QML_HEP_GSoC_2021_Task_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QML-HEP GSoC 2021 Task 3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN/XT2kA/Hk2UT66Vu6/+T2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ipsit1234/QML-HEP-Evaluation-Test-GSOC-2021/blob/main/QML_HEP_GSoC_2021_Task_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rz79RPDqc5_"
      },
      "source": [
        "# Task III: Quantum Convolutional Neural Network (QCNN) Part\n",
        "Your task is to setup and apply a quantum convolutional neural network (QCNN) on\n",
        "particle physics data to perform binary classification on two types of objects\n",
        "(electrons and photons). You should use TFQ for this task.  \n",
        "The electron-photon dataset (which can be found [here](https://github.com/ML4SCI/ML4SCI_GSoC/tree/main/QMLHEP/qcnn)) contains 100 samples for\n",
        "training and another 100 for testing, laid out as follows:  \n",
        "● data[\"x_train\"]: Training dataset of 100 32x32 images containing the particles'\n",
        "energy (100, 32, 32)  \n",
        "● data[\"y_train\"]:\" Training labels, 0 = \"photon\", 1 = \"electron\" (100,)  \n",
        "● data[\"x_test\"]: Test dataset of 100 32x32 images containing the particles'\n",
        "energy (100, 32, 32)  \n",
        "● data[\"y_test\"]:\" Test labels, 0 = \"photon\", 1 = \"electron\" (100,)  \n",
        "\n",
        "The dataset labels are labelled 0 for photons and 1 for electrons. Your task is to\n",
        "implement a QCNN model in Tensorflow Quantum that uses this dataset’s input and\n",
        "performs binary classification. Please feel free to experiment with different ways of\n",
        "encoding the classical data inputs into the qubits.  \n",
        "Specifically, show that the model fits the dataset and that your training loss\n",
        "decreases over time. (Given the small dataset size, we will not be focusing on the\n",
        "accuracy of your model)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbTVN2iJv8_v"
      },
      "source": [
        "## Downloading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCzKRrXhWAxo",
        "outputId": "9877f48f-0779-4157-a4b5-0e190fc87285"
      },
      "source": [
        "!wget https://github.com/ML4SCI/ML4SCI_GSoC/blob/main/QMLHEP/qcnn/electron-photon.npz?raw=true -O electron-photon.npz"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-19 15:06:37--  https://github.com/ML4SCI/ML4SCI_GSoC/blob/main/QMLHEP/qcnn/electron-photon.npz?raw=true\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/ML4SCI/ML4SCI_GSoC/raw/main/QMLHEP/qcnn/electron-photon.npz [following]\n",
            "--2021-03-19 15:06:38--  https://github.com/ML4SCI/ML4SCI_GSoC/raw/main/QMLHEP/qcnn/electron-photon.npz\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/ML4SCI/ML4SCI_GSoC/main/QMLHEP/qcnn/electron-photon.npz [following]\n",
            "--2021-03-19 15:06:38--  https://raw.githubusercontent.com/ML4SCI/ML4SCI_GSoC/main/QMLHEP/qcnn/electron-photon.npz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 821002 (802K) [application/octet-stream]\n",
            "Saving to: ‘electron-photon.npz’\n",
            "\n",
            "electron-photon.npz 100%[===================>] 801.76K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-03-19 15:06:38 (56.4 MB/s) - ‘electron-photon.npz’ saved [821002/821002]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpIQB7oBwBej"
      },
      "source": [
        "## Setting up the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRqW0J-mWS0b",
        "outputId": "af3ca7f3-ff2b-4df4-cc6e-efdf05888eba"
      },
      "source": [
        "!pip install -q tensorflow==2.3.1\n",
        "!pip install -q tensorflow-quantum\n",
        "import tensorflow as tf\n",
        "import tensorflow_quantum as tfq\n",
        "\n",
        "import cirq\n",
        "import sympy\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from cirq.contrib.svg import SVGCircuit"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 320.4MB 51kB/s \n",
            "\u001b[K     |████████████████████████████████| 460kB 20.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 20.1MB 102kB/s \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",
