{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with Amazon Braket\n",
    "\n",
    "In this hello-world tutorial we prepare a maximally entangled Bell state between two qubits. We then run our circuit on a local simulator and obtain the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# general imports\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# AWS imports: Import Braket SDK modules\n",
    "from braket.circuits import Circuit\n",
    "from braket.devices import LocalSimulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a circuit\n",
    "\n",
    "Let's build a Bell state with two qubits. By calling `Circuit()` we create an empty circuit, and we can just add gates to the circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a Bell state with two qubits. Here 'cnot(control=0, target=1)' can be simplified as 'cnot(0,1)'\n",
    "bell = Circuit().h(0).cnot(control=0, target=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit the circuit to the local simulator and obtain the results\n",
    "\n",
    "Here we submit our circuit to the local simulator and obtain the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'00': 502, '11': 498})\n"
     ]
    }
   ],
   "source": [
    "# set up device\n",
    "device = LocalSimulator()\n",
    "\n",
    "# run circuit\n",
    "result = device.run(bell, shots=1000).result()\n",
    "# get measurement shots\n",
    "counts = result.measurement_counts\n",
    "# print counts\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cUlUPTHMa/YAMBUlS4+0jSVJjKEiSGkNBktQYCpKkxlCQJDWGgua1JMsGZ4sdaH9/NwEbSd4+xHmuSPKMafa380mzmY+kal5Lsgy4vqqeP02fh6vqmY9znruBlVV13yT7FlTVkeMsVZoRXilIcHKSTUluTfKpJM9IckOSlUmuAp6eZGeSjyc5Nclnk3wtya4kv5Dkt4DnAF9M8kUYD5Ikf5jky8DLjp5vYN+G7hw3JVnUtT+v2765O/bhrn1xkhu7GnYlecVo/jNpPjAUJPgRYGNVvRB4EPj1ozuqaj3wnao6v6p+ifH3UOyrqvO6q4vPVdVfMD4vz6uq6lXdoacCu6rqJVX1pQmfdypwU1Wdx/j7K361a78auLqqfrw731G/CHy+qs4HzgN2nrg/uvRYhoIE91bVv3TrHwN+cpq+Xwdek+RdSV5RVf8zRb8jwKen2Pdd4Ppu/RZgWbf+MuBvu/VPDPS/GXhLkncCL6iqh6apTzouhoJ07JTMUw60VdW/Ay9mPBz+JMnvT9H1/6YZR/hePTqYdwQ4edrixl+K9FPAfwIfTfIr0/WXjoehIMHZSV7WrV8KTLzd870kTwFI8hzg21X1MeDdwI91fR4CTjvOOm4Cfq5bX3O0MclzgYNV9TfABwY+UzrhDAUJbgfWJrkVWAj81YT9G4Fbk3wceAGwPclO4PeAPx7o849HB5qfoCuA306yHVgMHL019UpgZ5KvMh4aVx/HZ0jT8pFUaZbovufwnaqqJGuAS6tq9ajr0vwy7b1MSTPqxcBfJgnj77R464jr0TzklYIkqXFMQZLUGAqSpMZQkCQ1hoIkqTEUJEnN/wOJsD3A1LLD/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot using Counter\n",
    "plt.bar(counts.keys(), counts.values());\n",
    "plt.xlabel('bitstrings');\n",
    "plt.ylabel('counts');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_braket",
   "language": "python",
   "name": "conda_braket"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}