quantum_circuit_pennylane.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ericyoc/quantum-circuits/blob/main/quantum_circuit_pennylane.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5dd1767b-a9b4-44f6-a98f-450c18672756",
      "metadata": {
        "id": "5dd1767b-a9b4-44f6-a98f-450c18672756"
      },
      "source": [
        "PennyLane (Amazon Braket, Xanadu)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "597a7246-9dc6-4802-b9e8-42ff0177bedd",
      "metadata": {
        "id": "597a7246-9dc6-4802-b9e8-42ff0177bedd"
      },
      "outputs": [],
      "source": [
        "#!pip install pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.templates import StronglyEntanglingLayers\n",
        "from tensorflow.keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "184ab241-8b60-4dcf-890f-5c520441ee9b",
      "metadata": {
        "id": "184ab241-8b60-4dcf-890f-5c520441ee9b"
      },
      "outputs": [],
      "source": [
        "def create_pennylane_circuit():\n",
        "    # Load MNIST dataset\n",
        "    (train_data, train_labels), (test_data, test_labels) = mnist.load_data()\n",
        "\n",
        "    # Define the quantum device\n",
        "    dev = qml.device('default.qubit', wires=4)\n",
        "\n",
        "    # Quantum circuit for image classification\n",
        "    @qml.qnode(dev)\n",
        "    def circuit(inputs, weights):\n",
        "        # Flatten the input image and scale it to the range [-pi/2, pi/2]\n",
        "        flat_inputs = np.pi * inputs.flatten() / 255.0 - np.pi / 2\n",
        "\n",
        "        # Encode the flattened inputs into the quantum circuit\n",
        "        for i in range(4):\n",
        "            qml.RX(flat_inputs[i], wires=i)\n",
        "\n",
        "        # Apply the StronglyEntanglingLayers template\n",
        "        StronglyEntanglingLayers(weights, wires=range(4))\n",
        "\n",
        "        return [qml.expval(qml.PauliZ(i)) for i in range(4)]\n",
        "\n",
        "    # Define the variational classifier\n",
        "    def variational_classifier(weights, bias):\n",
        "        def classifier(x):\n",
        "            outputs = circuit(x, weights)\n",
        "            return np.dot(outputs, weights) + bias\n",
        "        return classifier\n",
        "\n",
        "    # Initialize the weights and bias\n",
        "    num_layers = 2\n",
        "    num_qubits = 4\n",
        "    weights = np.random.normal(loc=0, scale=1, size=(num_layers, num_qubits, 3))\n",
        "    bias = np.random.normal(loc=0, scale=1, size=(1,))\n",
        "\n",
        "    # Create the variational classifier\n",
        "    classifier = variational_classifier(weights, bias)\n",
        "\n",
        "    # Predict the digit for a test image\n",
        "    test_image = test_data[0]\n",
        "    probabilities = classifier(test_image)\n",
        "    predicted_digit = np.round(probabilities).astype(int)\n",
        "\n",
        "    print(f\"Predicted digit: {predicted_digit}\")\n",
        "\n",
        "    # Visualize the circuit\n",
        "    fig, ax = qml.draw_mpl(circuit)(test_image, weights)\n",
        "    fig.savefig('pennylane_circuit.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f7c95b70-759a-4d70-95ff-c03b5dc0bde3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "f7c95b70-759a-4d70-95ff-c03b5dc0bde3",
        "outputId": "0006f8e9-1b0b-4022-a6f7-6467f276d679"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "Predicted digit: [[1 1 1]\n",
            " [0 0 1]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Call the function to create and visualize the circuit\n",
        "create_pennylane_circuit()"
      ]
    }
  ],
  "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": "3.7.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}