            "\u001b[K     |████████████████████████████████| 5.9MB 9.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.6MB 29.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6MB 51.3MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwPmj87GwFA9"
      },
      "source": [
        "## Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJfIORsFWrzV"
      },
      "source": [
        "with np.load('./electron-photon.npz') as data:\n",
        "  x_train = data['x_train']\n",
        "  y_train = data['y_train']\n",
        "  x_test = data['x_test']\n",
        "  y_test = data['y_test']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Qpq-CySMWtPx",
        "outputId": "f7f75226-693c-4a66-9922-1be3ec50bbe2"
      },
      "source": [
        "# plot a sample each from the training set\n",
        "print(y_train[0])\n",
        "\n",
        "plt.imshow(x_train[0])\n",
        "plt.colorbar()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fb973b615d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdVbm-akwKbg"
      },
      "source": [
        "## Downscaling the images\n",
        "An image size of 32x32 is much too large for current quantum computers. We will use PCA to downscale the images to have a feature dimension of 16 i.e., almost 99% reduction in size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbSJAe9xWxVq"
      },
      "source": [
        "def truncate_x(x_train, x_test, n_components=10):\n",
        "  \"\"\"Performs PCA on image dataset keeping the top `n_components` components.\"\"\"\n",
        "  n_points_train = tf.gather(tf.shape(x_train), 0)\n",
        "  n_points_test = tf.gather(tf.shape(x_test), 0)\n",
        "\n",
        "  # Flatten to 1D\n",
        "  x_train = tf.reshape(x_train, [n_points_train, -1])\n",
        "  x_test = tf.reshape(x_test, [n_points_test, -1])\n",
        "\n",
        "  # Normalize\n",
        "  feature_mean = tf.reduce_mean(x_train, axis=0)\n",
        "  x_train_normalized = x_train - feature_mean\n",
        "  x_test_normalized = x_test - feature_mean\n",
        "\n",
        "  # Truncate\n",
        "  eigen_vals, eigen_vectors = tf.linalg.eigh(\n",
        "      tf.einsum('ji, jk->ik', x_train_normalized, x_train_normalized))\n",
        "  return tf.einsum('ij, jk->ik', x_train_normalized, eigen_vectors[:, -n_components:]), \\\n",
        "  tf.einsum('ij, jk->ik', x_test_normalized, eigen_vectors[:, -n_components:])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Txl9_XGWzPT",
        "outputId": "de4f227e-6d2b-4cbe-b770-093d5de5317e"
      },
      "source": [
        "FEATURE_DIM = 16\n",
        "x_train_small, x_test_small = truncate_x(x_train, x_test, FEATURE_DIM)\n",
        "print('New Datapoint Dimension:', len(x_train_small[0]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New Datapoint Dimension: 16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FfzyJNg3W027",
        "outputId": "50e2a0b8-0955-455c-80d2-c2af355d1b98"
      },
      "source": [
        "print(y_train[0])\n",
        "\n",
        "print(x_train_small[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "tf.Tensor(\n",
            "[-1.6053880e-03  2.3733489e-02  1.4729184e-02 -5.9277429e-03\n",
            "  3.2285508e-05  2.0039830e-02 -5.3670205e-02 -1.6176773e-02\n",
            "  1.2461127e-02  3.0327177e-02  1.7056542e-02  7.0775233e-02\n",
            "  3.8512979e-02 -1.3234577e-01  3.6286876e-02 -3.3949080e-01], shape=(16,), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vC3M5z1kw2ih"
      },
      "source": [
        "## Encoding the data as quantum circuits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51puxRioW5iY"
      },
      "source": [
        "THRESHOLD = 0\n",
        "\n",
        "x_train_bin = np.array(x_train_small > THRESHOLD, dtype=np.float32)\n",
        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUZ7y78pxNNQ"
      },
      "source": [
        "The qubits at pixel indices with values that exceed a threshold, are rotated through an  $X$  gate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgtoY1XpW9Ub"
      },
      "source": [
        "def convert_to_circuit(image):\n",
        "  \"\"\"Encode truncated classical image into quantum datapoint\"\"\"\n",
        "  values = np.ndarray.flatten(image)\n",
        "  qubits = cirq.GridQubit.rect(1, 16) # LineQubit will give an error when converting to tensors\n",
