examples/getting_started/2_Running_quantum_circuits_on_QPU_devices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running quantum circuits on QPU devices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we show how to run circuits on QPU devices. We can run our circuit on both the superconducting machine from Rigetti and the ion-trap machine provided by IonQ. As shown below, one can seamlessly swap between different devices without any modifications to the circuit definition, by just re-defining the device object. We also show how to recover results using the unique ARN associated with every task. This tool is useful in order to deal with potential delays if your quantum task sits in the queue for some time waiting for execution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# general imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "import string\n",
    "import time\n",
    "\n",
    "# AWS imports: Import Braket SDK modules\n",
    "from braket.circuits import Circuit, Gate, Instruction, circuit, Observable\n",
    "from braket.devices import LocalSimulator\n",
    "from braket.aws import AwsDevice, AwsQuantumTask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Supported gate sets by different devices\n",
    "\n",
    "First, let us check out the gate set currently supported by our SDK. Note that the QPU devices only support a subset of these gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gate set supported by SDK:\n",
      " ['CCNot', 'CNot', 'CPhaseShift', 'CPhaseShift00', 'CPhaseShift01', 'CPhaseShift10', 'CSwap', 'CV', 'CY', 'CZ', 'ECR', 'H', 'I', 'ISwap', 'PSwap', 'PhaseShift', 'Rx', 'Ry', 'Rz', 'S', 'Si', 'Swap', 'T', 'Ti', 'Unitary', 'V', 'Vi', 'X', 'XX', 'XY', 'Y', 'YY', 'Z', 'ZZ']\n",
      "\n",
      "\n",
      "Gate set supported by the Rigetti device:\n",
      " ['cz', 'xy', 'ccnot', 'cnot', 'cphaseshift', 'cphaseshift00', 'cphaseshift01', 'cphaseshift10', 'cswap', 'h', 'i', 'iswap', 'phaseshift', 'pswap', 'rx', 'ry', 'rz', 's', 'si', 'swap', 't', 'ti', 'x', 'y', 'z', 'start_verbatim_box', 'end_verbatim_box']\n",
      "\n",
      "\n",
      "Gate set supported by the IonQ device:\n",
      " ['x', 'y', 'z', 'rx', 'ry', 'rz', 'h', 'cnot', 's', 'si', 't', 'ti', 'v', 'vi', 'xx', 'yy', 'zz', 'swap', 'i']\n",
      "\n",
      "\n",
      "Gate set supported by the OQC device:\n",
      " ['ccnot', 'cnot', 'cphaseshift', 'cswap', 'cy', 'cz', 'h', 'i', 'phaseshift', 'rx', 'ry', 'rz', 's', 'si', 'swap', 't', 'ti', 'v', 'vi', 'x', 'y', 'z', 'ecr', 'start_verbatim_box', 'end_verbatim_box']\n"
     ]
    }
   ],
   "source": [
    "# print all (the usual suspects) available gates currently available within SDK\n",
    "gate_set = [attr for attr in dir(Gate) if attr[0] in string.ascii_uppercase]\n",
    "print('Gate set supported by SDK:\\n', gate_set)\n",
    "print('\\n') \n",
    "\n",
    "# the Rigetti device\n",
    "device = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-1\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the Rigetti device:\\n', supported_gates)\n",
    "print('\\n') \n",
    "\n",
    "# the IonQ device\n",
    "device = AwsDevice(\"arn:aws:braket:::device/qpu/ionq/ionQdevice\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the IonQ device:\\n', supported_gates)\n",
    "print('\\n') \n",
    "\n",
    "# the Oxford Quantum Circuits (OQC) device\n",
