community/hello_world/quantum_emoticon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/qiskit-heading.gif\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*Quantum Emoticon*_ \n",
    "\n",
    "The latest version of this notebook is available on https://github.com/QISKit/qiskit-tutorial.\n",
    "\n",
    "For more information about how to use the IBM Q Experience (QX), consult the [tutorials](https://quantumexperience.ng.bluemix.net/qstage/#/tutorial?sectionId=c59b3710b928891a1420190148a72cce&pageIndex=0), or check out the [community](https://quantumexperience.ng.bluemix.net/qstage/#/community).\n",
    "\n",
    "***\n",
    "### Contributors\n",
    "James R. Wootton, University of Basel; \n",
    "Anna Phan, IBM Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This program aims to accomplish what \"Hello World\" does for classical programming.\n",
    "\n",
    "Specifically:\n",
    "* It is simple;\n",
    "* It performs an understandable and relatable task;\n",
    "* It demonstrates simple principles of the programming language;\n",
    "* It shows how to produce and look at an output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything that can be done with bits can be done with qubits. Simply leave a qubit in its initialized value for the state *0*, or use an operation with the effect of a NOT gate (such as *X* or *Y*) to rotate it to a *1*. Each qubit then becomes a bit, allowing us to implement \"Hello, World!\" directly on a quantum computer.\n",
    "\n",
    "In practice, it is not so straightforward. ASCII encoding of \"Hello, World!\" requires over 100 bits, and therefore over 100 qubits. Current quantum devices are not yet large enough for the job.\n",
    "\n",
    "However, two ASCII characters require only 16 (qu)bits. Writing an emoticon like ;) can therefore be done using *ibmqx5*.\n",
    "\n",
    "The string corresponding to ;) is\n",
    "    \n",
    "    ;)  =  '0011101100101001'\n",
    "\n",
    "To prepare this state we can perform an *X* on qubits 0, 3, 5, 8, 9, 11, 12, 13. Here we number the bits from 0 to 15, from right to left.\n",
    "\n",
    "Of course, just writing a bit string in some qubits isn't very quantum. Instead we can prepare a superposition of two different emoticons. We choose ;) and 8), which correspond to the bit strings\n",
    "\n",
    "    8)  =  '0011100000101001'\n",
    "    ;)  =  '0011101100101001'\n",
    "\n",
    "Note that these strings differ only on bits 8 and 9. It is therefore only these on which the superposition must be prepared. The superposition will be of the '00' of 8), and the '11' of ;) , and so will be a standard [Bell state](https://github.com/QISKit/qiskit-tutorial/blob/master/2_quantum_information/superposition_and_entanglement.ipynb).\n",
    "\n",
    "We'll now implement this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:37.858346Z",
     "start_time": "2018-09-25T16:40:37.834896Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import ClassicalRegister, QuantumRegister\n",
    "from qiskit import QuantumCircuit, execute\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit import IBMQ, available_backends, get_backend\n",
    "from qiskit.wrapper.jupyter import *\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:40.239920Z",
     "start_time": "2018-09-25T16:40:40.234444Z"
    }
   },
   "outputs": [],
   "source": [
    "# set up registers and program\n",
    "qr = QuantumRegister(16)\n",
    "cr = ClassicalRegister(16)\n",
    "qc = QuantumCircuit(qr, cr)\n",
    "\n",
    "# rightmost eight (qu)bits have ')' = 00101001\n",
    "qc.x(qr[0])\n",
    "qc.x(qr[3])\n",
    "qc.x(qr[5])\n",
    "\n",
    "# second eight (qu)bits have superposition of\n",
    "# '8' = 00111000\n",
    "# ';' = 00111011\n",
    "# these differ only on the rightmost two bits\n",
    "qc.h(qr[9]) # create superposition on 9\n",
    "qc.cx(qr[9],qr[8]) # spread it to 8 with a CNOT\n",
    "qc.x(qr[11])\n",
    "qc.x(qr[12])\n",
    "qc.x(qr[13])\n",
    "\n",
    "# measure\n",
    "for j in range(16):\n",
    "    qc.measure(qr[j], cr[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first prepare set up the credentials required to access the devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:42.306017Z",
     "start_time": "2018-09-25T16:40:41.379339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.backends.ibmq.ibmqprovider.IBMQProvider at 0xa17936470>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load saved IBMQ accounts\n",