        "  circuit = cirq.Circuit()\n",
        "  for i, value in enumerate(values):\n",
        "    if value:\n",
        "      circuit.append(cirq.X(qubits[i]))\n",
        "  \n",
        "  return circuit\n",
        "\n",
        "x_train_circ = [convert_to_circuit(x) for x in x_train_bin]\n",
        "x_test_circ = [convert_to_circuit(x) for x in x_test_bin]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "PfJ9tMahW-zs",
        "outputId": "b321b46f-960d-4f3a-dcb0-6c49632c9f34"
      },
      "source": [
        "# Visualizing a sample quantum datapoint circuit\n",
        "SVGCircuit(x_train_circ[0])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
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              "<cirq.contrib.svg.svg.SVGCircuit at 0x7fb96005ec10>"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhzke2N-XA3k",
        "outputId": "a873a4ac-6735-4a7b-d83d-0c8934337d2c"
      },
      "source": [
        "# Verifying the encoding\n",
        "bin_img = x_train_bin[0]\n",
        "indices = np.array(np.where(bin_img)).T\n",
        "indices"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1],\n",
              "       [ 2],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10],\n",
              "       [11],\n",
              "       [12],\n",
              "       [14]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55gEJlclyufH"
      },
      "source": [
        "## Converting these Cirq circuits into tensors for TFQ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87URHBvCXFWQ"
      },
      "source": [
        "x_train_tfcirc = tfq.convert_to_tensor(x_train_circ)\n",
        "x_test_tfcirc = tfq.convert_to_tensor(x_test_circ)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AixYBiAf0NWS"
      },
      "source": [
        "## QCNN Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh9LLanoXIPY"
      },
      "source": [
        "def one_qubit_unitary(bit, symbols):\n",
        "    \"\"\"Make a 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",
        "    \"\"\"Make a 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",
        "    \"\"\"Make a Cirq circuit to do a parameterized 'pooling' operation, which\n",
        "    attempts to 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": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DWBGSYI0v4H"
      },
      "source": [
        "Printing circuit samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "4iPnSSKMXLar",
        "outputId": "112ddfb0-0d21-4ad9-a5b1-30c2dab6c47d"
      },
      "source": [
        "# one qubit unitary\n",
        "SVGCircuit(one_qubit_unitary(cirq.GridQubit(0, 0), sympy.symbols('x0:3')))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7fb96002f1d0>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "sZzMRsQfXM22",
        "outputId": "d6606e7b-802c-45df-d46b-5d8ccf9f55d5"
      },
      "source": [
        "# two qubit unitary\n",
        "SVGCircuit(two_qubit_unitary(cirq.GridQubit.rect(1, 2), sympy.symbols('x0:15')))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "bx1aOdhzXOZ8",
        "outputId": "e0a6a2f8-b13e-41a7-b04c-0d2e5c4b3e8d"
      },
      "source": [
        "# two qubit pool\n",
        "SVGCircuit(two_qubit_pool(*cirq.GridQubit.rect(1, 2), sympy.symbols('x0:6')))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcaybku51Dvd"
      },
      "source": [
        "### Quantum Convolution\n",
        "We define the 1D quantum convolution as the application of a two-qubit parameterized unitary to every pair of adjacent qubits with a stride of one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iptb_eutXSEl"
      },
      "source": [
        "def quantum_conv_circuit(bits, symbols):\n",
        "    \"\"\"Quantum Convolution Layer following the above diagram.\n",
        "    Return a Cirq circuit with the cascade of `two_qubit_unitary` applied\n",
        "    to all pairs of qubits in `bits` as in the diagram above.\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": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "id": "oRz3qq5gXTus",
        "outputId": "941b8df0-6485-4a95-9503-68d90cb8d414"
      },
      "source": [
        "SVGCircuit(\n",
        "    quantum_conv_circuit(cirq.GridQubit.rect(1, 16), sympy.symbols('x0:15')))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3I2gncdg1stz"