    "device = AwsDevice(\"arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the OQC device:\\n', supported_gates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Problem: Prepare Bell State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Prepare maximally entangled Bell state: \n",
    "$$\\left|0,0\\right> \\rightarrow \\left|\\mathrm{Bell}\\right> = \\frac{1}{\\sqrt{2}}\\left(\\left|0,0\\right> + \\left|1,1\\right>\\right).$$"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To this end, we build and run the following circuit using a single-qubit Hadamard gate (denoted as ```H```) acting on the first qubit followed by a two-qubit ```CNOT``` gate: \n",
    "<div>\n",
    "<img src=\"attachment:image-2.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# define circuit\n",
    "bell = Circuit().h(0).cnot(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T  : |0|1|\n",
      "          \n",
      "q0 : -H-C-\n",
      "        | \n",
      "q1 : ---X-\n",
      "\n",
      "T  : |0|1|\n"
     ]
    }
   ],
   "source": [
    "# print circuit\n",
    "print(bell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Local Simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we run our circuit locally, using an exact state-vector simulator. This local simulator is the preferred choice for fast experiments with low to intermediate qubit numbers ($N<20-25$) and essentially unlimited circuit depth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# set up device: Local Simulator\n",
    "device = LocalSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'11': 506, '00': 494})\n"
     ]
    }
   ],
   "source": [
    "# run circuit (execute single TASK)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAQLklEQVR4nO3de6xlZXnH8e8PRrygyG1KYYY6VEhbUgvSCULVRiEqYCvEguJ1ipNOk2IrsUlF/6jWYAJpLYK2JFNRBu+ItVA0KkEssSnoIHeodUogMAIz3KGICj79Y7/zsh3ODBtn1tmHOd9PsrPf9ax3r/0MOeGXddlrpaqQJAlgu2k3IEmaOwwFSVJnKEiSOkNBktQZCpKkbsG0G9gSu+++ey1ZsmTabUjSM8qVV155d1UtnGndMzoUlixZwurVq6fdhiQ9oyS5dVPrPHwkSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEndoKGQ5JYk1yW5OsnqVts1ycVJftTed2n1JDkzyZok1yY5aMjeJElPNhu/aH51Vd09tnwycElVnZrk5Lb8PuBIYL/2ehlwVnuX5qUlJ39t2i1oDrvl1NcPst1pHD46GljVxquAY8bq59bI5cDOSfacQn+SNG8NHQoFfCvJlUlWtNoeVXVHG98J7NHGi4Dbxj57e6v9kiQrkqxOsnr9+vVD9S1J89LQh49eUVVrk/wacHGS/x5fWVWV5Gk9JLqqVgIrAZYuXeoDpiVpKxp0T6Gq1rb3dcBXgYOBuzYcFmrv69r0tcDeYx9f3GqSpFky2J5Ckh2B7arqoTZ+LfBh4EJgGXBqe7+gfeRC4N1JvsjoBPMDY4eZtjpP4mlzhjqJJ811Qx4+2gP4apIN3/P5qvpGku8D5yVZDtwKvKnN/zpwFLAGeAQ4YcDeJEkzGCwUqupm4IAZ6vcAh89QL+DEofqRJD01f9EsSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEnd4KGQZPskVyW5qC3vk+SKJGuSfCnJDq3+7La8pq1fMnRvkqRfNht7Cu8BbhpbPg04var2Be4Dlrf6cuC+Vj+9zZMkzaJBQyHJYuD1wCfbcoDDgPPblFXAMW18dFumrT+8zZckzZKh9xQ+BvwN8Iu2vBtwf1U91pZvBxa18SLgNoC2/oE2/5ckWZFkdZLV69evH7B1SZp/BguFJH8ErKuqK7fmdqtqZVUtraqlCxcu3JqblqR5b8GA23458IYkRwHPAXYCzgB2TrKg7Q0sBta2+WuBvYHbkywAXgjcM2B/kqSNDLanUFXvr6rFVbUEOB74dlW9DbgUOLZNWwZc0MYXtmXa+m9XVQ3VnyTpyabxO4X3Ae9NsobROYOzW/1sYLdWfy9w8hR6k6R5bcjDR11VfQf4ThvfDBw8w5xHgeNmox9J0sz8RbMkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWDhUKS5yT5XpJrktyQ5O9afZ8kVyRZk+RLSXZo9We35TVt/ZKhepMkzWzIPYWfAodV1QHAgcARSQ4BTgNOr6p9gfuA5W3+cuC+Vj+9zZMkzaLBQqFGHm6Lz2qvAg4Dzm/1VcAxbXx0W6atPzxJhupPkvRkg55TSLJ9kquBdcDFwP8C91fVY23K7cCiNl4E3AbQ1j8A7DbDNlckWZ1k9fr164dsX5LmnYlCIcl7kuyUkbOT/CDJa5/qc1X1eFUdCCwGDgZ+e8vahapaWVVLq2rpwoULt3RzkqQxk+4pvKuqHgReC+wCvAM4ddIvqar7gUuBQ4GdkyxoqxYDa9t4LbA3QFv/QuCeSb9DkrTlJg2FDcf2jwI+U1U3jNVm/kCyMMnObfxc4DXATYzC4dg2bRlwQRtf2JZp679dVTVhf5KkrWDBU08B4Mok3wL2Ad6f5AXAL57iM3sCq5Jszyh8zquqi5LcCHwxySnAVcDZbf7ZwGeSrAHuBY5/mv8WSdIWmjQUljO6rPTmqnokyW7ACZv7QFVdC7x0hvrNjM4vbFx/FDhuwn4kSQOY9PDRxVX1g3ZugKq6h9FvCSRJ25DN7ikkeQ7wPGD3JLvwxHmEnXjiUlJJ0jbiqQ4f/TlwErAXcCVPhMKDwCeGa0uSNA2bDYWqOgM4I8lfVtXHZ6knSdKUTHSiuao+nuQPgCXjn6mqcwfqS5I0BROFQpLPAC8GrgYeb+UCDAVJ2oZMeknqUmB/f0wmSdu2SS9JvR749SEbkSRN36R7CrsDNyb5HqPnJABQVW8YpCtJ0lRMGgofGrIJSdLcMOnVR/8xdCOSpOmb9OqjhxhdbQSwA6OnqP1fVe00VGOSpNk36Z7CCzaM2yMyjwYOGaopSdJ0PO3HcbZnL/8b8Lqt344kaZomPXz0xrHF7Rj9buHRQTqSJE3NpFcf/fHY+DHgFkaHkCRJ25BJzyls9oE6kqRtw0TnFJIsTvLVJOva6ytJFg/dnCRpdk16ovnTwIWMnquwF/DvrSZJ2oZMGgoLq+rTVfVYe50DLBywL0nSFEwaCvckeXuS7dvr7cA9QzYmSZp9k4bCu4A3AXcCdwDHAn86UE+SpCmZ9JLUDwPLquo+gCS7Av/AKCwkSduISfcUfm9DIABU1b3AS4dpSZI0LZOGwnZJdtmw0PYUJt3LkCQ9Q0z6P/aPAv+V5Mtt+TjgI8O0JEmalkl/0XxuktXAYa30xqq6cbi2JEnTMPEhoBYCBoEkbcOe9q2zJUnbLkNBktQZCpKkzlCQJHWDhUKSvZNcmuTGJDckeU+r75rk4iQ/au+7tHqSnJlkTZJrkxw0VG+SpJkNuafwGPDXVbU/cAhwYpL9gZOBS6pqP+CStgxwJLBfe60AzhqwN0nSDAYLhaq6o6p+0MYPATcBixg9xnNVm7YKOKaNjwbOrZHLgZ2T7DlUf5KkJ5uVcwpJljC6V9IVwB5VdUdbdSewRxsvAm4b+9jtrbbxtlYkWZ1k9fr164drWpLmocFDIcnzga8AJ1XVg+PrqqqAejrbq6qVVbW0qpYuXOhzfiRpaxo0FJI8i1EgfK6q/rWV79pwWKi9r2v1tcDeYx9f3GqSpFky5NVHAc4GbqqqfxxbdSGwrI2XAReM1d/ZrkI6BHhg7DCTJGkWDHn765cD7wCuS3J1q30AOBU4L8ly4FZGT3QD+DpwFLAGeAQ4YcDeJEkzGCwUquq7QDax+vAZ5hdw4lD9SJKemr9oliR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkbrBQSPKpJOuSXD9W2zXJxUl+1N53afUkOTPJmiTXJjloqL4kSZs25J7COcARG9VOBi6pqv2AS9oywJHAfu21AjhrwL4kSZswWChU1WXAvRuVjwZWtfEq4Jix+rk1cjmwc5I9h+pNkjSz2T6nsEdV3dHGdwJ7tPEi4Laxebe32pMkWZFkdZLV69evH65TSZqHpnaiuaoKqF/hcyuramlVLV24cOEAnUnS/DXboXDXhsNC7X1dq68F9h6bt7jVJEmzaLZD4UJgWRsvAy4Yq7+zXYV0CPDA2GEmSdIsWTDUhpN8AXgVsHuS24EPAqcC5yVZDtwKvKlN/zpwFLAGeAQ4Yai+JEmbNlgoVNVbNrHq8BnmFnDiUL1IkibjL5olSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKmbU6GQ5IgkP0yyJsnJ0+5HkuabORMKSbYH/gk4EtgfeEuS/afblSTNL3MmFICDgTVVdXNV/Qz4InD0lHuSpHllwbQbGLMIuG1s+XbgZRtPSrICWNEWH07yw1nobT7YHbh72k3MFTlt2h1oBv6NjtnCv9EXbWrFXAqFiVTVSmDltPvY1iRZXVVLp92HtCn+jc6OuXT4aC2w99jy4laTJM2SuRQK3wf2S7JPkh2A44ELp9yTJM0rc+bwUVU9luTdwDeB7YFPVdUNU25rPvGQnOY6/0ZnQapq2j1IkuaIuXT4SJI0ZYaCJKkzFOahmW4n0k7wX9FqX2on+6WpSPKpJOuSXD9WOy7JDUl+kcRLUwdiKMwzm7mdyGnA6VW1L3AfsHx6XUqcAxyxUe164I3AZbPezTxiKMw/m7qdyGHA+W3OKuCY6bQnQVVdBty7Ue2mqvIOBgMzFOafmW4nsgi4v6oe26gmaZ4xFCRJnaEw/2zqdiI7J1mwUU3SPGMozD+bup3IpcCxbc4y4IIp9SdpivxF8zyU5CjgYzxxO5GPJPlNRieddwWuAt5eVT+dXpeaz5J8AXgVo9tl3wV8kNGJ548DC4H7gaur6nVTanGbZShIkjoPH0mSOkNBktQZCpKkzlCQJHWGgiSpMxQ0ryVZMn4nzrH6J9uNAknygQm2c1KS521mfd+eNJd5SarmtSRLgIuq6nc3M+fhqnr+U2znFmBpVd09w7rtq+rxLe1Vmg3uKUiwIMnnktyU5Pwkz0vynSRLk5wKPDfJ1W3Ojkm+luSaJNcneXOSvwL2Ai5NcimMgiTJR5NcAxy6YXtj6z7StnF5kj1a/cVt+bokpyR5uNX3THJZ6+H6JK+czn8mzQeGggS/BfxzVf0O8CDwFxtWVNXJwE+q6sCqehuje/z/uKoOaHsX36iqM4EfA6+uqle3j+4IXNHmfXej79sRuLyqDmD0bIA/a/UzgDOq6iWM7lS7wVuBb1bVgcABwNVb6x8ubcxQkOC2qvrPNv4s8IrNzL0OeE2S05K8sqoe2MS8x4GvbGLdz4CL2vhKYEkbHwp8uY0/Pzb/+8AJST4EvKSqHtpMf9IWMRQk2PjE2iZPtFXV/wAHMQqHU5L87SamPrqZ8wg/rydO5j0OLNjEvA3feRnwh4zuXHtOkndubr60JQwFCX4jyaFt/FZg48M9P0/yLIAkewGPVNVngb9nFBAADwEv2MI+Lgf+pI2P31BM8iLgrqr6F+CTY98pbXWGggQ/BE5MchOwC3DWRutXAtcm+RzwEuB7Sa5mdOfOU8bmfGPDieZf0UnAe5NcC+wLbDg09SrgmiRXAW9mdO5BGoSXpEpzRPudw0+qqpIcD7ylqo6edl+aXzZ7LFPSrPp94BNJwuh5Ae+abjuaj9xTkCR1nlOQJHWGgiSpMxQkSZ2hIEnqDAVJUvf/Yi8fay6i/hwAAAAASUVORK5CYII=",
      "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Quantum Hardware: Rigetti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we submit our circuit to the superconducting quantum chip provided by Rigetti. Depending on our position in the queue, we may have to wait for some time till our circuit is actually run. However, thanks to asynchronous execution, we can always come back and recover the results by providing the unique ID associated with every task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: CREATED\n"
