QCNN_elec_ph_clf_task3_Dey.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QCNN_elec_ph_clf_task3_Dey.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sharmita01/gsoc_ml4sc_cern/blob/main/QCNN_elec_ph_clf_task3_Dey.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMVHWFoaeb8B"
      },
      "source": [
        "##QCNN for electron-photon classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aquwcz-0aHqz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3dfc814f-f5d6-4f89-e548-6b5c23424296"
      },
      "source": [
        "!pip install tensorflow==2.3.1"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.3.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/eb/18/374af421dfbe74379a458e58ab40cf46b35c3206ce8e183e28c1c627494d/tensorflow-2.3.1-cp37-cp37m-manylinux2010_x86_64.whl (320.4MB)\n",
            "\u001b[K     |████████████████████████████████| 320.4MB 55kB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (2.10.0)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.3.3)\n",
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            "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (2.4.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.2.0)\n",
            "Collecting tensorflow-estimator<2.4.0,>=2.3.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/ed/5853ec0ae380cba4588eab1524e18ece1583b65f7ae0e97321f5ff9dfd60/tensorflow_estimator-2.3.0-py2.py3-none-any.whl (459kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 45.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (3.12.4)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.12.1)\n",
            "Collecting numpy<1.19.0,>=1.16.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d6/c6/58e517e8b1fb192725cfa23c01c2e60e4e6699314ee9684a1c5f5c9b27e1/numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl (20.1MB)\n",
            "\u001b[K     |████████████████████████████████| 20.1MB 11.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.36.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.15.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.12.0)\n",
            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.1.2)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.6.3)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (3.3.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.1.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (0.4.3)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.3.4)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (2.23.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.28.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (54.2.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.0.1)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.0.4)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (4.7.2)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.7.4.3)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (0.4.8)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorflow-estimator, numpy, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.4.0\n",
            "    Uninstalling tensorflow-estimator-2.4.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.4.0\n",
            "  Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Found existing installation: tensorflow 2.4.1\n",
            "    Uninstalling tensorflow-2.4.1:\n",
            "      Successfully uninstalled tensorflow-2.4.1\n",
            "Successfully installed numpy-1.18.5 tensorflow-2.3.1 tensorflow-estimator-2.3.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Pl5PW-ACO9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaa6a476-2eae-4fab-a770-631c5889bf0f"
      },
      "source": [
        "!pip install tensorflow-quantum"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-quantum\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/53/02/878b2d4e7711f5c7f8dff9ff838e8ed84d218a359154ce06c7c01178a125/tensorflow_quantum-0.4.0-cp37-cp37m-manylinux2010_x86_64.whl (5.9MB)\n",
            "\u001b[K     |████████████████████████████████| 5.9MB 9.6MB/s \n",
            "\u001b[?25hCollecting sympy==1.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4d/a7/25d5d6b3295537ab90bdbcd21e464633fb4a0684dd9a065da404487625bb/sympy-1.5-py2.py3-none-any.whl (5.6MB)\n",
            "\u001b[K     |████████████████████████████████| 5.6MB 22.9MB/s \n",
            "\u001b[?25hCollecting cirq==0.9.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/05/39c24828744b91f658fd1e5d105a9d168da43698cfaec006179c7646c71c/cirq-0.9.1-py3-none-any.whl (1.6MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6MB 49.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy==1.5->tensorflow-quantum) (1.2.1)\n",
