QMLHEP_Test_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMKICjscjMkKQblUNjOU0W2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/border-b/QMLHEP/blob/main/Task_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) Implement a simple quantum operation with Pennylane**\n",
        "- With 5 qubits \n",
        "- Apply Hadamard operation on every qubit \n",
        "- Apply CNOT operation on (0, 1), (1,2), (2,3), (3,4) \n",
        "- SWAP (0, 4) \n",
        "- Rotate X with pi/2 on any qubit \n",
        "- Plot the circuit "
      ],
      "metadata": {
        "id": "Dqea6V_-xc0A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pennylane --upgrade\n",
        "!pip install pennylane-lightning"
      ],
      "metadata": {
        "id": "y1bUBlI-xllJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "a_1CmNZZxILL",
        "outputId": "7e3b38f2-b191-4984-8d06-b2a19e665c85"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Defining the circuit\n",
        "dev = qml.device('default.qubit', wires=5)\n",
        "@qml.qnode(dev)\n",
        "def circuit(params):\n",
        "    qml.broadcast(qml.Hadamard, wires, pattern=\"single\")\n",
        "    qml.broadcast(qml.CNOT, wires=[0, 1, 2, 3, 4], pattern=\"chain\")\n",
        "    qml.SWAP(wires=[0, 4])\n",
        "    qml.RX(params[0], wires=wires[2])\n",
        "    return qml.expval(qml.PauliZ(wires=wires[0]))\n",
        "\n",
        "# Initializing the parameters\n",
        "params = np.array([np.pi/2])\n",
        "wires = range(5)\n",
        "\n",
        "# Plotting the circuit\n",
        "fig, ax = qml.draw_mpl(circuit, decimals=2)(params)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2) Implement a second circuit with a framework of your choice:**\n",
        "- Apply a Hadmard gate to the first qubit\n",
        "- rotate the second qubit by pi/3 around X\n",
        "- Apply Hadamard gate to the third and fourth qubit\n",
        "- Perform a swap test between the states of the first and second qubit |q1 q2> and the third and fourth qubit |q3 q4>\n"
      ],
      "metadata": {
        "id": "JnAcChk-yEe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using Qiskit for this task\n",
        "\n",
        "!pip install qiskit\n",
        "!pip install pylatexenc"
      ],
      "metadata": {
        "id": "B_k6U8mByGas"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from qiskit import QuantumCircuit, transpile, Aer, execute, QuantumRegister, ClassicalRegister\n",
        "\n",
        "swap_test_circuit = QuantumCircuit(4 + 1, 1) # using one ancilla qubit\n",
        "swap_test_circuit.h(0)\n",
        "swap_test_circuit.rx(np.pi/3, 1)\n",
        "swap_test_circuit.h(2)\n",
        "swap_test_circuit.h(3)\n",
        "\n",
        "swap_test_circuit.h(0)\n",
        "for i in range(2):\n",
        "    swap_test_circuit.cswap(4, i, i+2)\n",
        "swap_test_circuit.h(0)\n",
        "swap_test_circuit.measure([0],[0])\n",
        "\n",
        "swap_test_circuit.draw('mpl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "ZFNYLUbMyPxL",
        "outputId": "2e53dfdc-0eb5-4079-9640-7c3eb7e79232"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 538.33x535.111 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shots = 8192 #Number of shots\n",
        "#Execute the circuit in the qasm_simulator\n",
        "job = execute( swap_test_circuit, Aer.get_backend('qasm_simulator'),shots=shots )\n",
        "counts = job.result().get_counts() #Count the results of the execution\n",
        "if '1' in counts:\n",
        "    m = counts['1']\n",
        "else:\n",
        "    m = 0\n",
        "print(\"Fidelity of the two states |q1 q2> and |q3 q4>: \", 1-2*m/shots)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Q9l_oK10TbW",
        "outputId": "76726e79-7060-49b1-c4ab-13d930a65517"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fidelity of the two states |q1 q2> and |q3 q4>:  0.0048828125\n"
          ]
        }
      ]
    }
  ]
}