     ]
    }
   ],
   "source": [
    "# set up device\n",
    "rigetti = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-1\")\n",
    "\n",
    "# create a clean circuit with no result type attached.(This is because some result types are only supported when shots=0)\n",
    "bell = Circuit().h(0).cnot(0, 1)  \n",
    "\n",
    "# add the Z \\otimes Z expectation value\n",
    "bell.expectation(Observable.Z() @ Observable.Z(), target=[0,1])\n",
    "\n",
    "# run circuit \n",
    "rigetti_task = rigetti.run(bell, shots=1000)\n",
    "\n",
    "# get id and status of submitted task\n",
    "rigetti_task_id = rigetti_task.id\n",
    "rigetti_status = rigetti_task.state()\n",
    "# print('ID of task:', rigetti_task_id)\n",
    "print('Status of task:', rigetti_status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The task is submitted and we can regularly (or irregularly) check the status of this task by executing the following cell. You may easily build logic around this query to wait for this task to complete before your code proceeds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of (reconstructed) task: COMPLETED\n"
     ]
    }
   ],
   "source": [
    "# print status\n",
    "status = rigetti_task.state()\n",
    "print('Status of (reconstructed) task:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Quantum Hardware: IonQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we submit our example Bell state circuit to IonQ. We set the device as AwsDevice(\"arn:aws:braket:::device/qpu/ionq/ionQdevice\"). This task may not readily be executed but enter a queue for this specific machine. While we can interrupt our kernel (and work on something else), we can always recover our results using the unique ID of this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: CREATED\n"
     ]
    }
   ],
   "source": [
    "# set up device\n",
    "ionq = AwsDevice(\"arn:aws:braket:::device/qpu/ionq/ionQdevice\")\n",
    "\n",
    "# run circuit\n",
    "ionq_task = ionq.run(bell, shots=1000)\n",
    "\n",
    "# get id and status of submitted task\n",
    "ionq_task_id = ionq_task.id\n",
    "ionq_status = ionq_task.state()\n",
    "# print('ID of task:', ionq_task_id)\n",
    "print('Status of task:', ionq_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of (reconstructed) task: COMPLETED\n"
     ]
    }
   ],
   "source": [
    "# print status\n",
    "status = ionq_task.state()\n",
    "print('Status of (reconstructed) task:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantum Hardware: Oxford Quantum Circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we submit our circuit to the superconducting quantum computer provided by Oxford Quantum Circuits (OQC). The task runs asynchronously. We can retrieve the result when the task is completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: COMPLETED\n"
     ]
    }
   ],
   "source": [
    "# set up device\n",
    "oqc = AwsDevice(\"arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy\")\n",
    "\n",
    "# run circuit\n",
    "oqc_task = oqc.run(bell, shots=1000)\n",
    "\n",
    "# get id and status of submitted task\n",
    "oqc_task_id = oqc_task.id\n",
    "oqc_status = oqc_task.state()\n",
