{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coding with Qiskit Episode 3\n",
    "\n",
    "# Hello World!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this episode of Coding with Qiskit, we built our first Hello World application.\n",
    "\n",
    "The application first entangled two qubits which were initially in the state $\\vert00\\rangle$. Then, we noted the results by running the application on a simulator as well as real quantum hardware at IBM.\n",
    "\n",
    "This Jupyter notebook shows the code that was used in the video, as well as updates in the latest version of Qiskit.\n",
    "\n",
    "**Link to Episode 3:** https://www.youtube.com/watch?v=RrUTwq5jKM4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"960.0\"\n",
       "            height=\"540.0\"\n",
       "            src=\"http://www.youtube.com/embed/RrUTwq5jKM4\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x109c62eb8>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(src=\"http://www.youtube.com/embed/RrUTwq5jKM4\", width=1920/2, height=1080/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by importing Qiskit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the video, we initialized two qubits into a `QuantumRegister` and two classical bits into a `ClassicalRegister` by writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "qr = QuantumRegister(2)\n",
    "cr = ClassicalRegister(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we built a `QuantumCircuit` composed of the two classical and quantum bits by saying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(qr, cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above three lines of code can be simplified into one line by writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where the first argument is the number of quantum bits, and the second argument is the number of classical bits. This is the recommended way of creating circuits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you create your circuit, you can draw it at any point by writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        \n",
       "q_0: |0>\n",
       "        \n",
       "q_1: |0>\n",
       "        \n",
       " c_0: 0 \n",
       "        \n",
       " c_1: 0 \n",
       "        </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x12c414e80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a plot based on `matplotlib`, use instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 156.52x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you only need to run the `%matplotlib inline` command once in your notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's add the gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 397.32x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the quantum circuit has two qubits. they are indexed as qubits 0 and 1\n",
    "circuit.h(0)\n",
    "circuit.cx(0,1) # order is control, target\n",
    "circuit.measure([0,1], [0,1]) # qubits [0,1] are measured and results are stored in classical bits [0,1] in order\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on a simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by setting the simulator to `qasm_simulator` from the Aer element of Qiskit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we execute the circuit on the simulator and store the results in a variable called `result`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = execute(circuit, backend=simulator).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we are ready to plot the results of the simulator execution. To do this, first import the visualization tools from Qiskit using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we plot the results using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on real quantum hardware"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to run the circuit on real quantum hardware at IBM, we begin by loading our saved IBMQ account using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub = 'ibm-q')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we choose the backend that we want to use in our run. In the video, we used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "qcomp = provider.get_backend('ibmq_16_melbourne')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, if you have no preference on which backend you'd like to use, you can just ask for the least busy backend by using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibmq_16_melbourne\n"
     ]
    }
   ],
   "source": [
    "num_qubits = 2\n",
    "\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "possible_devices = provider.backends(filters=lambda x: \n",
    "                                     x.configuration().n_qubits >= num_qubits\n",
    "                                       and \n",
    "                                     x.configuration().simulator == False)\n",
    "qcomp = least_busy(possible_devices)\n",
    "print(qcomp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the backend is selected, we are ready to run the circuit just as before using the `execute` function. Before doing so, a useful feature is to run the job watcher that is available in the latest version of Qiskit using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_job_watcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = execute(circuit, backend=qcomp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also monitor the job progress inline using the job monitor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the job runs sucessfully, we can visualize the results using `plot_histogram` as before using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = job.result()\n",
    "plot_histogram(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can close the job watcher window by using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qiskit_disable_job_watcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For detailed information about Qiskit, check out the tutorials in the Github repository:\n",
    "\n",
    "https://github.com/Qiskit/qiskit-iqx-tutorials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good to note the Qiskit version that was used in your work. One way to do this is print the version at the bottom of the Jupyter notebook using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qiskit-terra': '0.9.0',\n",
       " 'qiskit-ignis': '0.2.0',\n",
       " 'qiskit-aqua': '0.6.0',\n",
       " 'qiskit': '0.12.0',\n",
       " 'qiskit-aer': '0.3.0',\n",
       " 'qiskit-ibmq-provider': '0.3.2'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qiskit.__qiskit_version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%qiskit_copyright"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}