QuSTEAM_HAQS_getting_started.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": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:#D30982;\">qBraid instructions</h1> \n",
    "\n",
    "If you are not planning to submit jobs to quantum computers, this notebook should work well with `Python [Default]` kernel. If you want to submit jobs to quantum computers, make sure that you have installed the amazon braket environment on qBraid ([installation instructions](https://qbraid-qbraid.readthedocs-hosted.com/en/latest/lab/environments.html#install-environment)) and have selected the `Python [Braket]` kernel (Menu Bar->Kernel->Change Kernel).\n",
    "\n",
    "If you do not have aws credentials for submitting your quantum jobs, you will need qBraid-Credits. If you have qBraid-Credits, you can run the following line of code to submit quantum jobs through qBraid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!qbraid jobs enable amazon_braket #You only need to run this line once after you install the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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\n",
    "s3_folder = (\"dummy_variable\", \"dummy_variable\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "<h1 style=\"color:#D30982;\">Investigate Entanglement</h1> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create an entangled state\n",
    "\n",
    "Goal: Prepare maximally entangled Bell state: \n",
    "$$\\left|0,0\\right> \\rightarrow \\left|\\mathrm{Bell}\\right> = \\frac{1}{\\sqrt{2}}\\left(\\left|0,0\\right> + \\left|1,1\\right>\\right).$$"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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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": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# define circuit\n",
    "bell = Circuit().h(0).cnot(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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| <class 'braket.circuits.circuit.Circuit'>\n"
     ]
    }
   ],
   "source": [
    "# print circuit\n",
    "print(bell, type(bell))\n"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# set up device: Local Simulator\n",
    "device = LocalSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'11': 505, '00': 495})\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": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'counts')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/av/WaAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQM0lEQVR4nO3de6xlZXnH8e9PRrwgyG1KYYY6VEhbUgvSCULVRiEqYCvEguKNKU46TUpbiU0q+ke1DSaQ1iJoSzIVZfCOWAtFoxLEEpuCDnKHWqcEAiMwwx2KqODTP/Y7L9vhzLBxZp19mPP9JDv7Xc9699rPkBN+WZe9VqoKSZIAnjPtBiRJc4ehIEnqDAVJUmcoSJI6Q0GS1C2YdgNbYvfdd68lS5ZMuw1Jela56qqr7qmqhTOte1aHwpIlS1i9evW025CkZ5Ukt21qnYePJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpGzQUktya5Pok1yRZ3Wq7JrkkyQ/b+y6tniRnJVmT5LokBw3ZmyTpqWbjF82vrap7xpZPAS6tqtOSnNKW3wccCezXXq8Azm7vg1hyyleH2rS2Abee9sZptyBNxTQOHx0NrGrjVcAxY/XzauQKYOcke06hP0mat4YOhQK+meSqJCtabY+qurON7wL2aONFwO1jn72j1X5BkhVJVidZvX79+qH6lqR5aejDR6+qqrVJfgW4JMl/j6+sqkryjB4SXVUrgZUAS5cu9QHTkrQVDRoKVbW2va9L8hXgYODuJHtW1Z3t8NC6Nn0tsPfYxxe3mjQved5LmzPUea/BDh8l2SHJjhvGwOuBG4CLgGVt2jLgwja+CDihXYV0CPDg2GEmSdIsGHJPYQ/gK0k2fM/nqurrSb4HnJ9kOXAb8JY2/2vAUcAa4FHgxAF7kyTNYLBQqKpbgANmqN8LHD5DvYCThupHkvT0/EWzJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1g4dCku2SXJ3k4ra8T5Irk6xJ8sUk27f689rymrZ+ydC9SZJ+0WzsKbwHuHls+XTgjKraF7gfWN7qy4H7W/2MNk+SNIsGDYUki4E3Ap9oywEOAy5oU1YBx7Tx0W2Ztv7wNl+SNEuG3lP4KPDXwM/b8m7AA1X1eFu+A1jUxouA2wHa+gfb/F+QZEWS1UlWr1+/fsDWJWn+GSwUkvwBsK6qrtqa262qlVW1tKqWLly4cGtuWpLmvQUDbvuVwJuSHAU8H9gJOBPYOcmCtjewGFjb5q8F9gbuSLIAeDFw74D9SZI2MtieQlW9v6oWV9US4HjgW1X1DuAy4Ng2bRlwYRtf1JZp679VVTVUf5Kkp5rG7xTeB7w3yRpG5wzOafVzgN1a/b3AKVPoTZLmtSEPH3VV9W3g2218C3DwDHMeA46bjX4kSTPzF82SpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQNFgpJnp/ku0muTXJjkr9t9X2SXJlkTZIvJtm+1Z/Xlte09UuG6k2SNLMh9xR+AhxWVQcABwJHJDkEOB04o6r2Be4Hlrf5y4H7W/2MNk+SNIsGC4UaeaQtPre9CjgMuKDVVwHHtPHRbZm2/vAkGao/SdJTDXpOIcl2Sa4B1gGXAP8LPFBVj7cpdwCL2ngRcDtAW/8gsNsM21yRZHWS1evXrx+yfUmadyYKhSTvSbJTRs5J8v0kr3+6z1XVE1V1ILAYOBj4zS1rF6pqZVUtraqlCxcu3NLNSZLGTLqn8O6qegh4PbAL8C7gtEm/pKoeAC4DDgV2TrKgrVoMrG3jtcDeAG39i4F7J/0OSdKWmzQUNhzbPwr4dFXdOFab+QPJwiQ7t/ELgNcBNzMKh2PbtGXAhW18UVumrf9WVdWE/UmStoIFTz8FgKuSfBPYB3h/kh2Bnz/NZ/YEViXZjlH4nF9VFye5CfhCklOBq4Fz2vxzgE8nWQPcBxz/DP8tkqQtNGkoLGd0WektVfVokt2AEzf3gaq6Dnj5DPVbGJ1f2Lj+GHDchP1IkgYw6eGjS6rq++3cAFV1L6PfEkiStiGb3VNI8nzghcDuSXbhyfMIO/HkpaSSpG3E0x0++lPgZGAv4CqeDIWHgI8P15YkaRo2GwpVdSZwZpK/qKqPzVJPkqQpmehEc1V9LMnvAUvGP1NV5w3UlyRpCiYKhSSfBl4KXAM80coFGAqStA2Z9JLUpcD+/phMkrZtk16SegPwq0M2Ikmavkn3FHYHbkryXUbPSQCgqt40SFeSpKmYNBQ+NGQTkqS5YdKrj/5j6EYkSdM36dVHDzO62ghge0ZPUfu/qtppqMYkSbNv0j2FHTeM2yMyjwYOGaopSdJ0POPHcbZnL/8b8Iat344kaZomPXz05rHF5zD63cJjg3QkSZqaSa8++sOx8ePArYwOIUmStiGTnlPY7AN1JEnbhonOKSRZnOQrSda115eTLB66OUnS7Jr0RPOngIsYPVdhL+DfW02StA2ZNBQWVtWnqurx9joXWDhgX5KkKZg0FO5N8s4k27XXO4F7h2xMkjT7Jg2FdwNvAe4C7gSOBf54oJ4kSVMy6SWpfwcsq6r7AZLsCvwDo7CQJG0jJt1T+J0NgQBQVfcBLx+mJUnStEwaCs9JssuGhbanMOlehiTpWWLS/7F/BPivJF9qy8cBHx6mJUnStEz6i+bzkqwGDmulN1fVTcO1JUmahokPAbUQMAgkaRv2jG+dLUnadhkKkqTOUJAkdYaCJKkbLBSS7J3ksiQ3JbkxyXtafdcklyT5YXvfpdWT5Kwka5Jcl+SgoXqTJM1syD2Fx4G/qqr9gUOAk5LsD5wCXFpV+wGXtmWAI4H92msFcPaAvUmSZjBYKFTVnVX1/TZ+GLgZWMToMZ6r2rRVwDFtfDRwXo1cAeycZM+h+pMkPdWsnFNIsoTRvZKuBPaoqjvbqruAPdp4EXD72MfuaLWNt7Uiyeokq9evXz9c05I0Dw0eCkleBHwZOLmqHhpfV1UF1DPZXlWtrKqlVbV04UKf8yNJW9OgoZDkuYwC4bNV9a+tfPeGw0LtfV2rrwX2Hvv44laTJM2SIa8+CnAOcHNV/ePYqouAZW28DLhwrH5CuwrpEODBscNMkqRZMOTtr18JvAu4Psk1rfYB4DTg/CTLgdsYPdEN4GvAUcAa4FHgxAF7kyTNYLBQqKrvANnE6sNnmF/ASUP1I0l6ev6iWZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSusFCIcknk6xLcsNYbdcklyT5YXvfpdWT5Kwka5Jcl+SgofqSJG3akHsK5wJHbFQ7Bbi0qvYDLm3LAEcC+7XXCuDsAfuSJG3CYKFQVZcD921UPhpY1cargGPG6ufVyBXAzkn2HKo3SdLMZvucwh5VdWcb3wXs0caLgNvH5t3Rak+RZEWS1UlWr1+/frhOJWkemtqJ5qoqoH6Jz62sqqVVtXThwoUDdCZJ89dsh8LdGw4Ltfd1rb4W2Hts3uJWkyTNotkOhYuAZW28DLhwrH5CuwrpEODBscNMkqRZsmCoDSf5PPAaYPckdwAfBE4Dzk+yHLgNeEub/jXgKGAN8Chw4lB9SZI2bbBQqKq3bWLV4TPMLeCkoXqRJE3GXzRLkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVI3p0IhyRFJfpBkTZJTpt2PJM03cyYUkmwH/BNwJLA/8LYk+0+3K0maX+ZMKAAHA2uq6paq+inwBeDoKfckSfPKgmk3MGYRcPvY8h3AKzaelGQFsKItPpLkB7PQ23ywO3DPtJuYK3L6tDvQDPwbHbOFf6Mv2dSKuRQKE6mqlcDKafexrUmyuqqWTrsPaVP8G50dc+nw0Vpg77Hlxa0mSZolcykUvgfsl2SfJNsDxwMXTbknSZpX5szho6p6PMmfA98AtgM+WVU3Trmt+cRDcprr/BudBamqafcgSZoj5tLhI0nSlBkKkqTOUJiHknwyybokN4zVjktyY5KfJ/GyP03VTLe8aRehXNlqX2wXpGgrMxTmp3OBIzaq3QC8Gbh81ruRxmzmljenA2dU1b7A/cDy6XW57TIU5qGquhy4b6PazVXlr8M1F2zqljeHARe0OauAY6bT3rbNUJA018x0y5tFwANV9fhGNW1lhoIkqTMUJM01m7rlzc5JFmxU01ZmKEiaazZ1y5vLgGPbnGXAhVPqb5vmL5rnoSSfB17D6FbEdwMfZHTi+WPAQuAB4JqqesOUWtQ8l+Qo4KM8ecubDyf5dUYnnXcFrgbeWVU/mV6X2yZDQZLUefhIktQZCpKkzlCQJHWGgiSpMxQkSZ2hoHktyZLxu8WO1T/RbsJGkg9MsJ2Tk7xwM+v79qS5zEtSNa8lWQJcXFW/vZk5j1TVi55mO7cCS6vqnhnWbVdVT2xpr9JscE9BggVJPpvk5iQXJHlhkm8nWZrkNOAFSa5pc3ZI8tUk1ya5Iclbk/wlsBdwWZLLYBQkST6S5Frg0A3bG1v34baNK5Ls0eovbcvXJzk1ySOtvmeSy1sPNyR59XT+M2k+MBQk+A3gn6vqt4CHgD/bsKKqTgF+XFUHVtU7GD2H4kdVdUDbu/h6VZ0F/Ah4bVW9tn10B+DKNu87G33fDsAVVXUAo+dX/EmrnwmcWVUvY3QX0A3eDnyjqg4EDgCu2Vr/cGljhoIEt1fVf7bxZ4BXbWbu9cDrkpye5NVV9eAm5j0BfHkT634KXNzGVwFL2vhQ4Ett/Lmx+d8DTkzyIeBlVfXwZvqTtoihIMHGJ9Y2eaKtqv4HOIhROJya5G82MfWxzZxH+Fk9eTLvCWDBJuZt+M7Lgd9ndFfQc5OcsLn50pYwFCT4tSSHtvHbgY0P9/wsyXMBkuwFPFpVnwH+nlFAADwM7LiFfVwB/FEbH7+hmOQlwN1V9S/AJ8a+U9rqDAUJfgCclORmYBfg7I3WrwSuS/JZ4GXAd5Ncw+jusqeOzfn6hhPNv6STgfcmuQ7YF9hwaOo1wLVJrgbeyujcgzQIL0mV5oj2O4cfV1UlOR54W1UdPe2+NL9s9limpFn1u8DHk4TRMy3ePd12NB+5pyBJ6jynIEnqDAVJUmcoSJI6Q0GS1BkKkqTu/wGjLR6JwTGN/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot using result\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": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: QUEUED\n"
     ]
    }
   ],
   "source": [
    "# set up device\n",
    "rigetti = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-2\")\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, s3_folder,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": 15,
   "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": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: QUEUED\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, s3_folder, 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of (reconstructed) task: QUEUED\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status of task: QUEUED\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,s3_folder, 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": 19,
   "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/ionq/ionQdevice.\n",
      "\n",
      "Measurement counts: Counter({'00': 517, '11': 472, '01': 7, '10': 4})\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recover task\n",
    "task_load = AwsQuantumTask(arn=\"arn:aws:braket:us-east-1:592242689881:quantum-task/a197f55f-5d91-4e57-8ee7-7a839e1e021e\")\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",
    "    results = task_load.result()\n",
    "    # print(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 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('AWS_QC_results.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. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [Braket]",
   "language": "python",
   "name": "python3_aws_braket_kwx6dl"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}