            "Requirement already satisfied: requests~=2.18 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (2.23.0)\n",
            "Requirement already satisfied: sortedcontainers~=2.0 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (2.3.0)\n",
            "Requirement already satisfied: google-api-core[grpc]<2.0.0dev,>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (1.26.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (3.7.4.3)\n",
            "Requirement already satisfied: numpy~=1.16 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (1.18.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (1.1.5)\n",
            "Requirement already satisfied: protobuf~=3.12.0 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (3.12.4)\n",
            "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (1.4.1)\n",
            "Collecting freezegun~=0.3.15\n",
            "  Downloading https://files.pythonhosted.org/packages/17/5d/1b9d6d3c7995fff473f35861d674e0113a5f0bd5a72fe0199c3f254665c7/freezegun-0.3.15-py2.py3-none-any.whl\n",
            "Requirement already satisfied: networkx~=2.4 in /usr/local/lib/python3.7/dist-packages (from cirq==0.9.1->tensorflow-quantum) (2.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests~=2.18->cirq==0.9.1->tensorflow-quantum) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests~=2.18->cirq==0.9.1->tensorflow-quantum) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests~=2.18->cirq==0.9.1->tensorflow-quantum) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests~=2.18->cirq==0.9.1->tensorflow-quantum) (2020.12.5)\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (54.2.0)\n",
            "Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (20.9)\n",
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            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (2018.9)\n",
            "Requirement already satisfied: grpcio<2.0dev,>=1.29.0; extra == \"grpc\" in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (1.32.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->cirq==0.9.1->tensorflow-quantum) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->cirq==0.9.1->tensorflow-quantum) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->cirq==0.9.1->tensorflow-quantum) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib~=3.0->cirq==0.9.1->tensorflow-quantum) (2.4.7)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx~=2.4->cirq==0.9.1->tensorflow-quantum) (4.4.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (4.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (4.7.2)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->cirq==0.9.1->tensorflow-quantum) (0.4.8)\n",
            "Installing collected packages: sympy, freezegun, cirq, tensorflow-quantum\n",
            "  Found existing installation: sympy 1.7.1\n",
            "    Uninstalling sympy-1.7.1:\n",
            "      Successfully uninstalled sympy-1.7.1\n",
            "Successfully installed cirq-0.9.1 freezegun-0.3.15 sympy-1.5 tensorflow-quantum-0.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkEIPYuIE2nG"
      },
      "source": [
        "Parts of the notebook are adapted from the examples by tfq"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QytLEAtoejW5"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_quantum as tfq\n",
        "\n",
        "import cirq\n",
        "import sympy\n",
        "import numpy as np\n",
        "\n",
        "# visualization tools\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from cirq.contrib.svg import SVGCircuit"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tuWTlrEcDrj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9a03893-5779-49f2-e5bd-f9bd2dd29d30"
      },
      "source": [
        "#mounting google drive\n",
        "from google.colab import drive\n",
        "import numpy as np\n",
        "drive.mount(\"/content/drive\")\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fobBe9LZc02v"
      },
      "source": [
        "#load data from the drive\n",
        "data = np.load('/content/drive/My Drive/Datasets/electron-photon.npz')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dJEF2O_c8Dz"
      },
      "source": [
        "#inspect data\n",
        "import matplotlib.pyplot as plt\n",
        "train_data = data[\"x_train\"]\n",
        "\n",
        "photon = train_data[np.where(data[\"y_train\"]==0)]\n",
        "electron = train_data[np.where(data[\"y_train\"]==1)]\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUrvTCU1hDgP"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "threshold = 0.0\n",
        "x_train = data['x_train'][..., tf.newaxis]\n",
        "y_train = data['y_train']\n",
