HelloWorld.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "qr = QuantumRegister(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr = ClassicalRegister(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(qr, cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         \n",
       "q1_0: |0>\n",
       "         \n",
       "q1_1: |0>\n",
       "         \n",
       " c0_0: 0 \n",
       "         \n",
       " c0_1: 0 \n",
       "         </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x7f3f5039b080>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f3f5039b0f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(qr[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 227.556x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f3f501b3cc0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.cx(qr[0], qr[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 287.756x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f3f3a1286d8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.measure(qr, cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAACoCAYAAACVBefMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAWjElEQVR4nO3de1TUdf7H8eeA3OQmoiAXBVFQREAFL2mJhj/FcrPlmLe0ixloteVqZys9Xc6WiKZr9dvyR7WmG9bmJdd+WRoquBWakEGaGqI/QLxginLxymV+fxDEyG2wGb4wn/fjnDnOfOfz/fAenHnx+X4/H77o9Hq9HiGEUJiV1gUIIYTWJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKE+CUAihPAlCIYTyJAiFEMqTIBRCKK+T1gUI01iwQZuv+8aD2nxdLel0Ok2+rl6v1+TrqkBGhEII5UkQCiGUJ0EohFCeBKEQQnkShEII5UkQCiGUJ8tnhDAzGxsbBgwYQPfu3dHr9RQWFpKTk9PkchgvLy8mTpzI2rVr27hSdUkQ/qqwsJAVK1aQmZlJVlYW165d49ChQwwcOFDr0kQHZGtry5QpU4iLi2PEiBHY2dkZPF9WVsaePXtYs2YNX331VV0oenl5kZqaSr9+/aisrOSf//ynFuUrR4LwV7m5uXzyySdEREQQFRXFjh07tC5JNOJiOfyQD1dvgLMDDPEHVwetqzIUHR3N+++/j7+/f922nJwcCgoKsLKyIjAwkJ49ezJ58mQmT55MRkYGjzzyCJcuXaoLwaysLD7//HPtXoRiJAh/NXr0aIqKigBYt26dxQfh+095M2LKqwwc81jdNr1ez/887sp/xa+n79A/alhdQxVVsOkAHDhpuP1/f4A7g2DyELBuB2e8ly5dyuLFiwE4fPgwb731Fhs3bqSkpMSgnbe3Nw899BBPPfUUQ4cO5eDBgxQXF+Pl5UVWVhbR0dEUFxdr8RKU1A7eOuZXXV3NypUrCQwMxN7envDwcPbu3Uu/fv2Ii4sDwMpKiW8FAOXFp7ly+Szde4UbbC85f5Kb18vwDIjUqLLG6fWQ/G3DEASo1sN/foatmW1f162WL1/O4sWLqaioYMmSJQwePJj33nuvQQgCnDlzhsTERIKDg0lOTsbOzg4vLy/y8/MlBDWgxIhwzpw5bN26lRdffJGIiAjS09OZMWMGv/zyCwsXLtS6vDZXdDIDnZU17r4hBtsvFGTT2dUTZ/eeGlXWuIKLkH2q+TbfHIcxwdDNuW1qutV9993HX/7yFyoqKoiNjTX6sNbJyYmhQ4fWPfb09MTDw0OCsI1Z/DDoo48+Yv369Xz22Wc8++yzjB07liVLlnDHHXdQWVlJREREq/orKipi/PjxdO7cmfDwcH744QczVW4+RSczcOsRRCdbw5NrvxRk49G7fY0GAb47AcZc5qCxEWNbcHV1JSkpCYBFixYZHYL1J0aysrJITk7G3t6eDz74wJzlikZY/Ihw2bJlxMTEEBUVZbC9b9++2NjYEBoa2qr+5s+fT//+/dm2bRsffvghU6ZMIScnB2tra1OWbcCYq508k2z8lUmKTmZwuSiXpHndDLZX3Cgn8g8vmLy23+u+Zz/HLywGK6umv8fVVZW8sWYD9yY9YvZ6bvXoo4/So0cP0tPT+fvf/27UPreGYHR0NJWVlURHRzNixAjGjh1LamqqwT5aXfWmIzP2ij0WPSIsLCzk8OHDPPDAAw2eKygoICQkpMGyhuaUlZWxfft2XnrpJRwcHIiLi6Oqqor9+/ebsmyzK/q/TIbHvsLMpVkGt042Dni2wxHhzaslNScKm6PT1bTTwPz584Gac4TGfPAaC8Hi4mJKS0tZs2aNQZ+ibVh8EAL06NHDYPu1a9fYu3dvqw+Ljx8/jru7O926/TaSCg0N5ciRI7+/2Gbo9foWb8a6fC6XG1cu4Rc2AWd337pbVcV1bly9jEcrJ0qMqe333pY/PxMr6+YPXqysrFm78uk2qac+X19fgoKCuHTpEtu3b2/x+9VUCNaqXTcYHR2tyffa0m7GsuggrA2snJwcg+0rVqzg7NmzDBkypFX9XblyBRcXF4NtLi4ulJeX/75C21DRyQw62XVuMGN89ng6Tu49cXT11KiypoX4gIdL0+cJdYB/N+jdvS2rqlH7HsrMzKSqqqrZti2FIEB+fj7nz5+na9euBusQhXlZ9DnCgIAAwsLCSEhIoGvXrvj4+LB582a++OILgAYjws2bNwM1b2qAlJQUjh07hqOjIxMnTsTR0ZGysjKDfUpLS3FycmqDV2MaRScz8Ow9tMEI62zuvnZ5WAw16wPn3w1v74YL9b79OkAPeLvB3CjQ4hSaj48PACdOnGi2nTEhWCs3NxcPDw+8vb3Jy8szdcmiETp9a8aPHVBOTg7x8fEcOHAAd3d3Hn74YZydnVmyZAmlpaU4OPw2c9rUyWg/Pz/y8vIoKyujW7dunDlzBnd3dwB69+5NcnIyo0aNapPX0xQVLtVfUQVZ+bBhX83jEB8YGgChvm27mLr++8Ta2hpHR0eqq6ubPTLo168faWlpnDt3rsV1gq6urlRVVXHlyhWDwzsL/6hqyuKDsDGzZ88mOzubH3/8sdX7xsbG0qtXLxITE0lOTiYhIYHjx4+bddbYGCoEYa3a16rV30u53dnboKAgLly4cNtrBBX8qLYZiz40bkpmZiYjRoy4rX3XrFnDrFmzcHNzIzAwkC1btmgegqJjuPVctWg/lAvC8vJycnJyeOKJJ25rf09PT1JSUkxclRBCS8oFoZOTU4uze0IItVj08hkhhDCGBKEQQnkShEII5UkQCiGUJ0EohFCecrPGlkqrxcUqau3C5ueXvwtA4nNxBvdF+yEjQiGE8iQIhRDKkyAUQihPglAIoTwJQiGE8iQIhRDKkyAUQihPglAIoTwJQiGE8iQIhRDKkyAUQihPglAIoTwJQiGE8iQIhRDKkyAUQihPrkcohCJu9w/T/x4d5Y/Sy4hQCKE8CUIhhPIkCEWHcvXGb/fzLsD1Cu1qEZZDzhGKdq+4HNJz4Yd8uFj+2/Y3dtb86+ECQ/zhjr7g6qBJiaKDkyAU7dbNSvjyR0g7Cs2dcj9fCjt+hJTDMH4gjAsBaznWEa0gQSjapUtXICkVzpUYv09VdU1w/nQa4saAk73ZyhMWRn5uinbn8lX475TWhWB9BRfh7V1w5UbLbYXpuLq6al3CbZMRoWhXqvWQnA7FV5puU/s3nBdsaLrN2RLYdAAeucu09akgICCA+++/n8jISIKCgrC1taWsrIxDhw7x3Xff8emnn1JSYvhTKjAwkNTUVFavXs2qVas0qvz2yYiwnsLCQp5++mlGjhxJ586d0el0HD58WOuylLIvF3KLTNNXVgFkF5imLxUMHjyYL7/8khMnTrBq1SpmzJhBREQEoaGhjBw5kvj4eNauXcvp06d55513cHd3B2pCMC0tDR8fH+69916sra01fiWtJ0FYT25uLp988gldunQhKipK63KUU10Nu34ybZ8ph6GD/HKDZqysrHjllVc4cOAAMTExXLt2jQ8//JC5c+cyfPhwwsLCGDt2LAsXLmT37t04Ojoyf/58fvrpJ+Lj40lLS8Pb25vU1FQmTZpEVVWV1i+p1eTQuJ7Ro0dTVFQzHFm3bh07duzQuCK1HD1bM0liSoWXas4Z+nUzbb+WwsrKivXr1zNr1iwA3nzzTf76179SXFzcoG1aWhqrV68mODiYd955hzFjxrBmzRp0Ol1dCF69erWtX4JJKDMirK6uZuXKlQQGBmJvb094eDh79+6lX79+xMXFATVvCqGdnHMdq19LkJiYyKxZsygrK2PcuHEsWLCg0RCs7+jRo8THx1NaWopOp0Ov17N69eoOG4KgUBDOmTOHV199lfj4eL788kumTp3KjBkzOHnyJBEREVqXJ4BTF83Ub/Ofa2WNGjWKRYsWUVlZyaRJk9i9e7dR+9VOjLi4uJCXl4dOp+Ptt9/GxcXFzBWbkV4BGzZs0AP6tLQ0g+2xsbF6QJ+RkdFgnw8++EAP6A8dOtTguZdeekkfHBys1+l0+k2bNpmt7lrUrCe2+NsjfzuhfyZZb3C7Hbf2MfWVfZq+rucSk/TPJSY1uK/1LTMzU6/X6/Wvvvqq0fsEBgbqT58+rdfr9fo9e/bonZ2d9fv379fr9Xr90qVLNX9Nt96MpcSIcNmyZcTExDSYAOnbty82NjaEhoa2qr/AwEDefPNNhg0bZsoyBea5TJTOTP12ZMOHDyciIoILFy7w2muvGbVP7exw/YmRsrIy/vznPwMwd+5cbG1tzVm22Vj8ZElhYSGHDx+u+8+qr6CggJCQEOzs7FrVZ+2J5aVLl5qkxpboFZn2/NuOmomN+hpbK2jMOsL67h49nH9p+D18fvm7QM3/Y/37ba3+9QhnzJgBwNq1a7lxo+WV542FYO05wX379pGdnU14eDh33323wSRjR3nvWvyIsLCwEIAePXoYbL927Rp79+6V84PtiG9X8/Tb0908/XZkkZGRAKSkpLTYtrkQrLVr1y6Dfjsaiw/Cbt1q1k3k5OQYbF+xYgVnz55lyJAhWpQlGtHHwzz9BnQ3T78dWUhICADZ2dnNtjMmBAGysrIM+u1oLP7QOCAggLCwMBISEujatSs+Pj5s3ryZL774AqDBiHDz5s0AZGZmAjU/MY8dO4ajoyMTJ05s2+IVE9YTHO1M+zvC3ZwgsEfL7VTz+uuv4+jo2OJSmY8//rjFEAQ4ePAgr732GocOHTJHuWZn8UFoZWXFpk2biI+PZ/78+bi7u/Pwww/z5JNPsmTJEsLCwgzaP/DAAwaPFy5cCICfnx95eXltVbaSbKzhriDYYcLP0phgsJK5kgYSEhKMajdr1ixefvllHnvssWbXCR45coQXX3zRVOW1OYsPQoCgoCBSU1MNts2ePZvg4GAcHAyv5GnMyd2Kigqqqqqorq6moqKC69evY2dnp8kfx7E040Jqfkf4dq88U19AdxgZ+Pv7UdmxY8fqJlYsmcWfI2xKZmbmbU+UPP744zg4OPD1118zc+ZMHBwcyM/PN3GFaupkDQ+NAnubptss2NDyjLGzPTw4UkaDwjhKBmF5eTk5OTm3PVGybt069Hq9wc3f39+0RSrM2w2eiIbOt7kkrUtneHIcuDuZti5huZQ4NL6Vk5NTh7xChkp6ucNzk2DjdzVXnDZWpD/8MbJm0kUIYykZhKJjcHWAuVFwvAi+yYHDhTUXbr2VtRUM6gV3BkFvWSojboMEoWjXdDoI6lFzu1kJZy5DUQlUVNXMMnt1Ae8uNecWhbhdEoSiw7DtBP7dam5CmJKSkyVCCFGfBKEQQnlyaCyEIlp7JZjaK+UkPhdncN8SyYhQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQEadOnSI6Oprg4GAGDhzICy+8oHVJwkhpaWmEhITQt29f5s6d2yH+FO2f/vQnfH196dSpY1z7WYJQEZ06dWL58uUcPXqUgwcPkp6ezrZt27QuS7SgurqauXPnsmnTJnJzcyktLSU5OVnrslo0bdo0vv/+e63LMJoEoSK8vLyIjIwEwNbWlrCwMAoKCjSuSrQkIyMDb29vBgwYAMBjjz3Gli1bNK6qZXfeeSeenp5al2G0jjFuFSZ18eJF/v3vf5OSkqJ1KRZr4/Y0zp6/aLDtzQ+2NHo/tF9v7h45pNF+CgsL6dmzZ93jXr16cerUKRNXWyM3/zTb9+xvsL2xujtZW/Po1Il0trczSy1tTYJQMTdu3GDKlCksWLCA/v37a12OxYoYGMR7//rcYFv9YKy9b9PJmiEDg5rsR6/Xo9PpDB6bS0Avb6ytrCg894vB9sbqHnvHIIsJQZBDY6VUVVXx4IMPMnjwYBYtWqR1ORatj583IUH+LbaLGj6ILi5OTT7fs2dPg1MYp06dwtfX1xQlNmCl0zEp+o4W2zk7OjBm+CCz1KAVCcJfZWRkcM8999ClSxccHR0ZMWIEGzdu1Losk4qLi8PZ2ZlVq1ZpXYoS7hkzAmvrpj9irs6OjB4e3mwfkZGRnD59miNHjgDwj3/8g9jYWJPWWZ+/bw/C+gc022ZC1DDs7GzNVoMWJAiB1NRURo0axTfffMPUqVOZN28e586dY9q0aRYTGt9++y1r164lMzOTwYMHM2jQIN566y3AvIdbKnN3c+HOyNAmn4+JGoatTfNnp6ytrXnvvfeYMmUKffr0wcnJidmzZ5u6VAMTxwynk7V1o8/5eHZr9lC+Vnx8PL6+vlRVVeHr68uTTz5p6jJNSqdX/FNQWVlJ//79KSwsZP/+/QwaVDPkLykpYdiwYeTl5ZGTk4Ofn5/GlZrPx5/txr2LC+NHD9W6FItz/cZNVr77CeVXrxls7+nlwfzZk7Gqd/6vPdn5nwxS9/3QYHv8zD/Qu6eXBhWZlxIjwm3btjFp0iQ8PDyws7PDz8+PmTNncujQIfbs2cOJEyeYOXNmXQgCuLq6snjxYm7evMn69es1rN68zhRdIPvoCayslHgrtDl7O1vG3xXZYPsfou9otyEIMGbEIJydOhtsC+0XYJEhCBYehJWVlUyfPp3777+f7OxsYmNjeeaZZxg8eDBbtmzh9OnTpKWlATB+/PgG+0+YMAGAvXv3tmXZbWp3+kHs7WwZFTlQ61IsVmRYP3p071r3eNCAvvTyad9r7OxsbYgZPazucSdrayaOGdbMHh2bRR8az5s3j6SkJB5//HFWr16No6Nj3XOnTp2iS5cuzJkzh82bN5OZmUlERESDPpydnXFzczPr4uPnl79rtr6FUFnic3FGtbPYdYRff/01SUlJxMTEkJSUZLAWC6hbpFpSUgLUHAo3xsXFpa6NEMIyWWwQvvHGGwAkJiY2CMH2xtifWqZ0pugCb637lHGjIhh3Z8ORsDC94pIynBzssbW10bqUVim6cAnPbm5al2FWFhuEKSkp+Pv7Ex7e/Dqt2pFgU6O+0tJS3NzM+ybQ8tB417ffs+vbjvPL8UK0hrGDDIucLLl8+TJlZWX4+/u32DYwMBCA48ePN3ju3LlzlJeX17URQlgmixwR1s7/nD9/vsW2UVFRLFu2jK+++orp06cbPLdz5866NubU1ofGH279ihP5Z3hu3gwcLOj3RYW4XRY5InRzc6NPnz4cPXqUXbt2NXj+559/rrsfHR1NQEAAH330EVlZWXXbS0pKSEhIwNbWloceeqhN6m4LZ4ou8FNOHndGhkoICvErixwRAiQkJDBt2jRiYmKYPHkyffv25fz586SnpzNgwAC2bt0K1Fyw9P3332fChAmMHj2a6dOn4+zszJYtW8jPz2flypVGHWJ3FMWXy+jaxVnWDQpRj0WvI9y5cyevv/46GRkZXL9+HQ8PD4YNG8aCBQu46667DNoeOHCAl19+mfT0dCoqKggNDWXhwoVMmzZNo+rNp7q6Wn6TRIh6LDoIhRDCGDIsEEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPIkCIUQypMgFEIoT4JQCKE8CUIhhPL+H3KzM2OR3pKyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 408.156x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = execute(circuit, backend = simulator).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider('ibm-q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "qcomp = provider.get_backend('ibmq_16_melbourne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = execute(circuit, backend=qcomp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.monitor import job_monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogramramamam(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}