    "# print('ID of task:', oqc_task_id)\n",
    "print('Status of task:', oqc_status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Task Recovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply grabbing the unique task ID associated with the quantum tasks we have submitted above, we can recover this task at any point in time and (once the status is completed) visualize and analyze the corresponding results. Note that apart from other metadata, you can retrieve the compiled circuit that was actually run on the Rigetti device. More information about the compiling process can be found [here](https://pyquil-docs.rigetti.com/en/v2.22.0/compiler.html#partial). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of (reconstructed) task: COMPLETED\n",
      "\n",
      "\n",
      "1000 shots taken on machine arn:aws:braket:::device/qpu/rigetti/Aspen-9.\n",
      "\n",
      "The compiled circuit is:\n",
      " DECLARE ro BIT[2]\n",
      "PRAGMA INITIAL_REWIRING \"PARTIAL\"\n",
      "RESET\n",
      "RZ(pi) 12\n",
      "RZ(-pi/2) 13\n",
      "RX(-pi/2) 13\n",
      "XY(pi) 13 12\n",
      "RZ(pi/2) 13\n",
      "RX(pi/2) 13\n",
      "RZ(-pi/2) 13\n",
      "XY(pi) 13 12\n",
      "RX(-pi/2) 12\n",
      "MEASURE 12 ro[1]\n",
      "MEASURE 13 ro[0]\n",
      "\n",
      "Measurement counts: Counter({'11': 458, '00': 421, '10': 81, '01': 40})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recover task\n",
    "task_load = AwsQuantumTask(arn=rigetti_task_id)\n",
    "\n",
    "# print status\n",
    "status = task_load.state()\n",
    "print('Status of (reconstructed) task:', status)\n",
    "print('\\n')\n",
    "# wait for job to complete\n",
    "# terminal_states = ['COMPLETED', 'FAILED', 'CANCELLED']\n",
    "if status == 'COMPLETED':\n",
    "    # get results\n",
    "    rigetti_results = task_load.result()\n",
    "    # print(rigetti_results)\n",
    "    \n",
    "    # get all metadata of submitted task\n",
    "    metadata = task_load.metadata()\n",
    "    # example for metadata\n",
    "    shots = metadata['shots']\n",
    "    machine = metadata['deviceArn']\n",
    "    # print example metadata\n",
    "    print(\"{} shots taken on machine {}.\\n\".format(shots, machine))\n",
    "    \n",
    "    # get the compiled circuit\n",
    "    print(\"The compiled circuit is:\\n\", rigetti_results.additional_metadata.rigettiMetadata.compiledProgram)\n",
    "    \n",
    "    # get measurement counts\n",
    "    rigetti_counts = rigetti_results.measurement_counts\n",
    "    print('Measurement counts:', rigetti_counts)\n",
    "\n",
    "    # plot results: see effects of noise\n",
    "    plt.bar(rigetti_counts.keys(), rigetti_counts.values())\n",
    "    plt.xlabel('bitstrings')\n",
    "    plt.ylabel('counts')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('rigetti.png', dpi=700)\n",
    "    \n",
    "elif status in ['FAILED', 'CANCELLED']:\n",
    "    # print terminal message \n",
    "    print('Your task is in terminal status, but has not completed.')\n",
    "\n",
    "else:\n",
    "    # print current status\n",
    "    print('Sorry, your task is still being processed and has not been finalized yet.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of (reconstructed) task: COMPLETED\n",
      "1000 shots taken on machine arn:aws:braket:::device/qpu/ionq/ionQdevice.\n",
      "Measurement counts: Counter({'11': 501, '00': 456, '01': 25, '10': 18})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recover task\n",
