IBM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🌐 IBM\n",
    "\n",
    "We can now execute teleportation inside a chip of a real quantum computer thanks to IBM Quantum Experience. An account is neede d in order to perform it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📚 Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Librerie necessarie\n",
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer, IBMQ\n",
    "from qiskit.visualization import plot_histogram, plot_bloch_multivector\n",
    "from qiskit.extensions import Initialize\n",
    "from qiskit_textbook.tools import random_state, array_to_latex\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔧 Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni per la creazione del circuito\n",
    "\n",
    "def bell_pair(circuit, a, b):\n",
    "    # Crea una coppia di Bell nel circuito usando i qubit 'a' e 'b'\n",
    "    circuit.h(a)\n",
    "    circuit.cx(a, b)\n",
    "    \n",
    "def Alice_side(circuit, c, a):\n",
    "    # Operazioni che compie Alice, dove il qubit 'c' contiene lo stato da teletrasportare\n",
    "    circuit.barrier()\n",
    "    circuit.cx(c, a)\n",
    "    circuit.h(c)\n",
    "    \n",
    "def Bob_side(circuit, c, a, b):\n",
    "    circuit.barrier()\n",
    "    # Modifica della funzione originaria poiché i quantum computer IBM non supportano istruzioni dopo misure\n",
    "    circuit.cz(c, b)\n",
    "    circuit.cx(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ❓ Random State Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creazione di uno stato iniziale casuale\n",
    "# Si utilizza lo stesso metodo di QASM\n",
    "\n",
    "psi = random_state(1)\n",
    "init_gate = Initialize(psi) # Porta dallo stato |0> allo stato |psi>\n",
    "init_gate.label = \"init\"\n",
    "inverse_init_gate = init_gate.gates_to_uncompute() # Porta dallo stato |psi> allo stato |0>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🧩 Circuit Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1502.59x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creazione del circuito\n",
    "\n",
    "circuit = QuantumCircuit(3, 1) # 3 qubit e 1 bit\n",
    "\n",
    "# Inizializzazione del qubit C da teletrasportare\n",
    "circuit.append(init_gate, [0])\n",
    "circuit.barrier()\n",
    "\n",
    "# Passo 1: creazione della coppia di Bell\n",
    "bell_pair(circuit, 1, 2)\n",
    "\n",
    "# Passo 2: Alice agisce sui suoi qubit\n",
    "Alice_side(circuit, 0, 1)\n",
    "\n",
    "# Passo 3: Bob agisce sul suo qubit in base al risultato della misura di Alice\n",
    "Bob_side(circuit, 0, 1 ,2)\n",
    "\n",
    "# Passo 4: inversione del processo di inizializzazione e misura del valore\n",
    "circuit.append(inverse_init_gate, [2])\n",
    "circuit.measure(2,0)\n",
    "\n",
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🌐 Login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2020-07-29 14:35:12,651: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5f216d077fa4e80019d6037b\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "# Esecuzione in un quantum computer della IBM\n",
    "\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub = 'ibm-q')\n",
    "backend = provider.get_backend('ibmq_london')\n",
    "job = execute(circuit, backend = backend, shots = 8192)\n",
    "print(job.job_id())\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ✅ Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 7046, '1': 1146}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizzazione dei risultati\n",
    "\n",
    "result = job.result()\n",
    "measurement_result = result.get_counts(circuit)\n",
    "print(measurement_result)\n",
    "plot_histogram(measurement_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L'errore sperimentale è del:  13.9892578125 %\n"
     ]
    }
   ],
   "source": [
    "# Visualizzazione dell'errore percentuale\n",
    "\n",
    "error_rate_percent = sum([measurement_result[result] for result in measurement_result.keys() if result[0]=='1']) \\\n",
    "                    * 100./ sum(list(measurement_result.values()))\n",
    "print(\"L'errore sperimentale è del: \", error_rate_percent, \"%\")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}