        "x_test = data['x_test'][..., tf.newaxis]/np.max(x_train)\n",
        "y_test = data['y_test']\n",
        "resize_shape = (100,4,4,1)\n",
        "x_train_small = np.empty(resize_shape)\n",
        "x_test_small = np.empty(resize_shape)\n",
        "\n",
        "x_train_small_bin = np.empty(resize_shape)\n",
        "x_test_small_bin = np.empty(resize_shape)\n",
        "\n",
        "#reduce data dimension\n",
        "for image in range(x_train.shape[0]):\n",
        "  x_train_small[image] = tf.image.resize_with_crop_or_pad(x_train[image], 4,4).numpy()\n",
        "for image in range(x_test.shape[0]):\n",
        "  x_test_small[image] = tf.image.resize_with_crop_or_pad(x_test[image], 4,4).numpy()\n",
        "\n",
        "x_train_small_scaled = tf.keras.utils.normalize(x_train_small, axis=-1, order=2)\n",
        "x_test_small_scaled =  tf.keras.utils.normalize(x_test_small, axis=-1, order=2)                                                        #x_test_small/np.max(x_train_small)\n",
        "\n",
        "\n",
        "#binary thresholding\n",
        "for image in range(x_train.shape[0]):\n",
        "  x_train_small_bin[image] = np.array(x_train_small[image] > threshold, dtype=np.float32)\n",
        "for image in range(x_test.shape[0]):\n",
        "  x_test_small_bin[image] = np.array(x_test_small[image] > threshold, dtype=np.float32)\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4APgTxvuabW5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "2c67a824-5096-48e2-c025-a63a83d36cf1"
      },
      "source": [
        "plt.imshow(x_train_small[9,:,:,0])\n",
        "\n",
        "print(x_train_small_bin[0,:,:,0])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 1. 1. 0.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeAZWVrtqu5m"
      },
      "source": [
        "The qubits at pixel indices with values that exceed the threshold of 0.0, are rotated through an X  gate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ReAG8JrqZI6"
      },
      "source": [
        "def to_circuit(data_):\n",
        "    \"\"\"Encode truncated classical image into quantum datapoint.\"\"\"\n",
        "    val = np.ndarray.flatten(data_)\n",
        "    qubits = cirq.GridQubit.rect(1, 16)\n",
        "    circuit = cirq.Circuit()\n",
        "    for i, value in enumerate(val):\n",
        "        if value:\n",
        "            circuit.append(cirq.X(qubits[i]))\n",
        "        \n",
        "    return circuit\n",
        "\n",
        "\n",
        "x_train_circ = [to_circuit(x) for x in x_train_small_bin]\n",
        "x_test_circ = [to_circuit(x) for x in x_test_small_bin]\n",
        "\n",
        "x_train_tfcirc = tfq.convert_to_tensor(x_train_circ)\n",
        "x_test_tfcirc = tfq.convert_to_tensor(x_test_circ)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyRA2gBlFqma",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9444709-6ff6-47ad-d2c1-e3fc52fc47fa"
      },
      "source": [
        "print (x_train_tfcirc.shape)\n",
        "print (y_train.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100,)\n",
            "(100,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRNp5Eu-HAD7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "outputId": "a4f27e5d-5c3e-45cb-e74c-b815b99b2637"
      },
      "source": [
        "SVGCircuit(x_test_circ[0])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f17a01a93d0>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J58W9oe3xIox"
      },
      "source": [
        "QCNN layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNRGOqky2exY"
      },
      "source": [
        "def one_qubit_unitary(bit, symbols):\n",
        "    \"\"\"Cirq circuit enacting a rotation of the bloch sphere about the X,\n",
        "    Y and Z axis, that depends on the values in `symbols`.\n",
        "    \"\"\"\n",
        "    return cirq.Circuit(\n",
        "        cirq.X(bit)**symbols[0],\n",
        "        cirq.Y(bit)**symbols[1],\n",
        "        cirq.Z(bit)**symbols[2])\n",
        "\n",
        "\n",
        "def two_qubit_unitary(bits, symbols):\n",
        "    \"\"\"Cirq circuit that creates an arbitrary two qubit unitary.\"\"\"\n",
        "    circuit = cirq.Circuit()\n",
        "    circuit += one_qubit_unitary(bits[0], symbols[0:3])\n",
        "    circuit += one_qubit_unitary(bits[1], symbols[3:6])\n",
        "    circuit += [cirq.ZZ(*bits)**symbols[6]]\n",
        "    circuit += [cirq.YY(*bits)**symbols[7]]\n",
        "    circuit += [cirq.XX(*bits)**symbols[8]]\n",
        "    circuit += one_qubit_unitary(bits[0], symbols[9:12])\n",
        "    circuit += one_qubit_unitary(bits[1], symbols[12:])\n",
        "    return circuit\n",