    "IBMQ.load_accounts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now run the circuit on the simulator for 128 shots (so we don't need to wait in the *ibmqx5* queue) and plot the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:53.411141Z",
     "start_time": "2018-09-25T16:40:43.608027Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backend = \"ibmq_qasm_simulator\" \n",
    "shots_sim = 128\n",
    "\n",
    "job_sim = execute(qc, backend, shots=shots_sim)\n",
    "stats_sim = job_sim.result().get_counts()\n",
    "\n",
    "plot_histogram(stats_sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above results in *stats* tell us how many times each bit string was found as a result. To make our emoticon, we convert the bit strings into ASCII characters. We also calculate the fraction of shots for which each result occurred. The most common results are then printed to screen.\n",
    "\n",
    "This gives us a list of characters and probabilities. But that isn't very fun. For something more visual, we use matplotlib to create an image in which the all the characters included in the result are printed on top of each other. The alpha channel of each character is set to the fraction of shots for which that result occurred. For our simulator results, this is approximately equally split between 8) and ;).\n",
    "\n",
    "We will write this transformation and visualization from results to ASCII characters as a function, so we can reuse it later when we run the smiley circuit on the *ibmqx5* device. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:53.585248Z",
     "start_time": "2018-09-25T16:40:53.413341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5390625\t;)\n",
      "0.4609375\t8)\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": [
    "plt.rc('font', family='monospace')\n",
    "\n",
    "def plot_smiley (stats, shots):\n",
    "    for bitString in stats:\n",
    "        char = chr(int( bitString[0:8] ,2)) # get string of the leftmost 8 bits and convert to an ASCII character\n",
    "        char += chr(int( bitString[8:16] ,2)) # do the same for string of rightmost 8 bits, and add it to the previous character\n",
    "        prob = stats[bitString] / shots # fraction of shots for which this result occurred\n",
    "        # create plot with all characters on top of each other with alpha given by how often it turned up in the output\n",
    "        plt.annotate( char, (0.5,0.5), va=\"center\", ha=\"center\", color = (0,0,0, prob ), size = 300)\n",
    "        if (prob>0.05): # list prob and char for the dominant results (occurred for more than 5% of shots)\n",
    "            print(str(prob)+\"\\t\"+char)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "plot_smiley(stats_sim, shots_sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now run the smiley circuit on *ibmqx5*, after checking that the device is online. Given the noise on the device, our smiley won't be as clean as our previous run on the simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:40:58.663189Z",
     "start_time": "2018-09-25T16:40:58.280586Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "774200624d8c4794a8b8439ab3a2187c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<p style='font-size:16px;'>Job Status : job is being initialized </p>\"),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%qiskit_job_status\n",
    "backends = available_backends()\n",
    "backend = get_backend('ibmqx5')\n",
    "\n",
    "shots_device = 1000\n",
    "job_device = execute(qc, backend, shots=shots_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-25T16:57:46.141411Z",
     "start_time": "2018-09-25T16:57:45.093209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.143\t8)\n",
      "0.056\t;!\n",
      "0.148\t;)\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": [
    "stats_device = job_device.result().get_counts()\n",
    "plot_smiley(stats_device, shots_device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there you have it. A program to take one of the world's most advanced devices and made a smiley with it. A quantum smiley!\n",
    "\n",
    "An alternative introduction to this program can be found [here](https://medium.com/@decodoku/making-a-quantum-computer-smile-cee86a6fc1de)."
   ]
  }
 ],
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