cirq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cirq\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ionq-samples/getting-started/blob/main/cirq.ipynb)\n",
    "\n",
    "[Cirq](https://github.com/quantumlib/Cirq) is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. It is designed to be flexible, and is suitable for use in a variety of quantum computing tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# First, we install the essential libraries to our current Python runtime.\n",
    "# \"%%capture\" (above) captures and in this case, hides the output of this \n",
    "# cell, so you can comment it out if you need help debugging this step.\n",
    "\n",
    "%pip install cirq cirq_ionq matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cirq, cirq_ionq, matplotlib.pyplot as plt, os\n",
    "from getpass import getpass\n",
    "\n",
    "# Before you begin, get your API key from https://cloud.ionq.com/settings/keys\n",
    "\n",
    "# If your API key is stored as \"IONQ_API_KEY\" in your local environment, this\n",
    "# should find it. Otherwise you'll be prompted to enter your API key manually.\n",
    "\n",
    "api_key = os.getenv('IONQ_API_KEY') or getpass('Enter your IonQ API key: ')\n",
    "\n",
    "service = cirq_ionq.Service(api_key=api_key, default_target='simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we set up our circuit. In this case, we're creating a circuit with two\n",
    "# qubits, applying an H gate to qubit-0, a CXGate to both, then measuring.\n",
    "\n",
    "q0, q1 = cirq.LineQubit.range(2)\n",
    "circuit = cirq.Circuit(\n",
    "    cirq.H(q0),           # H\n",
    "    cirq.CX(q0, q1),      # CNOT\n",
    "    cirq.measure(q0, q1)  # Measure both qubits\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: ───H───@───M───\n",
      "          │   │\n",
      "1: ───────X───M───\n"
     ]
    }
   ],
   "source": [
    "# Before submitting the job, we can visualize the circuit using print().\n",
    "\n",
    "print(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = service.run(circuit, name=\"Cirq example\", repetitions=100)\n",
    "_ = cirq.plot_state_histogram(result, plt.subplot())\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}