samples/getting-started/azure-quantum/provider-specific/Getting-started-with-IonQ-and-JSON-on-Azure-Quantum.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Getting started with IonQ and JSON on Azure Quantum\n",
    "\n",
    "This notebook shows how to send a basic quantum circuit expressed using the [IonQ JSON spec](https://docs.ionq.com/#tag/quantum_programs) to a Quantinuum target via the Azure Quantum service."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "First, install `azure-quantum` and optionally `matplotlib` for plotting:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# To install the Azure Quantum client package, uncomment and run the line below:\n",
    "#\n",
    "# !pip install azure-quantum==0.19.2109.165653 --quiet\n",
    "#\n",
    "# We also recommend installing matplotlib, if you don't have it installed already:\n",
    "# !pip install matplotlib --quiet"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Connecting to the Azure Quantum service\n",
    "\n",
    "To connect to the Azure Quantum service, find the resource ID and location of your Workspace from the Azure portal here: https://portal.azure.com. Navigate to your Azure Quantum workspace and copy the values from the header.\n",
    "\n",
    "<img src=\"../azure-quantum-resource-id.png\" alt=\"How to retrieve the resource ID and location from an Azure Quantum workspace\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "from azure.quantum import Workspace\n",
    "from azure.quantum.target import IonQ\n",
    "\n",
    "# Enter your workspace details here\n",
    "# Find your resource ID and location via portal.azure.com\n",
    "workspace = Workspace(\n",
    "    resource_id=\"\",\n",
    "    location=\"\"\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Use `workspace.get_targets` to see what targets are currently available for the Quantinuum provider, including wait times. Running this method will trigger authentication to your Microsoft account, if you're not already logged in."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "workspace.get_targets(provider_id=\"ionq\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<Target name=\"ionq.qpu\", avg. queue time=54 s, Available>,\n",
       " <Target name=\"ionq.simulator\", avg. queue time=0 s, Available>]"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# See https://docs.ionq.com/#tag/quantum_programs for JSON format spec\n",
    "circuit = {\n",
    "    \"qubits\": 3,\n",
    "    \"circuit\": [\n",
    "        {\n",
    "        \"gate\": \"h\",\n",
    "        \"target\": 0\n",
    "        },\n",
    "        {\n",
    "        \"gate\": \"cnot\",\n",
    "        \"control\": 0,\n",
    "        \"target\": 1\n",
    "        },\n",
    "        {\n",
    "        \"gate\": \"cnot\",\n",
    "        \"control\": 0,\n",
    "        \"target\": 2\n",
    "        },\n",
    "    ]\n",
    "}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Run on IonQ Simulator"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Submit the circuit to the IonQ Simulator target. The following example uses the IonQ simulator, which returns a Job object. For more information, see [Azure Quantum Job](https://review.docs.microsoft.com/azure/quantum/optimization-job-reference)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "target = IonQ(workspace=workspace, name=\"ionq.simulator\")\n",
    "job = target.submit(circuit)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "job.id"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'628e950a-1d85-11ec-910f-70bc105c1854'"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Wait until the job is complete and then fetch the results."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "results = job.get_results()\n",
    "results"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "...."
