qiskit-example.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebook with Qiskit and Python 3 Kernel\n",
    "\n",
    "An example of a Jupyter Notebook with Qiskit and a Python 3 Kernel.\n",
    "\n",
    "## Information\n",
    "\n",
    "The GitHub Codespaces environment has installed all required packages and dependencies.\n",
    "\n",
    "## Setup\n",
    "\n",
    "The following steps are required to setup the Jupyter Notebook with a Python 3 Kernel.\n",
    "\n",
    "1. Select the `Python 3 (ipykernel)` Kernel in the Jupyter Notebook menu `Kernel` $\\rightarrow$ `Jupyter Kernel` $\\rightarrow$ `Python 3 (ipykernel)`.\n",
    "2. Run the Jupyter Notebook."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qiskit Example - Apply Hadamard Gates and Draw Circuit\n",
    "\n",
    "A Qiskit program that applies a Hadamard gate to `n` qubits initialized at $|0\\rangle$, measured, and drawn as a quantum circuit."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Modules\n",
    "\n",
    "Import the Qiskit Python modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import Aer, assemble, transpile\n",
    "from qiskit import QuantumCircuit, QuantumRegister\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Qiskit Program\n",
    "\n",
    "Create a Quantum Circuit with `n` qubits apply a Hadamard gate to each qubit, and measure the qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantum_register = QuantumRegister(3, 'q')\n",
    "quantum_circuit = QuantumCircuit(quantum_register)\n",
    "\n",
    "quantum_circuit.h(quantum_register[0])\n",
    "quantum_circuit.h(quantum_register[1])\n",
    "quantum_circuit.h(quantum_register[2])\n",
    "\n",
    "quantum_circuit.measure_all()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a Quantum Circuit\n",
    "\n",
    "Draw the quantum circuit. Use the `mpl` output format to draw the circuit using Matplotlib. It uses the `style` parameter to set the style to `bw` for black and white. The scale is set to `0.75` to make the circuit smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 435.29x275.917 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantum_circuit.draw(output='mpl', style='bw', scale=0.75)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a Quantum Circuit\n",
    "\n",
    "Use the `plot_histogram` function to plot the quantum circuit. The `plot_histogram` function takes a dictionary of counts and plots a histogram of the counts. The title is 'Hadamard Gate on 3 Qubits\" and the x-axis is 'Count'. The style is set to `color='midnightblue'` to make the bars blue and the `figsize=(9, 6.5)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x650 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_sim = Aer.get_backend('aer_simulator')\n",
    "transpiled_quantum_circuit = transpile(quantum_circuit, aer_sim)\n",
    "\n",
    "quantum_object = assemble(transpiled_quantum_circuit)\n",
    "results = aer_sim.run(quantum_object).result()\n",
    "counts = results.get_counts()\n",
    "\n",
    "plot_histogram(\n",
    "    counts, \n",
    "    title='Hadamard Gate on 3 Qubits', \n",
    "    color='midnightblue', \n",
    "    figsize=(9, 6.5)\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "- [Qiskit Documentation](https://qiskit.org/documentation/)\n",
    "- [Qiskit](https://qiskit.org)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (Qiskit)",
   "language": "python",
   "name": "qiskit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}