      },
      "source": [
        "### Quantum pooling\n",
        "A quantum pooling layer pools from $N$ qubits to $\\frac{N}{2}$ qubits using the two-qubit pool defined above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vofae1OkXYQZ"
      },
      "source": [
        "def quantum_pool_circuit(source_bits, sink_bits, symbols):\n",
        "  \"\"\"A layer that specifies a quantum pooling operation.\n",
        "  A Quantum pool tries to learn to pool the relevant information from two\n",
        "  qubits to 1.\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": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "id": "J2vGyRZrXZt6",
        "outputId": "a8b67e30-da8e-40f4-a685-b17342632e8b"
      },
      "source": [
        "test_bits = cirq.GridQubit.rect(1, 16)\n",
        "SVGCircuit(quantum_pool_circuit(test_bits[:8], test_bits[8:], sympy.symbols('x0:6')))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgB6AxVA2Ds_"
      },
      "source": [
        "## Model Definition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "id": "-xLkyxyo1xY4",
        "outputId": "bf1b6c4c-ed03-40b1-8266-cef435a1e8b6"
      },
      "source": [
        "def create_model_circuit(qubits):\n",
        "    \"\"\"Create sequence of alternating convolution and pooling operators \n",
        "    which gradually shrink over time.\"\"\"\n",
        "    model_circuit = cirq.Circuit()\n",
        "    symbols = sympy.symbols('qconv0:84')\n",
        "    # Cirq uses sympy.Symbols to map learnable variables. TensorFlow Quantum\n",
        "    # scans incoming circuits and replaces these with TensorFlow variables.\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",
        "    # model_circuit += quantum_conv_circuit(qubits[60:], symbols[84:99])\n",
        "    # model_circuit += quantum_pool_circuit(qubits[60:62], qubits[62:],\n",
        "    #                                       symbols[99:105])\n",
        "    # model_circuit += quantum_conv_circuit(qubits[62:], symbols[105:120])\n",
        "    # model_circuit += quantum_pool_circuit(qubits[62:63], qubits[63:],\n",
        "    #                                       symbols[120:126])\n",
        "    return model_circuit\n",
        "\n",
        "# Creating qubits and readout operators in Cirq\n",
        "input_bits = cirq.GridQubit.rect(1, 16)\n",
        "readout_operators = cirq.Z(input_bits[-1])\n",
        "\n",
        "input_tensors = tf.keras.Input(shape=(), dtype=tf.string) # since tfq converts Cirq circuits in tf.string dtype\n",
        "quantum_model = tfq.layers.PQC(create_model_circuit(input_bits), readout_operators)(input_tensors)\n",
        "qcnn_model = tf.keras.Model(inputs=[input_tensors], outputs=[quantum_model])\n",
        "\n",
        "# Show the keras plot of the model\n",
        "tf.keras.utils.plot_model(qcnn_model,\n",
        "                          show_shapes=True,\n",
        "                          show_layer_names=False,\n",
        "                          dpi=70)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1EUbThO_FiJ"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOm5k6_RXh5T",
        "outputId": "09e988d8-f8ff-43ef-947d-3946ba6715d7"
      },
      "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 else 0.0, y_pred)\n",
        "    return tf.keras.backend.mean(tf.keras.backend.equal(y_true, y_pred))\n",
        "\n",
        "qcnn_model.compile(\n",
        "    loss=tf.losses.mse,\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "    metrics=[custom_accuracy])\n",
        "\n",
        "print(qcnn_model.summary())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None,)]                 0         \n",
            "_________________________________________________________________\n",
            "pqc (PQC)                    (None, 1)                 84        \n",
            "=================================================================\n",
            "Total params: 84\n",
            "Trainable params: 84\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3RE65esXjzx",
        "outputId": "38c473ee-2ddb-453a-ff3d-8709ddaed691"
      },
      "source": [
        "EPOCHS = 100\n",
        "\n",
        "history = qcnn_model.fit(x=x_train_tfcirc,\n",
