sampler_lab_exercise_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6196753d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Sampler lab exercise solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de54c99-ae01-416b-a826-2192e8a68c89",
   "metadata": {},
   "source": [
    "This program samples one piece of candy from a box that contains two chocolate candies. The circuit that chooses the piece of candy should be the ψ− Bell state, also known as the singlet Bell State."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3710db96",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 1. Initialize the account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dac21228",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
    "backend = service.backend(\"ibmq_qasm_simulator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00356482",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 2. Create a circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "820a1bc7",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 538.128x284.278 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from numpy import pi\n",
    "\n",
    "qreg_q = QuantumRegister(2, 'q')\n",
    "creg_c = ClassicalRegister(2, 'c')\n",
    "circuit = QuantumCircuit(qreg_q, creg_c)\n",
    "\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.cx(qreg_q[0], qreg_q[1])\n",
    "circuit.z(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.measure(qreg_q[0], creg_c[0])\n",
    "circuit.measure(qreg_q[1], creg_c[1])\n",
    "\n",
    "display(circuit.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fc88d3",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 3. Initialize the Qiskit Runtime Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2756a944",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import Sampler\n",
    "\n",
    "sampler = Sampler(backend=backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617bbd25",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 4. Invoke the Sampler and get results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b474b4ea",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Job ID: cjqsesq7b8jcak59blo0\n",
      ">>> Job Status: JobStatus.RUNNING\n"
     ]
    }
   ],
   "source": [
    "job = sampler.run(circuit)\n",
    "print(f\">>> Job ID: {job.job_id()}\")\n",
    "print(f\">>> Job Status: {job.status()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19a1b861",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> SamplerResult(quasi_dists=[{1: 0.47725, 2: 0.52275}], metadata=[{'shots': 4000}])\n",
      "  > Quasi-probability distribution: {1: 0.47725, 2: 0.52275}\n",
      "  > Metadata: {'shots': 4000}\n"
     ]
    }
   ],
   "source": [
    "result = job.result()\n",
    "print(f\">>> {result}\")\n",
    "print(f\"  > Quasi-probability distribution: {result.quasi_dists[0]}\")\n",
    "print(f\"  > Metadata: {result.metadata[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e30b5e6-5f92-4895-8ecd-8c6fbefb553f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_histogram\n",
    "plot_histogram(job.result().quasi_dists[0].binary_probabilities())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "565fcbe5-e97e-4f68-889e-fb7374b521e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}