        "\n",
        "\n",
        "def two_qubit_pool(source_qubit, sink_qubit, symbols):\n",
        "    \"\"\"Cirq circuit for parameterized 'pooling', that\n",
        "    reduce entanglement down from two qubits to just one.\"\"\"\n",
        "    pool_circuit = cirq.Circuit()\n",
        "    sink_basis_selector = one_qubit_unitary(sink_qubit, symbols[0:3])\n",
        "    source_basis_selector = one_qubit_unitary(source_qubit, symbols[3:6])\n",
        "    pool_circuit.append(sink_basis_selector)\n",
        "    pool_circuit.append(source_basis_selector)\n",
        "    pool_circuit.append(cirq.CNOT(control=source_qubit, target=sink_qubit))\n",
        "    pool_circuit.append(sink_basis_selector**-1)\n",
        "    return pool_circuit"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Fa19Lzb3wnR"
      },
      "source": [
        "def quantum_conv_circuit(bits, symbols):\n",
        "    \"\"\"Quantum Convolution Layer.\n",
        "    Return a Cirq circuit with the cascade of `two_qubit_unitary` applied\n",
        "    to all pairs of qubits in `bits`.\n",
        "    \"\"\"\n",
        "    circuit = cirq.Circuit()\n",
        "    for first, second in zip(bits[0::2], bits[1::2]):\n",
        "        circuit += two_qubit_unitary([first, second], symbols)\n",
        "    for first, second in zip(bits[1::2], bits[2::2] + [bits[0]]):\n",
        "        circuit += two_qubit_unitary([first, second], symbols)\n",
        "    return circuit"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3svBAfap4xhP"
      },
      "source": [
        "Quantum pooling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jD3fgcWO4yEU"
      },
      "source": [
        "def quantum_pool_circuit(source_bits, sink_bits, symbols):\n",
        "    \"\"\"layer for a quantum pooling operation.\n",
        "    \"\"\"\n",
        "    circuit = cirq.Circuit()\n",
        "    for source, sink in zip(source_bits, sink_bits):\n",
        "        circuit += two_qubit_pool(source, sink, symbols)\n",
        "    return circuit"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23VcPLT45Lg7"
      },
      "source": [
        "Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzEsY6-n5NR0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "7d8adf64-8f1b-4d0b-9a2e-84b3941bb840"
      },
      "source": [
        "def create_model_circuit(qubits):\n",
        "    \"\"\"sequence of alternating convolution and pooling operators \n",
        "    \"\"\"\n",
        "    model_circuit = cirq.Circuit()\n",
        "    symbols = sympy.symbols('qconv0:84')\n",
        "    \n",
        "    model_circuit += quantum_conv_circuit(qubits, symbols[0:15])\n",
        "    model_circuit += quantum_pool_circuit(qubits[:8], qubits[8:],\n",
        "                                          symbols[15:21])\n",
        "    model_circuit += quantum_conv_circuit(qubits[8:], symbols[21:36])\n",
        "    model_circuit += quantum_pool_circuit(qubits[8:12], qubits[12:],\n",
        "                                          symbols[36:42])\n",
        "    model_circuit += quantum_conv_circuit(qubits[12:], symbols[42:57])\n",
        "    model_circuit += quantum_pool_circuit(qubits[12:14], qubits[14:],\n",
        "                                          symbols[57:63])\n",
        "    model_circuit += quantum_conv_circuit(qubits[14:], symbols[63:78])\n",
        "    model_circuit += quantum_pool_circuit(qubits[14:15], qubits[15:],\n",
        "                                          symbols[78:84])\n",
        "    return model_circuit\n",
        "\n",
        "\n",
        "# qubits and readout operators in Cirq.\n",
        "cluster_state_bits = cirq.GridQubit.rect(1, 16)\n",
        "readout_operators = cirq.Z(cluster_state_bits[-1])\n",
        "\n",
        "input_ = tf.keras.Input(shape=(), dtype=tf.dtypes.string)\n",
        "\n",
        "\n",
        "quantum_model = tfq.layers.PQC(create_model_circuit(cluster_state_bits),\n",
        "                               readout_operators)(input_)\n",
        "\n",
        "qcnn_model = tf.keras.Model(inputs=[input_], outputs=[quantum_model])\n",
        "\n",
        "# visualize model\n",
        "tf.keras.utils.plot_model(qcnn_model,\n",
        "                          show_shapes=True,\n",
        "                          show_layer_names=False,\n",
        "                          dpi=70)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jqTEe5VSbug"
      },
      "source": [
        "Model training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TFkAm1sQZEN"
      },
      "source": [
        "@tf.function\n",
        "def custom_accuracy(y_true, y_pred):\n",