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'duration': 6075357, 'histogram': {'0': 0.5, '7': 0.5}}"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "You can then visualize the results using [Matplotlib](https://matplotlib.org/stable/users/installing.html)."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "%matplotlib inline\n",
    "import pylab as pl\n",
    "pl.rcParams[\"font.size\"] = 16\n",
    "# The histogram returned by the results can be sparse, so here we add any of the missing bitstring labels.\n",
    "hist = {format(n, \"03b\"): 0 for n in range(8)}\n",
    "hist.update({format(int(k), \"03b\"): v for k, v in results[\"histogram\"].items()})\n",
    "pl.bar(hist.keys(), hist.values())\n",
    "pl.ylabel(\"Probabilities\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probabilities')"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Run on IonQ QPU\n",
    "\n",
    "#### Note: The time required to run a circuit on the QPU may vary depending on current queue times."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Submit the circuit to the IonQ QPU target."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "qpu_target = IonQ(workspace=workspace, name=\"ionq.qpu\")\n",
    "qpu_job = qpu_target.submit(circuit)\n",
    "qpu_job.id"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'7a14dd3e-fa72-11eb-83af-00155df1914a'"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Wait until the job is complete and then fetch the results."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "results = qpu_job.get_results()\n",
    "results"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'histogram': {'0': 0.59, '2': 0.01, '3': 0.03, '7': 0.37}}"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plot the results."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "pl.rcParams[\"font.size\"] = 16\n",
    "hist = {format(n, \"03b\"): 0 for n in range(8)}\n",
    "hist.update({format(int(k), \"03b\"): v for k, v in results[\"histogram\"].items()})\n",
    "pl.bar(hist.keys(), hist.values())\n",
