ftEMdlFRUVObPn49X531lZWVubq67uzsutfVWUlKyaNGihQsXtre3x8TEFBcX79ixA/sSCa+fUd8mJib//ve/Kysrf/rpp5ycHDs7u82bNw/iZ0LILTJ2HnT79m1s8QWovb3d19cX3hngSY4AAB8fHwBAZ2cnz+qHAICQkBDYHa5Yc6EkbBGdTtfW1l6+fLlUgxzEvL29S0pK7ty5I3lVJ06cUFNTW7RokeRVYVgsVnBwsLW1dWZmJp1Oz8nJWbVqlXgTWoQZ9a2lpbV9+/aioqLvvvvu/Pnz5ubmcLwdIjtcLhfrbjM0NLx161ZnZ+f79+8TExOtra2xYnfu3Om7Ui5cOoVEIp05cwbbefLkSXi/z93dXdASu/v27QMAREREiL1Ir5eXl7GxsbGxcXl5uajHCloJnCtBi7q7u/X19Tdu3ChqMAiGw+F8+umnU6ZMYbFY/RTjWQm8r/LycjU1tcDAQDFiELQSeEVFxbRp08hk8vbt25ubm8WouTdR1/quqKiAZ8ErV65saWnpWwCtBC4NgMvlJiYm9h6+gI1EcXR0xG4+vHv3ru/BVVVV2MgDOzu75cuXY+ehVCq1trZW0LtKnhydnZ3hG5WUlIh6bD/JUewWwSfPZGRkiBoM0tuDBw8AAD/88EM/ZfpPjiwWa968eXp6ek1NTWIEwDc5JiYm6unpGRoawq+9hOrr64cNG+bn5yfqgVFRURoaGlQqNTMzk+cllBylgQwA8PDwiI6Ohk/aAwCwWCwajUan0588eQIXdjU1NeU7sMDIyCg7OxuOjszOzr569Socyjtv3rxnz571noOoKMRuEZ1OnzRpkqOjo+xiHYxmzZq1devWAwcOiL1mxN69e5OTk6OiovT09HAJ6aeffvL09LS3t8/NzZ09e7bkFYq91vdXX32VmZmppaXl7Ox869YtySNB+vdXj4mPj4+7u3tubq6WltakSZPg8lxv376tr68H/DocMYaGhg8fPszJyUlOTm5sbDQ2NnZ1dcWu06UnNTVVSjWL0aLa2to7d+4o4tMW5dBPP/1UVFS0evXqCxcueHp6Cn8gh8M5cODA8ePHDx065O3tLXkkXC734MGDBw8eXL9+/dmzZ3FZLweu9X3gwAHx1vqePHlyRkaGl5eXu7v7yZMn0RInUvX331tHRwc+JgUD78+CfpMjRKPRaDQa7sERSKQWXbx4kUQirV27VqohDRHKysrXrl1buXKll5fXd999d+DAAb5TWnnU19f7+/vfunUrKCjou+++wyWSffv2/fjjj0FBQQcPHsSlQoDHWt9aWlo3btxYv379pk2bzM3N+c5qQ3DR30ypzMxMuDFgchzi6HT6woUL8Vo8DRk2bNjvv//+zTffhIaGTpo06cqVK/1MpmxpaTl48KCpqWlaWlpcXNzBgwfxGj1mbW194cIFHDNjamrqrVu3/v3vf0u41reqquqVK1diYmIG2RmJvOnvSgGeOSorK+P1N7h9+zYcSp2RkQEAePDgQWdnJwDA0dGR56RVgTx58qS4uBitN4UvJSWlsLCwVatW7d27d82aNVu3bnVzc3N1dR0zZkxHR0ddXd3NmzfhuJ9Hjx5xuVx/f//g4GB8u7nxvRTgcDiBgYGWlpa4TKAikUirVq2SvB6kHwKTI5fLhXNFbGxs4NQRySUkJPR+fmlSUlJSUhIAYN++fYqbHOl0uoGBwYIFC4gOZBCys7O7f/9+RkbGb7/9dv369V9//RXuf/v27fXr18lksqOjY2hoqJeXl4mJCbGhDigjIyMrK+v27dtorW9FIfDvVFRU9OHDB/DPKdUSio6OHmRrHTKZzPj4+E2bNqFPvPRMmzZt2rRpP/7444cPH6qrq1+8eDF8+HBLS8sxY8bg9bMtA46Ojs+fP7e3tyc6EERYAr/SqMNRGAkJCUwmU7EmAimuYcOGDRs2zNLSkuhAxKGkpIQyo2IRmBxXrVoFB1Io0I+z7EVHR0+bNk1Bv64IgvRDYHJUUVHBHik1yJiYmOC1en5NTU1oaCguVSEIIleGYk/Zzz//jFdVAy7WjyCIgsJ5bXoEQZDBASVHBEEQPlByRBAE4QMlRwRBED6G4g0ZBBGkubn5559/Pnv2LI51slis9vZ2+PhsDofT1tamra2N76NomUymeM90RfqBkiOC/G3Lli1tbW3Dhw/Hsc68vLw7d+6sW7dOTU2tqqrqypUr7u7u+OayqqoqNFkDdyg5Isjfjh8/jnuddDr9zp07wcHBurq66enpV65c2bJlC8pl8g/1OSIIgvCBkiOCIAgfKDkiCILwgZIjgiAIHyg5IgiC8IGSI4IgCB/yNZSHzWaXl5dXVlYaGBhYWlriO1BWShQxZgRBBiQv32QOhxMRETFu3DgzM7NZs2ZZW1ubmJicOHGC6Lj6o4gxIwgiJLlIjl1dXV5eXjt37qypqQEAaGhoAAAqKyu3b9/u7e3N4XCIDpAPRYwZQRDhyUVyDA4OTkxMBAD4+/vX1dUxmcz8/HwvLy8AQEJCwpEjR4gOkA9FjBlBEBFwiVZcXAwf3bd27dre+9lstoeHBwBAWVm5rKyMqPD4UsSYEaKcO3cOANDc3Mzlch8/fgwAePbsGdFBScXLly8BAPfv3+dyuR0dHQCAyMhIooMSH/FnjmfOnGGxWBQK5ejRo733k8nk8PBwJSUlFot16dIlosLjSxFjRhBEJMQnx5iYGACAk5NT33VKqFSqk5MTAODy5csERCaYIsaMIIhICE6OZWVl8IaGra0t3wJwP4PBePfunUwjE0wRY0YQRFQEJ8fc3Fy4ISjRWFhYwA3YnSEPFDFmBEFEJd1B4G/evDl//vzdu3crKio6OjrGjh07YcKEgIAANzc3WKCpqQlumJqa8q0B219eXi6NCKOiokpLSwEA+/btGzFihDCHEB4zgiAyIK3k2NXVFRoaeuTIERaLhe1sbW0tKCi4efOms7NzamoqAKClpQW+pK6uzrceTU1NuNHW1iaNOOPi4tLS0gAAAQEBQiZHwmNGEEQGpJIc29raFixYkJ6eDgCwt7f38vKiUqlNTU0VFRWxsbEVFRVUKhWWxBKNqqoq36pUVFSwOqURqhgUMWYEQUSFf3JksVgwM1IolKioqHXr1vV+9eDBg5GRkZ9//jn8Z3NzM9wYMNEwmcy+r3Z3d7e3t+vq6oodbVBQUEBAAADAwMBAyEMkjFkY+fn53d3d4h0rie7u7paWllGjRsn+rYX3/v17TU1NCoVCdCDCevPmDQAgJyeHyWQ2NDQAAIqKipSUlIiOC38MBgP+t6OjA16HVVZWZmVlERuVeJ9qAwMD/AeB79+/H9Z+9erVAQv7+/vDwiUlJXwL5OTkwAI7d+7svT83N3fOnDlwlQcqlRoVFYVP9EIQO2YhEf5hQhBk4sSJOJ85lpeXh4WFAQAWL17s6ek5YHktLS24IehECduvo6OD7czKynJxcSGTybt27dLT00tMTAwICKiurj506JCkDRCCeDELz87OLjc3t3dfrcx0dXW1trbK+Znju3fvNDQ04GR2hXDz5s1Dhw6lpKS0tra+f//e39//woULVlZWRMeFPwaDsWrVqlOnTo0ePVpfX3/GjBl79+6Fc2oJ1N3d3dTUZGhoKNJR+vr6OCfHo0ePwm/1gQMHhCmvra0NN4RPNFwud8OGDSwWKyMjAw6m2bt3r5ub2+HDh1esWCGDz5wYMYtK0CAhRBHBEV00Gg0+fRAAMGnSpE8//ZTouPAHb0VaW1vPnDmzp6dHSUnJzMxMcVuKZ3LkcDjXrl0DANBotKlTpwpzCJZoBN27qK2thRtYosnNzX358uW2bduwDKKkpHT06FE7O7ujR49evHhRkiYIQ4yYRfXx40dC+hwBACwWC04bl1vyHyGP9vZ2AEBLS0tPTw/8zLS1tWE914OJgYFBcnLy5MmTGxoaVFRUHj58aGFhIQ8tFeMzo6mpieeHLD8/H84Jsba2FvKQkSNHwo3S0tIZM2b0LQAHIQIAsLsud+/eBQDMmzevdzEajaavrw9n9UubGDGLJC8vj0ajoUXPBhkTExNse/bs2QRGgghj8uTJeCbHt2/fwg1sisiAbGxs4EZxcTHfAliiwa6XHzx4AACYMmUKT0kLC4v09PS6ujpROxdEJUbMIrG0tLxw4QI83ZCxDx8+vHv3bsKECbJ/a+FVVFTo6Ojo6ekRHYiwnjx5cvHixWPHjr1584ZCoYSFhe3fvx8bzTYoFRYWmpubY8M2iNXW1tbQ0DBx4kSRjrKwsMAzOWJPCBD+ktDa2ppCoXR2dgq6RZufnw8A0NPTMzMzg3vgRauRkRFPSRMTk/T0dAaDIe3kKEbMIlFRUVmzZo1EISLyRFlZ+eLFixs2bIB9jmFhYUuXLnVwcCA6LmQAeM6ttrS0hBuFhYVCHqKmprZ48WIAwKNHjxobG3leraqqev78OQBg6dKlJBIJ7mxtbSWTyX2HicH5Kh8/fhQ3fGGJETOCIAoHz+Q4fvz4MWPGAABu3Ljx6tWrvgXy8vIiIyN5dvr4+AAAOjs7edZGBACEhIRwuVwAgK+vL7bzw4cPfPtW4U6xx12LRNSYEQRROHgmRzKZfPjwYQAAi8WaPXt2QkLCu3fvOjo6SkpKYmJiPDw8aDRaUVERz1GLFi2C61CEh4efPXsW2x8ZGfnf//4XAODu7j5z5kxsP4VC4TsGsKenBwie7yyIt7f32LFjx44dW1FRIfxRosaMIIjikWCqCB8cDmf+/PmC3ktHR+fatWt9j6qqqsJWjbWzs1u+fDnWW0elUmtra3sXhmvedHd381QCz+bS09NFCtjZ2Rm+kaDpLoKIFDMylA2dxyQMMjiv50gikW7cuBEcHMxzM3HSpElhYWGVlZXLli3re5SRkVF2drarqysAIDs7++rVq3CS5rx58549e8ZzgwWOj8HujGOqqqqABEMLRSVSzAiCKBz8B9Oqqqp+//33Bw4cgMMedXR0qFTqgMs6GBoaPnz4MCcnJzk5ubGx0djY2NXVFRs005u5ufmLFy8YDAbPiJOCggIKhdJ7NJkw4Mpp4hE+ZgRBFI60ZhqoqKjY2dmJehSNRqPRaP2XcXd3j4mJefr0ae9x4AwGo6GhYcGCBdhaijIjTMwIgigc4h+wJSo3Nzc1NbXo6Gj47Efo559/BgAsWbKEuLgQBBlUFC85amtr79mz582bN97e3mVlZR0dHeHh4ZGRkRYWFjxrRyIIgohNkSbwY4KDg5uamk6dOpWUlAT3TJky5caNGwq0ACqCIHJOIZOjkpLSyZMnt2zZkpyczGQyP/nkEzc3N2zyIoIgiOQUMjlClpaW2IRFBEEQfKGzLQRBED5QckQQBOEDJUcEQRA+UHJEEAThAyVHBEEQPlByRBAE4QMlRwRBED5QckQQBOEDJUcEQRA+UHJEEAThAyVHBEEQPlByRBAE4QMlRwRBED5QckQQBOEDJUcEQRA+5Gs9RzabXV5eXllZaWBgYGlpqRDr1ypizAiCDEhevskcDiciImLcuHFmZmazZs2ytrY2MTE5ceIE0XH1RxFjRhBESHKRHLu6ury8vHbu3FlTUwMA0NDQAABUVlZu377d29ubw+EQHSAfihgzgiDCk4vkGBwcnJiYCADw9/evq6tjMpn5+fleXl4AgISEhCNHjhAdIB+KGDOCICLgEq24uFhZWRkAsHbt2t772Wy2h4cHAEBZWbmsrIyo8PhSxJgRopw7dw4A0NzczOVyHz9+DAB49uwZ0UEhAyP+zPHMmTMsFotCoRw9erT3fjKZHB4erqSkxGKxLl26RFR4fClizAiCiIT45BgTEwMAcHJyGj16NM9LVCrVyckJAHD58mUCIhNMEWNGEEQkBCfHsrIyeEPD1taWbwG4n8FgvHv3TqaRCaaIMSMIIiqCk2Nubi7cEJRoLCws4MbLly9lE9KAFDFmBEFEJd1B4G/evDl//vzdu3crKio6OjrGjh07YcKEgIAANzc3WKCpqQlumJqa8q0B219eXi6NCKOiokpLSwEA+/btGzFihDCHEB4zgiAyIK3k2NXVFRoaeuTIERaLhe1sbW0tKCi4efOms7NzamoqAKClpQW+pK6uzrceTU1NuNHW1iaNOOPi4tLS0gAAAQEBQiZHwmNGEEQGpJIc29raFixYkJ6eDgCwt7f38vKiUqlNTU0VFRWxsbEVFRVUKhWWxBKNqqoq36pUVFSwOqURqhgUMWYEQUSFf3JksVgwM1IolKioqHXr1vV+9eDBg5GRkZ9//jn8Z3NzM9wYMNEwmUzcQwUABAUFBQQEAAAMDAyEPITwmBEEkQH8k2NQUBA8Z7x8+bKnpyfPq6qqqjt37sT+2d3dDTcEzbfD9ve+PO+NzWZ3dHRoaWmJF62Li4uoh0gec/+ampqCgoJ6enrEOFZCjY2N9fX11tbWsn9r4RUVFenq6hoaGhIdiLBev34NANi0aVNRUZGxsTEA4Icffhg1ahTRcUkLl8t98eKFlZUVnFNLuPfv31dXV9vY2Ih0lIWFBc7Jsby8PCwsDACwePHivpmxLyypYRmHB7ZfR0en76sVFRU+Pj6enp47duwQM2LRSRjzgD58+JCVlUVIcmxpaWlpaenq6pL9Wwvv7du3mpqa1dXVRAciLDii6+XLl1VVVR8/fgQAFBUVvX37lui4pIXD4ZSWlnZ1dQnqlJex1tbWpqYmUb9Q7e3tOCfHo0ePwtOlAwcOCFNeW1sbboiUaOLi4tLT0wsLCx89esThcITJwjgSL2bhUanUjIwM8WJD5BCdTt+wYcOff/6pq6ubnp4+c+bMCxcuODg4EB0XMgA8xzlyOJxr164BAGg02tSpU4U5BEs0gu5d1NbWwo3eieb06dO//fabqqrq9OnTJYpYLOLFjAxZFhYW9vb2cAzDuHHjbGxs4MU1IufwPHPMz8+HVxDCd1qNHDkSbpSWls6YMaNvATgIEQCgq6uL7UxNTSWRSACA/fv3w/5NWRIvZuF1dHT88ssvhFzbNjU11dfXW1payv6thcdgMHR1dfX19YkORASenp5HjhzJz8+3s7NbvXr14J53z+Vyc3JyrKys1NTUiI4FAACam5tra2snT54s0lHm5uZ4JkesGwWbIjIgrJe0uLiYbwEs0VhZWWE7YWYkingxC6+iouLAgQPt7e3ihYfILRKJlJCQQHQUskAikeLj44mO4m8kEonL5Yp0iIODA57JEXtCgKDOuL6sra0pFEpnZ2dWVhbfAvn5+QAAPT09MzMzXIKUnLRjtrS0hN32CIIQCM8+R+xyrLCwUMhD1NTUFi9eDAB49OhRY2Mjz6tVVVXPnz8HACxdupTYs8XeFDFmBEFEhWdyHD9+/JgxYwAAN27cePXqVd8CeXl5kZGRPDt9fHwAAJ2dnTxrIwIAQkJC4Mmwr68vjnFKThFjRhBENPiunXv+/HlY7ejRo+Pj4xsbG9vb2xkMxpUrV5YuXUomk7du3dr3KLgOBYlEOnPmDLbz5MmT8Drd3d1d0Nvt27cPABARESF2wF5eXsbGxsbGxuXl5SIdKHbMCIIoBJyTI4fDmT9/vqBErKOjc+3atb5HVVVVYavG2tnZLV++HOuto1KptbW1gt5O8uTo7OwM36ikpESkA8WOGUEQhYDzeo4kEunGjRvBwcF6enq990+aNCksLKyysnLZsmV9jzIyMsrOznZ1dQUAZGdnX716lcFgAADmzZv37Nkz+ZwopogxIwgiPJHvcAupp6cHDnvU0dGhUqlCLuuQk5OTnJzc2NhobGzs6uo64HTI/fv3HzlyJCIiQpbTB3mIGjOCIApBWus5qqio2NnZiXoUjUaj0WjSiEd6FDFmBEEGRPwDthAEQeQQSo4IgiB8SPcZMlJy+/ZtOAsFrl7z4MGDzs5OAICjoyN8LCqCIIiEpHVDRqp8fX2xAZW97du374cffpB5OAiCDEL/B+oBTWp9pO0SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=436x182 at 0x1086F3BE0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit_drawer(qc)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}