        "    y_true = tf.squeeze(y_true)\n",
        "    y_pred = tf.map_fn(lambda x: 1.0 if x >= 0.0 else 0.0, y_pred)\n",
        "    return tf.keras.backend.mean(tf.keras.backend.equal(y_true, y_pred))\n",
        "\n",
        "\n",
        "qcnn_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                   loss=tf.losses.mse,\n",
        "                   metrics=[custom_accuracy])\n",
        "\n",
        "history = qcnn_model.fit(x=x_train_tfcirc,\n",
        "                         y=y_train,\n",
        "                         batch_size=32,\n",
        "                         epochs=15,\n",
        "                         verbose=1,\n",
        "                         validation_data=(x_test_tfcirc, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Llf1uYV4BO1u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a93a3fff-08fa-4ce8-a25b-6c22e78bee34"
      },
      "source": [
        "#Validation accuracy from the last epoch\n",
        "print (max(qcnn_model.history.history['val_custom_accuracy']))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.578125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tiCJOb5Qzcr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fe12749a-6228-4983-afaa-7c73e62a8c65"
      },
      "source": [
        "plt.plot(history.history['loss'][1:], label='Training')\n",
        "plt.plot(history.history['val_loss'][1:], label='Validation')\n",
        "plt.title('Quantum CNN loss plot for electron-photon classification')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLwrrLPOdhfA"
      },
      "source": [
        "Hybrid quantum-classical networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Y1viuyzndpsn",
        "outputId": "7f5089e1-f8f6-4bf4-ebc5-b18f059a1775"
      },
      "source": [
        "\n",
        "\n",
        "def multi_readout_model_circuit(qubits):\n",
        "    model_circuit = cirq.Circuit()\n",
        "    symbols = sympy.symbols('qconv0:21')\n",
        "    model_circuit += quantum_conv_circuit(qubits, symbols[0:15])\n",
        "    model_circuit += quantum_pool_circuit(qubits[:8], qubits[8:],\n",
        "                                          symbols[15:21])\n",
        "    return model_circuit\n",
        "\n",
        "cluster_state_bits = cirq.GridQubit.rect(1, 16)\n",
        "readout_operators = [cirq.Z(bit) for bit in cluster_state_bits[8:]]\n",
        "\n",
        "input_hybrid = tf.keras.Input(shape=(), dtype=tf.dtypes.string)\n",
        "\n",
        "#a single quantum convolution layer\n",
        "\n",
        "quantum_model_hybrid = tfq.layers.PQC(\n",
        "    multi_readout_model_circuit(cluster_state_bits),\n",
        "    readout_operators)(input_hybrid)\n",
        "\n",
        "# classical dense layer 1\n",
        "d1_ = tf.keras.layers.Dense(4)(quantum_model_hybrid)\n",
        "\n",
        "# classical dense layer 2\n",
        "\n",
        "d3_ = tf.keras.layers.Dense(1)(d1_)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "hybrid_model = tf.keras.Model(inputs=[input_hybrid], outputs=[d3_])\n",
        "\n",
        "# model structure\n",
        "tf.keras.utils.plot_model(hybrid_model,\n",
        "                          show_shapes=True,\n",
        "                          show_layer_names=False,\n",
        "                          dpi=70)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4o6fXyiiA04"
      },
      "source": [
        "@tf.function\n",
        "def custom_accuracy(y_true, y_pred):\n",
        "    y_true = tf.squeeze(y_true)\n",
        "    y_pred = tf.map_fn(lambda x: 1.0 if x >= 0.0 else 0.0, y_pred)\n",
        "    return tf.keras.backend.mean(tf.keras.backend.equal(y_true, y_pred))\n",
        "\n",
        "\n",
        "hybrid_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                   loss=tf.losses.mse,\n",
        "                   metrics=[custom_accuracy])\n",
        "\n",
        "history = hybrid_model.fit(x=x_train_tfcirc,\n",
        "                         y=y_train,\n",
        "                         batch_size=32,\n",
        "                         epochs=30,\n",
        "                         verbose=1,\n",
        "                         validation_data=(x_test_tfcirc, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "J6SUUCBwLKR_",
        "outputId": "ccd5fcfa-d580-43c2-b200-c6b5f182cb40"
      },
      "source": [
        "plt.plot(history.history['loss'][1:], label='Training')\n",
        "plt.plot(history.history['val_loss'][1:], label='Validation')\n",
        "plt.title('Hybrid Quantum-classical CNN loss plot for electron-photon classification')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}