QMLHEP_Test_6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPgfO5olgvLcohIDJaUfxII",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/border-b/QMLHEP/blob/main/QMLHEP_Test_6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qiskit\n",
        "!pip install qiskit-machine-learning\n",
        "!pip install pylatexenc"
      ],
      "metadata": {
        "id": "zqa1-GSRrdpM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Autoencoders are smaller neural networks that can achieve similar results as larger ones in deep learning. They offer the potential to accomplish tasks with fewer neurons in applications such as image classification, music composition, and art generation.\n",
        "\n",
        "With a quantum autoencoder, we can take bigger state vectors and encode them into smaller ones. And the cool part is that this will drastically reduce the amount of resources needed to train the model."
      ],
      "metadata": {
        "id": "SBdA-j-35Arl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "43fL7xM8rSsX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "# Qiskit libraries \n",
        "from qiskit import QuantumCircuit, transpile, Aer, IBMQ, execute, QuantumRegister, ClassicalRegister\n",
        "from qiskit.tools.jupyter import *\n",
        "from qiskit.visualization import *\n",
        "from qiskit.circuit import Parameter, ParameterVector\n",
        "\n",
        "#MNIST set libraries for the acquisition and pre-processing data.\n",
        "import tensorflow as tf\n",
        "\n",
        "#Graph libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading the MNIST set divided by a train set and a test set\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "# Rescale the images from [0,255] to the [0.0,1.0] range.\n",
        "x_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0\n",
        "\n",
        "#Showing the length of the train and test sets\n",
        "print(\"Number of images in the training set:\", len(x_train))\n",
        "print(\"Number of images in the test set:\", len(x_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J69F0XeOrbAM",
        "outputId": "c7ec96ed-ab8d-41ea-9f8f-79e98337da2e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images in the training set: 60000\n",
            "Number of images in the test set: 10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Due the constraint of resources available, we are only working with 0 and 1 digits in the MNIST dataset. This makes it a binary classification problem."
      ],
      "metadata": {
        "id": "DbtJPrDF4tPG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to filter the 0 and 1 labels of the MNIST set\n",
        "'''\n",
        "Input = x_label and y_label sets\n",
        "Output = x_label and y_label sets filtered\n",
        "'''\n",
        "def filter_01(x, y):\n",
        "    keep = (y == 0) | (y == 1)\n",
        "    x, y = x[keep], y[keep]\n",
        "    return x,y"
      ],
      "metadata": {
        "id": "phjfBlovriBa"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = filter_01(x_train, y_train) #Filter the train set\n",
        "x_test, y_test = filter_01(x_test, y_test) #Filter the test set\n",
        "\n",
        "#Showing the length of the train and test sets after filtering the data\n",
        "print(\"Number of images in the training set:\", len(x_train))\n",
        "print(\"Number of images in the test set:\", len(x_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbanGPROrkJp",
        "outputId": "055553c4-1ef0-4eda-acc9-b73def69dd39"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images in the training set: 12665\n",
            "Number of images in the test set: 2115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the first element of the train set\n",
        "plt.imshow(x_train[0, :, :, 0])\n",
        "plt.colorbar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "300NYOx1rnPh",
        "outputId": "a35a8cbe-5d37-4fd4-ee54-09ea3eb0acfa"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fd8b1a64eb0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#resizing the image from 28x28 to 8x8 by the nearest method\n",
        "x_train_small = tf.image.resize(x_train, (8,8), method='nearest', preserve_aspect_ratio=True).numpy()\n",
        "x_test_small = tf.image.resize(x_test, (8,8), method='nearest', preserve_aspect_ratio=True).numpy()"
      ],
      "metadata": {
        "id": "0-NoAttkrrjx"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the first element of the train set after the resizing\n",
        "plt.imshow(x_train_small[0,:,:,0], vmin=0, vmax=1)\n",
        "plt.colorbar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "aj7VKUS6rtp7",
        "outputId": "ee319850-ae1f-46ac-d804-632bfdaeb1b0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fd8aceec760>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshaping the train and test test to a 64x1 matriz\n",
        "x_train = x_train_small.reshape(len(x_train_small), 64)\n",
        "x_test = x_test_small.reshape(len(x_test_small), 64)\n",
        "x_train.shape,x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3d5jhyxgrv1_",
        "outputId": "1b674bf9-6fb7-4d6d-b0d8-1e8f3c4408e5"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12665, 64), (2115, 64))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Showing the first element of the train set\n",
        "x_train_small[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ktv9gScVry05",
        "outputId": "33859f56-1dda-491a-ed66-f26a47b57da0"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.93333333],\n",
              "        [0.92941176],\n",
              "        [0.        ],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.98823529],\n",
              "        [0.99215686],\n",
              "        [0.74117647],\n",
              "        [0.        ],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.69803922],\n",
              "        [0.0745098 ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.76470588],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.98823529],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.58039216],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.88235294],\n",
              "        [0.        ],\n",
              "        [0.44705882],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.98823529],\n",
              "        [0.98823529],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ],\n",