        "                         y=y_train,\n",
        "                         batch_size=16,\n",
        "                         epochs=EPOCHS,\n",
        "                         verbose=1,\n",
        "                         validation_data=(x_test_tfcirc, y_test))\n",
        "\n",
        "qcnn_results = qcnn_model.evaluate(x_test_tfcirc, y_test)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5508 - custom_accuracy: 0.4821 - val_loss: 0.4601 - val_custom_accuracy: 0.5982\n",
            "Epoch 2/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5459 - custom_accuracy: 0.5268 - val_loss: 0.4600 - val_custom_accuracy: 0.5536\n",
            "Epoch 3/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5433 - custom_accuracy: 0.5893 - val_loss: 0.4601 - val_custom_accuracy: 0.5536\n",
            "Epoch 4/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5405 - custom_accuracy: 0.6071 - val_loss: 0.4604 - val_custom_accuracy: 0.5268\n",
            "Epoch 5/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5381 - custom_accuracy: 0.6250 - val_loss: 0.4608 - val_custom_accuracy: 0.5179\n",
            "Epoch 6/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5363 - custom_accuracy: 0.6071 - val_loss: 0.4610 - val_custom_accuracy: 0.5268\n",
            "Epoch 7/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5346 - custom_accuracy: 0.5893 - val_loss: 0.4611 - val_custom_accuracy: 0.5089\n",
            "Epoch 8/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5324 - custom_accuracy: 0.5804 - val_loss: 0.4611 - val_custom_accuracy: 0.5089\n",
            "Epoch 9/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5310 - custom_accuracy: 0.5536 - val_loss: 0.4609 - val_custom_accuracy: 0.5179\n",
            "Epoch 10/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5296 - custom_accuracy: 0.5536 - val_loss: 0.4605 - val_custom_accuracy: 0.5179\n",
            "Epoch 11/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5279 - custom_accuracy: 0.5625 - val_loss: 0.4600 - val_custom_accuracy: 0.5179\n",
            "Epoch 12/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5266 - custom_accuracy: 0.6696 - val_loss: 0.4592 - val_custom_accuracy: 0.5179\n",
            "Epoch 13/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5253 - custom_accuracy: 0.6161 - val_loss: 0.4589 - val_custom_accuracy: 0.5089\n",
            "Epoch 14/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5239 - custom_accuracy: 0.6696 - val_loss: 0.4586 - val_custom_accuracy: 0.4911\n",
            "Epoch 15/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5233 - custom_accuracy: 0.6429 - val_loss: 0.4584 - val_custom_accuracy: 0.5089\n",
            "Epoch 16/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5214 - custom_accuracy: 0.6607 - val_loss: 0.4577 - val_custom_accuracy: 0.5089\n",
            "Epoch 17/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5201 - custom_accuracy: 0.6696 - val_loss: 0.4572 - val_custom_accuracy: 0.5089\n",
            "Epoch 18/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5188 - custom_accuracy: 0.7321 - val_loss: 0.4574 - val_custom_accuracy: 0.5268\n",
            "Epoch 19/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5165 - custom_accuracy: 0.6786 - val_loss: 0.4568 - val_custom_accuracy: 0.5357\n",
            "Epoch 20/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5146 - custom_accuracy: 0.7321 - val_loss: 0.4555 - val_custom_accuracy: 0.5357\n",
            "Epoch 21/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5126 - custom_accuracy: 0.6964 - val_loss: 0.4544 - val_custom_accuracy: 0.5268\n",
            "Epoch 22/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5104 - custom_accuracy: 0.6696 - val_loss: 0.4536 - val_custom_accuracy: 0.5446\n",
            "Epoch 23/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.5079 - custom_accuracy: 0.6786 - val_loss: 0.4532 - val_custom_accuracy: 0.5357\n",
            "Epoch 24/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5054 - custom_accuracy: 0.6607 - val_loss: 0.4521 - val_custom_accuracy: 0.5357\n",
            "Epoch 25/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5037 - custom_accuracy: 0.6071 - val_loss: 0.4518 - val_custom_accuracy: 0.5357\n",