    "task_load = AwsQuantumTask(arn=ionq_task_id)\n",
    "\n",
    "# print status\n",
    "status = task_load.state()\n",
    "print('Status of (reconstructed) task:', status)\n",
    "\n",
    "# wait for job to complete\n",
    "# terminal_states = ['COMPLETED', 'FAILED', 'CANCELLED']\n",
    "if status == 'COMPLETED':\n",
    "    # get results\n",
    "    results = task_load.result()\n",
    "    # print(rigetti_results)\n",
    "    \n",
    "    # get all metadata of submitted task\n",
    "    metadata = task_load.metadata()\n",
    "    # example for metadata\n",
    "    shots = metadata['shots']\n",
    "    machine = metadata['deviceArn']\n",
    "    # print example metadata\n",
    "    print(\"{} shots taken on machine {}.\".format(shots, machine))\n",
    "    \n",
    "    # get measurement counts\n",
    "    counts = results.measurement_counts\n",
    "    print('Measurement counts:', counts)\n",
    "\n",
    "    # plot results: see effects of noise\n",
    "    plt.bar(counts.keys(), counts.values())\n",
    "    plt.xlabel('bitstrings')\n",
    "    plt.ylabel('counts')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('bell_ionq.png', dpi=700)\n",
    "    \n",
    "elif status in ['FAILED', 'CANCELLED']:\n",
    "    # print terminal message \n",
    "    print('Your task is in terminal status, but has not completed.')\n",
    "\n",
    "else:\n",
    "    # print current status\n",
    "    print('Sorry, your task is still being processed and has not been finalized yet.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We have successfully recovered the results associated with the tasks that were queued up for the quantum hardware providers. Because of noise (decoherence) and other imperfections we cannot fully recover the exact results we have seen when using the classical simulator. To deal with that, in the fullness of time we will be able to use error correction techniques. As long as error correction is not available, it is important to benchmark our quantum results using classical simulators, whenever possible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# APPENDIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# set up device\n",
    "rigetti = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-1\")\n",
    "\n",
    "# run circuit\n",
    "task = rigetti.run(bell, shots=1000)\n",
    "print('Information on task:\\n', task)\n",
    "print('==================================' * 2)\n",
    "\n",
    "# get status of submitted task\n",
    "status = task.state()\n",
    "\n",
    "# wait for job to complete\n",
    "terminal_states = ['COMPLETED', 'FAILED', 'CANCELLED']\n",
    "while status not in terminal_states:\n",
    "    status = task.state()\n",
    "    print('Status:', status)\n",
    "    # time.sleep(60)\n",
    "\n",
    "print('Status:', status)\n",
    "\n",
    "# get results\n",
    "rigetti_results = task.result()\n",
    "print(rigetti_results)\n",
    "\n",
    "# get measurement counts\n",
    "rigetti_counts = rigetti_results.measurement_counts\n",
    "print(rigetti_counts)\n",
    "\n",
    "# plot results: see effects of noise\n",
    "plt.bar(rigetti_counts.keys(), rigetti_counts.values())\n",
    "plt.xlabel('bitstrings')\n",
    "plt.ylabel('counts')\n",
    "plt.tight_layout()\n",
    "plt.savefig('rigetti2.png', dpi=700)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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