    "pl.ylabel(\"Probabilities\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probabilities')"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAD9CAYAAACSoiH8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWnklEQVR4nO3de7SldX3f8feHwQsIUa5NNcJhhKUMFdNkmmCMiqykoBjQRVJdicqlAhqlUldMoHhFQiEaJUqTijcM0IYloYKiBuW2UiNtRqzUwYjIDHjBOsDITUQw3/7x7ONs9pzLfp7znHP2Yd6vtfba+/yey/7uhwc+PJff70lVIUlSW9stdwGSpJXJAJEkdWKASJI6MUAkSZ0YIJKkTrZf7gKW0u67715TU1PLXYYkrRhf+cpX7qyqPWaatk0FyNTUFOvWrVvuMiRpxUhy22zTlvwUVpKnJ7kkyT1J7k1yaZK9Wiy/f5JPJrkzyYNJvpnkTYtZsyRpa0t6BJJkR+Bq4CHgaKCAM4BrkhxYVQ/Ms/zawfLXAq8F7gH2A3ZaxLIlSTNY6lNYxwOrgWdW1S0ASW4EvgWcCLxvtgWTbAf8NXBVVb18aNI1i1euJGk2S30K6wjg+unwAKiqDcCXgCPnWfZgYH/mCBlJ0tJZ6gA5APj6DO3rgTXzLPubg/cnJrk+ycNJfpjkA0l26LVKSdK8ljpAdgU2z9B+N7DLPMs+dfB+MXAl8NvAn9FcC/lvsy2U5IQk65Ks27RpU/uKJUkzWkm38U6H3YVV9fbB52uTrALOSrJ/VX1jdKGqOg84D2Dt2rUOPSxJPVnqI5DNzHykMduRybC7Bu9fGGm/cvD+rxdQlySppaUOkPU010FGrQFuGmPZufxzp4okSZ0s9Smsy4H3JlldVbcCJJkCngecMs+yn6PpP3Io8Omh9sMG74vaxXzqlCsWc/Vz2njW4cv23ZI0m6U+AvkwsBG4LMmRSY4ALgO+A3xoeqYkeyd5JMn0tQ6q6i7gPwOvS3Jmkt9KcgrwduATw7cGS5IW35IegVTVA0kOAd4PXAAEuAo4uaruH5o1wCq2DrjTgfuAPwT+CLgDeA/w7kUuXZI0Ysnvwqqq24Gj5plnI02IjLYXTUdCOxNK0jLzeSCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE62bzNzku2A7arqkaG2Q4F/BVxdVV/tuT5J0oRqFSDAfwceAl4DkOR1wF8Opj2c5PCq+mKP9UmSJlTbU1gHAZ8d+vstwEeAJwOXAqf1VJckacK1DZA9ge8BJNkX2Ac4t6ruAz4OPLvf8iRJk6ptgNwL7Db4fDBwZ1XdOPj7Z8ATe6pLkjTh2l4D+QfglCSPACfz6NNZ+wLf7akuSdKEa3sE8sc0RyCX0xxtvHNo2iuAL/dTliRp0rU6AqmqbwH7Jdmtqu4amfwm4Ae9VSZJmmidOhJW1V1Jdkqyd5LHDdr+b1Vtmm/ZJE9PckmSe5Lcm+TSJHu1rSHJKUkqyf/s8hskSQvTOkCSvDTJDcA9wLcZ3HmV5CNJfn+eZXcErgaeBRwNvBrYD7gmyZNa1LAaeCvww7b1S5L60SpAkrwMuAy4E/iTkeU30ITCXI4HVgMvq6pPVdVlwBHA3sCJLUr5K+Ai4BstlpEk9ajtEcg7gI9X1b8FzhmZ9nWaIU3mcgRwfVXdMt1QVRuALwFHjlPA4CjnV4BTx6xZkrQI2gbI/sDFg881Mm0zW/qIzOYAmqAZtR5YM9+XJ9kFeD/wx1V193zzS5IWT5eOhLvPMm0KmO8i+q40QTPqbmCXMb7/PcDNwPljzAtAkhOSrEuybtOmea/xS5LG1DZAvgCcmuQpQ22V5AnAG4HP9VXYqCTPpxnE8fVVNXr0M6uqOq+q1lbV2j322GOxypOkbU7bnuinAf8b+CZNL/QCTgEOpBlQ8WXzLL+ZmY80ZjsyGfYh4KPAd4cCbHtg1eDvB6vqoXl/gSSpF62OQKpqI80F7M8Av00z/tULgOuBX6+q78+zivU010FGrQFummfZ/YHX0QTN9Ot5NCMEbwZeP9aPkCT1ou0RCFX1XeDfd/y+y4H3JlldVbcCJJmiCYJT5ln2RTO0nQOsAk4CbplhuiRpkbQOkAX6MM21ksuSvJXmFNi7ge/QnKICIMneNJ0UT6+q0wGq6trRlSX5EbD9TNMkSYtr3gBJ8jHg3VW1YfB5LlVVsx6dVNUDSQ6huRX3AiDAVcDJVXX/8NfSHFn4zHZJmlDjHIG8CPiLwedD2Lr/x7B5746qqtuBo+aZZyNNiMy3roPnm0eStDjmDZCq2mfo89SiViNJWjHajoX1giQ7zTLtSUle0E9ZkqRJ1/YawzXMPuTIswbTJUnbgLZ3Yc11XeIJNP1CJEkjpk65Ytm+e+NZhy/Kese5C2uKZgj2aWtnOI21A3AccHt/pUmSJtk4RyBH0wzjXoPXB3n0kUgN/n4EeEPfBUqSJtM4AXI+cC1NSFxNExKjw448BNzsEOuStO0Y5zbe24DbAJK8CLihqu5b7MIkSZOt1UX0qrpusQqRJK0s41xEvxV4eVV9LckG5umJXlXP6K06SdLEGucI5DqaJxFOfx77YU6SpMeuca6BHDv0+ZhFrUaStGI42q0kqZNxroG8ps0Kq+qvu5cjSVopxu0HMq4CDBBJ2gaMEyD7zD+LJGlbM25HQkmSHsWL6JKkTuxIKEnqxI6EkqRO7EgoSerEayCSpE5aB0iS/ZJ8IsnNSR4YvJ+fZN/FKFCSNJlaDeee5GDgs8CDwBXA/wP+BfA7wCuSHOaQ75K0bWgVIMCfA18FDq2q+6cbk+wMXDmYvra/8iRJk6rtKaw1wNnD4QEweELh2cABfRUmSZpsbQPku8DjZ5n2eOB7CytHkrRStA2Qs4F3JXnqcGOSpwHvAM7sqzBJ0mQbpyf66Oi6vwDcmuR6tlxEP2jw+YXAx/ouUpI0eca5iP4CHt37/BHgDmDvwYvB3wDP7680SdIkG6cn+tQS1CFJWmHsiS5J6qRtP5CfS7In8MTR9qq6fUEVSZJWhLY90bcDzgBOBJ4yy2yrFliTJGkFaHsK62TgDTQ9zkNz2+4ZwAbg28DxfRYnSZpcbQPkWOB0mv4gAP+jqt4B7E/TiXCvHmuTJE2wtgGyGlhXVT+juZ13B4Cqehg4Bziu1+okSROrbYDcw5YL598Hnjk0bXtg1z6KkiRNvrZ3YX2VZkDFvxu83pXkQZqjkT8Fbui3PEnSpGobIOfQnMaCZuyrXwEuGvx9G/DGfsqSJE26VgFSVV8Y+vyDJL8GPAPYEfjG4FqIJGkb0LkjIUBVFXBLT7VIklaQ1gGS5CnAfwSeCzyN5vbdfwDOqaof9VmcJGlytboLK8lzgG8Bp9LcjXXT4P0/ATcneXbvFUqSJlLbI5APAHcBa6vqtunGJFPA54EPAgf3VZwkaXK17Qfyb4C3DYcHQFVtpLkr69d6qkuSNOHaBshdwEOzTPvJYLokaRvQNkD+CnhLkkcN455kB+CPgP/SV2GSpMk2zjPRTx/+k+Yxtrcn+Sxbnon+EuBBmv4gkqRtwDgX0d86S/trZmg7DXh793IkSSvFOM9E97G3kqStGA6SpE46DWWS5KXAC2mGb78buLaqruizMEnSZGv7TPSdgc8Az6cZwv0uYDfgzUn+HnhpVd3fe5WSpInT9hTWmTRDuL8a2KGq/iXNUwlfM2g/s9/yJEmTqm2AHAW8taouGjzWlqr6WVVdBLxtMF2StA1oGyC70QygOJObBtMlSduAtgGyAXjpLNNeMpg+pyRPT3JJknuS3Jvk0iR7jbHc2iTnJfmnJD9OcnuSi5Ls0/I3SJJ60PYurA8Bf55kJ5pH2d4B/CLwSuC1wJvnWjjJjsDVNONpHQ0UcAZwTZIDq+qBORZ/JXAAzYjA62meRfI2YF2SX66q77T8LZKkBWj7SNv3J9mDJiiOGTQH+ClwVlX9xTyrOJ7mmerPrKpbAJLcSPOMkROB982x7NlVtWm4IcmXaI56jsce8JK0pNrexvtk4HTgPcBBbOkHcn1VbR5jFUcM5v35Y3CrasMgCI5kjgAZDY9B221JNtEcjUiSltDYAZJke5p+Hy+vqk8Dn+vwfQcAl83Qvh74vbYrS7I/sCfwjQ61SJIWYOyL6FX1CM3ouz9bwPftCsx0pHI3sEubFQ0C7b8Cm4CPLqAmSVIHbe/CupDmYvkkOBf4DeBVc50+S3JCknVJ1m3atNVZMElSR23vwtoI/EGSf6Q5FXUHzZ1UP1dVH5tj+c3MfKQx25HJjJKcBZwAHF1VV841b1WdB5wHsHbt2pprXknS+NoGyPQTB58K/OoM0wuYK0DW01wHGbWG2TsoPkqS04A/AU6qqgvGWUaS1L+2AfLrwH00Tx/s4nLgvUlWV9WtAEmmgOcBp8y3cJL/QNNv5LSqOrdjDZKkHsx7DSTJqiTvTLIZuB64keZ223uq6rbR1zyr+zDNabDLkhyZ5AiaU2HfoemkOP2deyd5JMnbh9peCZwDfB64OslBQ681rX61JGnBxjkCeR1NJ71rgX+k6Qj4cuBe4Ng2X1ZVDyQ5BHg/cAFNJ8SrgJNHhoEPsIpHB9xhg/bDBq9h1wEHt6lFkrQw4wTI8cCHq+rE6YYkJwLnJjmxqn7a5gur6nbmGbW3qjbShMVw2zFs6f0uSVpm49zGuxr45EjbxTRHCHv3XpEkaUUYJ0B2ojldNey+wfvO/ZYjSVopxr0L62lJVg/9vWqo/UfDM07fXSVJemwbN0AumaX9UzO0rZqhTZL0GDNOgLS600qStG2YN0Cq6hNLUYgkaWVpO5iiJEmAASJJ6sgAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqROtl/uAqTlMnXKFcv23RvPOnzZvlvqi0cgkqRODBBJUicGiCSpEwNEktSJASJJ6mTJAyTJ05NckuSeJPcmuTTJXmMu+8Qk70lyR5IHk3w5yQsWu2ZJ0taW9DbeJDsCVwMPAUcDBZwBXJPkwKp6YJ5VfBQ4HHgLcCvwBuDvkjy3qv7PohWuzrxVVnrsWup+IMcDq4FnVtUtAEluBL4FnAi8b7YFkzwH+H3guKr6+KDtOmA9cDpwxOKWLkkattSnsI4Arp8OD4Cq2gB8CThyjGUfBi4eWvYR4G+AQ5M8of9yJUmzWeoAOQD4+gzt64E1Yyy7oap+PMOyjwf2XXh5kqRxLfUprF2BzTO03w3ssoBlp6dvJckJwAmDP+9P8s0x6uzb7sCdXRfO2T1WsrUF1bbIHrPbbZJrWwKTXN9jsrYF7m97zzbhMT8WVlWdB5y3nDUkWVdVa5ezhtlYWzfW1t0k12dt7Sz1KazNzHykMdvRxbjLwpYjEUnSEljqAFlPcy1j1BrgpjGW3WdwK/Dosj8Fbtl6EUnSYlnqALkcOCjJ6umGJFPA8wbT5vJp4HHA7w0tuz3wCuDKqnqo92r7s6yn0OZhbd1YW3eTXJ+1tZCqWrovS54EfA14EHgrTUfCdwM7AwdW1f2D+fYGvg2cXlWnDy3/N8ChNB0JNwCvB14K/EZV3bBkP0SStLRHIIOe5ocANwMXABfRBMEh0+ExEGDVDPUdC3ycpvf6FcDTgcMMD0laekt6BCJJeuxwNN6Wxh0MMskuST6S5M4kDyT5YpJnzzBfbwNELkJtZya5MsldSSrJMV3qGre2JDsneW+SawfzVJKDZ1nfdklOTbIxyU+SfC3JURNS25uTfHrwz7SSvLNLXYN1/VKSDw72ix8P1jc1w3xj7Uc9b7e+a1uO7Tb2Pp7k+CT/lOShJN9M8rpJqC3J0Un+Nsltg/nO71JXFwZIC9kyGOSzaAaDfDWwH81gkE8ami80F/0PA04CjqK5AeCaJL80stqP0owR9naa6zl30AwQ+csTUNtJwA7AZ9rU0rU2YDfgOOAR4AvzrPbdwDuBc4EXA9cDn0zykgmo7XhgT+BTbWqZxb7Av6O5jf3v55hv3P2ol+22SLUtx3Ybax9PcjzwIeBvaf7d+STwl0lev9y1Aa8CnkGzX97boZ7uqsrXmC/gTcDPgH2H2vah+Y/Km4fajqS5QeBFQ21Ppumr8oGhtucM5jt2qG174JvA5ctZ26B9u8H7voNljlnk7Zahz781+M6DZ1jfnjQjOr9rpP0q4MblrG1ku20/mO+dC9jnthv6/NrB+qZG5hlrP+pzu/Vd23Jst3H38UE9PwQ+MdL+MZqe4Y9brtpmWN93gfO7bre2L49A2hl3MMgjgO9X1TVD891D83/+o/P1NUBk37VRVf/c4vsXXFsN/g0Yw6E0459dONJ+IfDsJPssY219brdx1zXuftTnduu7tuXYbuPO91xgD7bebhfQHJn+5jLW1ut2a8sAaWfcwSDnmm+vJDsNzbeh+hkgsu/a+rSQQTRnW99DbN15dP3gvc06+65tOYy7H/W53fqubZJNd34e3U8Wc7utCAZIO+MOBjnfwI+7jDnfjANELlFtfVrIIJqzre9HMxwVLOZ2m2Tj7kd9bre+a5tk0zWO/o6V9BsWhQEiSerEAGln3MEg5xv4cfOY87UZILLv2vq0kEE0Z1vfUwZ3lI2uDxZnu02ycfejPrdb37VNsun9YPR3rKTfsCgMkHbGHQxyrvlury297vscILLv2vq0kEE0Z1vfE2huXRxdHy3X2Xdty2Hc/ajP7dZ3bZNs+lrH6H6ymNttRTBA2hl3MMjLgacleeHQfL8A/M7IfH0OENl3bX1ayCCaM/k8zZ09fzDS/irg64O7qJartuUw7n7U53bru7ZJ9mWa23Vn2m5309yxt016zD9QqmcfBt4IXJZkeDDI79B0Mpp2Oc1Od2GSt9AcAp9KM8bXn03PVFVfTXIxcE6Sx7FlgMh92HpnXdLaAAYhswfwi4OmtUnuH9R+ySLURpIXA08CpnvGvzDJ7sADVfW5wXf/MMn7gFOT3AfcQPMfpENobhtto9faBvOtBabY8j9oa5L87uDzZ2e4I2lOQ8v+6uD9xUk2AZuq6rpx96Oet1uvtQ3WtaTbbTDPvPt4VT2c5G00HQe/B3yRZpsdB5xUVT9tU1eftQ3mW8OWo6EdgL2H1n9dVW1qW9/YlqrDyWPlBexF0xv1XuA+ml6zUzPMtytNR6O7gR/TdNZ6zgzz7QC8D/gB8BPgfzFLB7VlqO1amv+gbvVaxNo2zvKdG0fmW0UzovNtNLem3gj87iJvt3FrO3+27TbTeseob7Z1Xdt2P+pzuy1Cbcux3cbex4ETaQaCfQj4FvCHi7zdxqqNZmSB2da31Xbu8+VgipKkTrwGIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1Mn/B1qthpgjIontAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}