examples/getting_started/0_Getting_started/0_Getting_started.ipynb

{
 "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": {
    "ExecuteTime": {
     "end_time": "2023-08-29T20:32:04.268865Z",
     "start_time": "2023-08-29T20:32:01.949092Z"
    }
   },
   "outputs": [],
   "source": [
    "# general imports\n",
    "import matplotlib.pyplot as plt\n",
    "\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": {
    "ExecuteTime": {
     "end_time": "2023-08-29T20:32:04.275199Z",
     "start_time": "2023-08-29T20:32:04.271532Z"
    }
   },
   "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": {
    "ExecuteTime": {
     "end_time": "2023-08-29T20:32:04.490801Z",
     "start_time": "2023-08-29T20:32:04.276611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'11': 506, '00': 494})\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": {
    "ExecuteTime": {
     "end_time": "2023-08-29T20:32:04.661340Z",
     "start_time": "2023-08-29T20:32:04.499868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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
}