            "Epoch 26/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.5013 - custom_accuracy: 0.6786 - val_loss: 0.4509 - val_custom_accuracy: 0.5357\n",
            "Epoch 27/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4991 - custom_accuracy: 0.6696 - val_loss: 0.4496 - val_custom_accuracy: 0.5446\n",
            "Epoch 28/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4973 - custom_accuracy: 0.6875 - val_loss: 0.4485 - val_custom_accuracy: 0.5179\n",
            "Epoch 29/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4946 - custom_accuracy: 0.6607 - val_loss: 0.4476 - val_custom_accuracy: 0.5268\n",
            "Epoch 30/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4930 - custom_accuracy: 0.6161 - val_loss: 0.4448 - val_custom_accuracy: 0.5268\n",
            "Epoch 31/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4898 - custom_accuracy: 0.6607 - val_loss: 0.4441 - val_custom_accuracy: 0.5446\n",
            "Epoch 32/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4879 - custom_accuracy: 0.6607 - val_loss: 0.4428 - val_custom_accuracy: 0.5357\n",
            "Epoch 33/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4865 - custom_accuracy: 0.5982 - val_loss: 0.4418 - val_custom_accuracy: 0.5446\n",
            "Epoch 34/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4850 - custom_accuracy: 0.6161 - val_loss: 0.4411 - val_custom_accuracy: 0.5357\n",
            "Epoch 35/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4823 - custom_accuracy: 0.6607 - val_loss: 0.4404 - val_custom_accuracy: 0.5536\n",
            "Epoch 36/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4810 - custom_accuracy: 0.6607 - val_loss: 0.4412 - val_custom_accuracy: 0.5446\n",
            "Epoch 37/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4795 - custom_accuracy: 0.6518 - val_loss: 0.4407 - val_custom_accuracy: 0.5446\n",
            "Epoch 38/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4776 - custom_accuracy: 0.6250 - val_loss: 0.4390 - val_custom_accuracy: 0.5536\n",
            "Epoch 39/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4757 - custom_accuracy: 0.6071 - val_loss: 0.4374 - val_custom_accuracy: 0.5625\n",
            "Epoch 40/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4749 - custom_accuracy: 0.5625 - val_loss: 0.4368 - val_custom_accuracy: 0.5536\n",
            "Epoch 41/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4736 - custom_accuracy: 0.6696 - val_loss: 0.4371 - val_custom_accuracy: 0.5714\n",
            "Epoch 42/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4724 - custom_accuracy: 0.5893 - val_loss: 0.4368 - val_custom_accuracy: 0.5625\n",
            "Epoch 43/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4716 - custom_accuracy: 0.6429 - val_loss: 0.4371 - val_custom_accuracy: 0.5625\n",
            "Epoch 44/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4714 - custom_accuracy: 0.6696 - val_loss: 0.4376 - val_custom_accuracy: 0.5536\n",
            "Epoch 45/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4691 - custom_accuracy: 0.5893 - val_loss: 0.4377 - val_custom_accuracy: 0.5357\n",
            "Epoch 46/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4676 - custom_accuracy: 0.6071 - val_loss: 0.4386 - val_custom_accuracy: 0.5536\n",
            "Epoch 47/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4671 - custom_accuracy: 0.6607 - val_loss: 0.4378 - val_custom_accuracy: 0.5536\n",
            "Epoch 48/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4660 - custom_accuracy: 0.5804 - val_loss: 0.4374 - val_custom_accuracy: 0.5446\n",
            "Epoch 49/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4650 - custom_accuracy: 0.5804 - val_loss: 0.4373 - val_custom_accuracy: 0.5625\n",
            "Epoch 50/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4638 - custom_accuracy: 0.6071 - val_loss: 0.4373 - val_custom_accuracy: 0.5536\n",
            "Epoch 51/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4627 - custom_accuracy: 0.6071 - val_loss: 0.4371 - val_custom_accuracy: 0.5714\n",
            "Epoch 52/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4622 - custom_accuracy: 0.6250 - val_loss: 0.4386 - val_custom_accuracy: 0.5804\n",