              "        [0.        ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = (x_train)\n",
        "x_test = (x_test)\n",
        "\n",
        "x_train.shape,x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-djwqUQnr8wy",
        "outputId": "1e381480-98cf-4096-961b-35bf490a32f1"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12665, 64), (2115, 64))"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu8xvqRRr-2c",
        "outputId": "aedb8a1a-3814-4de3-dde7-5ad0abd86863"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.93333333, 0.92941176, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.98823529,\n",
              "       0.99215686, 0.74117647, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.69803922, 0.0745098 , 0.        , 0.        ,\n",
              "       0.76470588, 0.        , 0.        , 0.        , 0.98823529,\n",
              "       0.        , 0.        , 0.        , 0.58039216, 0.        ,\n",
              "       0.        , 0.        , 0.88235294, 0.        , 0.44705882,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.98823529, 0.98823529, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Deleting no valuable information for the training set\n",
        "k = 0\n",
        "\n",
        "while k < len(x_train):\n",
        "    a = x_train[k].copy() #make a copy of the actual element\n",
        "    #Verfify if it has some valuable data\n",
        "    if np.sum(a) == 0.: \n",
        "        #If not has valuable data\n",
        "        print(k,x_train[k]) \n",
        "        x_train = np.delete(x_train, k, axis=0) #Delete the actual element from the x_label\n",
        "        y_train = np.delete(y_train, k, axis=0) #Delete the actual element from the y_label\n",
        "        k -= 1 #Take back one value of the counter to match the new set length\n",
        "    k+=1"
      ],
      "metadata": {
        "id": "wFl8ntoQsBC0"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Deleting no valuable information for the test set\n",
        "k = 0\n",
        "\n",
        "while k < len(x_test): #Deleting no valuable information for the training set\n",
        "    a = x_test[k].copy()\n",
        "    #Verfify if it has some valuable data\n",
        "    if np.sum(a) == 0.:\n",
        "        #If not has valuable data\n",
        "        print(k,x_test[k])\n",
        "        x_test = np.delete(x_test, k, axis=0) #Delete the actual element from the x_label\n",
        "        y_test = np.delete(y_test, k, axis=0) #Delete the actual element from the y_label\n",
        "        k -= 1 #Take back one value of the counter to match the new set length\n",
        "    k+=1"
      ],
      "metadata": {
        "id": "zFxmNUbMsDop"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cmath\n",
        "#Funtion to normalize the data of an array\n",
        "'''\n",
        "Input = Array with n values\n",
        "Output = Array with normalized valued\n",
        "'''\n",
        "def Normalize(row):\n",
        "    #We calculate the squareroot of the sum of the square values of the row\n",
        "    suma = np.sqrt(np.sum(row**2)) \n",
        "    if suma == 0.:\n",
        "        #If the sum is zero we return a 0\n",
        "        return 0.0\n",
        "    #Else we divide each value between the sum value above\n",
        "    row = row/suma\n",
        "    return row \n",
        "\n",
        "#Normalize the training set data\n",
        "for i in range(len(x_train)):\n",
        "    x_train[i] = Normalize(x_train[i])\n",
        "\n",
        "#Normalize the test set data\n",
        "for i in range(len(x_test)):\n",
        "    x_test[i] = Normalize(x_test[i])\n",
        "    \n",
        "#Showing the state sum of the training set    \n",
        "print(\"The sum of the states from the training set 0\",np.sum(x_train[0]**2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lvp-OZNgsGfM",
        "outputId": "52499026-81af-4746-d527-6f67a1781f30"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The sum of the states from the training set 0 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n=6 #Number of qubits \n",
        "num_layers = 1 #Number of layers\n",
        "#Making a ndarray of floats based on the number of layers\n",
        "params = np.random.random(10*(num_layers))"
      ],
      "metadata": {
        "id": "Jmfla7OjsJPf"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Showing the normalized values of the first element of the training set\n",
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzj-dmxAsK82",
        "outputId": "a1d0e66d-36b4-4301-ea7d-898162d2e573"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.30173661, 0.30046881, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.31948582,\n",
              "       0.32075362, 0.23961437, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.22566856, 0.02408822, 0.        , 0.        ,\n",
              "       0.24722117, 0.        , 0.        , 0.        , 0.31948582,\n",
              "       0.        , 0.        , 0.        , 0.18763453, 0.        ,\n",
              "       0.        , 0.        , 0.2852552 , 0.        , 0.1445293 ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.31948582, 0.31948582, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to create a quantum circuit based on the number of qubit and a\n",
        "#vector of complex amplitudes to initialize to\n",
        "'''\n",
        "Input: Number of qubits, vector of complex amplitudes\n",
        "Output: Quantum Circuit\n",
        "'''\n",
        "def input_data(n,inputs):\n",
        "    circuit = QuantumCircuit(n,1) #create the quantum circuit with n qubits\n",
        "    #initialization of the circuit with the vector of amplitudes\n",
        "    circuit.initialize(inputs,range(0,n,1)) \n",
        "    circuit.barrier() #Draw a barrier\n",
        "    return circuit\n",
        "\n",
        "#Example of a quantum circuit with the first row of te trainig set\n",
        "input_data(n,x_train[0]).draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "fgq_YhNYsNA5",
        "outputId": "599d7caa-154c-4828-e933-a1538ffcdb81"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 621.941x618.722 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fidelity Check\n",
        "\n",
        "The following function takes two quantum circuits and returns their fidelity."