            "Epoch 53/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4622 - custom_accuracy: 0.5625 - val_loss: 0.4397 - val_custom_accuracy: 0.5625\n",
            "Epoch 54/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4613 - custom_accuracy: 0.5982 - val_loss: 0.4408 - val_custom_accuracy: 0.5804\n",
            "Epoch 55/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4596 - custom_accuracy: 0.5982 - val_loss: 0.4404 - val_custom_accuracy: 0.5804\n",
            "Epoch 56/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4584 - custom_accuracy: 0.5625 - val_loss: 0.4387 - val_custom_accuracy: 0.5625\n",
            "Epoch 57/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4575 - custom_accuracy: 0.5982 - val_loss: 0.4379 - val_custom_accuracy: 0.5625\n",
            "Epoch 58/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4572 - custom_accuracy: 0.5714 - val_loss: 0.4388 - val_custom_accuracy: 0.5625\n",
            "Epoch 59/100\n",
            "7/7 [==============================] - 19s 3s/step - loss: 0.4554 - custom_accuracy: 0.6071 - val_loss: 0.4396 - val_custom_accuracy: 0.5536\n",
            "Epoch 60/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4551 - custom_accuracy: 0.5893 - val_loss: 0.4400 - val_custom_accuracy: 0.5714\n",
            "Epoch 61/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4550 - custom_accuracy: 0.5714 - val_loss: 0.4401 - val_custom_accuracy: 0.5804\n",
            "Epoch 62/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4525 - custom_accuracy: 0.6339 - val_loss: 0.4400 - val_custom_accuracy: 0.5804\n",
            "Epoch 63/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4528 - custom_accuracy: 0.5625 - val_loss: 0.4395 - val_custom_accuracy: 0.5893\n",
            "Epoch 64/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4509 - custom_accuracy: 0.5625 - val_loss: 0.4406 - val_custom_accuracy: 0.5625\n",
            "Epoch 65/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4500 - custom_accuracy: 0.5804 - val_loss: 0.4427 - val_custom_accuracy: 0.5625\n",
            "Epoch 66/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4492 - custom_accuracy: 0.5982 - val_loss: 0.4440 - val_custom_accuracy: 0.5446\n",
            "Epoch 67/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4492 - custom_accuracy: 0.6607 - val_loss: 0.4443 - val_custom_accuracy: 0.5625\n",
            "Epoch 68/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4486 - custom_accuracy: 0.6250 - val_loss: 0.4435 - val_custom_accuracy: 0.5536\n",
            "Epoch 69/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4461 - custom_accuracy: 0.6071 - val_loss: 0.4441 - val_custom_accuracy: 0.5536\n",
            "Epoch 70/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4458 - custom_accuracy: 0.5982 - val_loss: 0.4442 - val_custom_accuracy: 0.5804\n",
            "Epoch 71/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4454 - custom_accuracy: 0.5982 - val_loss: 0.4431 - val_custom_accuracy: 0.5893\n",
            "Epoch 72/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4441 - custom_accuracy: 0.6250 - val_loss: 0.4431 - val_custom_accuracy: 0.5893\n",
            "Epoch 73/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4454 - custom_accuracy: 0.6250 - val_loss: 0.4456 - val_custom_accuracy: 0.5446\n",
            "Epoch 74/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4437 - custom_accuracy: 0.6250 - val_loss: 0.4442 - val_custom_accuracy: 0.5357\n",
            "Epoch 75/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4424 - custom_accuracy: 0.5893 - val_loss: 0.4450 - val_custom_accuracy: 0.5446\n",
            "Epoch 76/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4417 - custom_accuracy: 0.5268 - val_loss: 0.4451 - val_custom_accuracy: 0.5804\n",
            "Epoch 77/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4402 - custom_accuracy: 0.6071 - val_loss: 0.4459 - val_custom_accuracy: 0.5714\n",
            "Epoch 78/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4388 - custom_accuracy: 0.6250 - val_loss: 0.4455 - val_custom_accuracy: 0.5804\n",
            "Epoch 79/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4393 - custom_accuracy: 0.6339 - val_loss: 0.4457 - val_custom_accuracy: 0.5893\n",
            "Epoch 80/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4378 - custom_accuracy: 0.6429 - val_loss: 0.4450 - val_custom_accuracy: 0.6071\n",
            "Epoch 81/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4396 - custom_accuracy: 0.6518 - val_loss: 0.4441 - val_custom_accuracy: 0.5804\n",
            "Epoch 82/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4366 - custom_accuracy: 0.6339 - val_loss: 0.4443 - val_custom_accuracy: 0.5804\n",
            "Epoch 83/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4363 - custom_accuracy: 0.6161 - val_loss: 0.4450 - val_custom_accuracy: 0.5893\n",
            "Epoch 84/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4373 - custom_accuracy: 0.6429 - val_loss: 0.4451 - val_custom_accuracy: 0.5982\n",
            "Epoch 85/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4374 - custom_accuracy: 0.6339 - val_loss: 0.4449 - val_custom_accuracy: 0.5893\n",
            "Epoch 86/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4365 - custom_accuracy: 0.6518 - val_loss: 0.4454 - val_custom_accuracy: 0.5714\n",
            "Epoch 87/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4360 - custom_accuracy: 0.6339 - val_loss: 0.4460 - val_custom_accuracy: 0.5625\n",
            "Epoch 88/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4342 - custom_accuracy: 0.6875 - val_loss: 0.4465 - val_custom_accuracy: 0.5714\n",
            "Epoch 89/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4341 - custom_accuracy: 0.6607 - val_loss: 0.4455 - val_custom_accuracy: 0.5893\n",
            "Epoch 90/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4342 - custom_accuracy: 0.6607 - val_loss: 0.4458 - val_custom_accuracy: 0.6161\n",
            "Epoch 91/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4331 - custom_accuracy: 0.6607 - val_loss: 0.4449 - val_custom_accuracy: 0.5625\n",
            "Epoch 92/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4325 - custom_accuracy: 0.6607 - val_loss: 0.4437 - val_custom_accuracy: 0.5625\n",
            "Epoch 93/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4324 - custom_accuracy: 0.6518 - val_loss: 0.4441 - val_custom_accuracy: 0.5536\n",
            "Epoch 94/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4320 - custom_accuracy: 0.6518 - val_loss: 0.4462 - val_custom_accuracy: 0.6071\n",
            "Epoch 95/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4318 - custom_accuracy: 0.6518 - val_loss: 0.4464 - val_custom_accuracy: 0.5536\n",
            "Epoch 96/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4308 - custom_accuracy: 0.6250 - val_loss: 0.4473 - val_custom_accuracy: 0.5536\n",
            "Epoch 97/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4312 - custom_accuracy: 0.6429 - val_loss: 0.4490 - val_custom_accuracy: 0.5446\n",
            "Epoch 98/100\n",
            "7/7 [==============================] - 17s 2s/step - loss: 0.4307 - custom_accuracy: 0.6518 - val_loss: 0.4499 - val_custom_accuracy: 0.5446\n",
            "Epoch 99/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4325 - custom_accuracy: 0.6250 - val_loss: 0.4510 - val_custom_accuracy: 0.5536\n",
            "Epoch 100/100\n",
            "7/7 [==============================] - 18s 3s/step - loss: 0.4326 - custom_accuracy: 0.6339 - val_loss: 0.4512 - val_custom_accuracy: 0.5536\n",
            "4/4 [==============================] - 1s 169ms/step - loss: 0.4512 - custom_accuracy: 0.5469\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mp478wSwXlc7",
        "outputId": "f2dba884-a3f1-4b1e-d037-65ef8e121556"
      },
      "source": [
        "plt.plot(history.history['loss'][1:], label='Training')\n",
        "plt.plot(history.history['val_loss'][1:], label='Validation')\n",
        "plt.title('Training a Quantum CNN to Classify Photons from Electrons')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Fqw-JTTsXnAN",
        "outputId": "d4589126-e456-4a0b-a701-2d58cdf326a1"
      },
      "source": [
        "plt.plot(history.history['custom_accuracy'][1:], label='Training')\n",
        "plt.plot(history.history['val_custom_accuracy'][1:], label='Validation')\n",
        "plt.title('Training a Quantum CNN to Classify Photons from Electrons')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "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"
          }
        }
      ]
    }
  ]
}