      ],
      "metadata": {
        "id": "a4RAN7Cz6Ara"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# takes two circuts as input and returns their fidelity as output\n",
        "\n",
        "def compute_fidelity(num_qubits, circuit1, circuit2):\n",
        "    list1, list2 = [], []\n",
        "    for i in range(num_qubits):\n",
        "        list1.append(i + 1)\n",
        "        list2.append(i + num_qubits + 1)\n",
        "    swap_test_circuit = QuantumCircuit(2*num_qubits + 1, 1)\n",
        "\n",
        "    swap_test_circuit.compose(circuit1, qubits=list1, inplace = True)\n",
        "    swap_test_circuit.compose(circuit2, qubits=list2, inplace = True)\n",
        "    swap_test_circuit.h(0)\n",
        "    for i in range(num_qubits):\n",
        "        swap_test_circuit.cswap(0,i+1,i+num_qubits+1)\n",
        "    swap_test_circuit.h(0)\n",
        "    swap_test_circuit.measure([0],[0])\n",
        "    swap_test_circuit.draw('mpl')\n",
        "    shots = 1024 #Number of shots\n",
        "    #Execute the circuit in the qasm_simulator\n",
        "    job = execute( swap_test_circuit, Aer.get_backend('qasm_simulator'),shots=shots )\n",
        "    counts = job.result().get_counts() #Count the results of the execution\n",
        "    if '1' in counts:\n",
        "        m = counts['1']\n",
        "    else:\n",
        "        m = 0\n",
        "    # print(1-2*m/shots)\n",
        "    return (1 - 2*m/shots)"
      ],
      "metadata": {
        "id": "Z-ahZ64FFEzv"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train[0].shape)\n",
        "cc1 = input_data(n, x_train[0])\n",
        "cc2 = input_data(n, x_train[1])\n",
        "\n",
        "print(compute_fidelity(n, cc1, cc1))\n",
        "print(compute_fidelity(n, cc1, cc2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbDcbIqjFF2H",
        "outputId": "1e4816ae-fc3c-4455-b5b8-53cb43a518f2"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64,)\n",
            "1.0\n",
            "-0.00390625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to create a quantum variational circuit\n",
        "'''\n",
        "Input: number of qubits, number of layers, parameters to initialized the circuit\n",
        "Output: Quantum Circuit\n",
        "'''\n",
        "def vqc(n, num_layers,params):\n",
        "    #Set the number of layers and qubits\n",
        "    #ParameterVectors are initialized with a string identifier and an integer specifying the vector length\n",
        "    parameters = ParameterVector('θ', 10*(num_layers))\n",
        "    len_p = len(parameters)\n",
        "    circuit = QuantumCircuit(n, 1) #create the quantum circuit with n qubits\n",
        "    \n",
        "\n",
        "    #Creating the circuit for each layer\n",
        "    for layer in range(num_layers):\n",
        "        #Applying a ry gate in each qubit\n",
        "        for i in range(n):\n",
        "            #the rotation of the ry gate is defined in the parameters list\n",
        "            #based on the layer\n",
        "            circuit.ry(parameters[(layer)+i], i)\n",
        "        circuit.barrier() #Create a barrier\n",
        "\n",
        "        circuit.cx(2,0) #Apply a CNOT gate between the qubit 2 and 0\n",
        "        circuit.cx(3,1) #Apply a CNOT gate between the qubit 3 and 1\n",
        "        circuit.cx(5,4) #Apply a CNOT gate between the qubit 5 and 4\n",
        "        circuit.barrier() #Create a barrier\n",
        "        \n",
        "        #Apply a RY gate in the qubit 0 with the rotation specified in the parameter list\n",
        "        circuit.ry(parameters[6+(layer)],0)\n",
        "        #Apply a RY gate in the qubit 1 with the rotation specified in the parameter list\n",
        "        circuit.ry(parameters[7+(layer)],1)\n",
        "        #Apply a RY gate in the qubit 4 with the rotation specified in the parameter list\n",
        "        circuit.ry(parameters[8+(layer)],4)\n",
        "        circuit.barrier() #Create a barrier\n",
        "        \n",
        "        circuit.cx(4,1) #Apply a CNOT gate between the qubit 4 and 1\n",
        "        circuit.barrier() #Create a barrier\n",
        "        \n",
        "        #Apply a RY gate in the qubit 1 with the rotation specified in the parameter list\n",
        "        circuit.ry(parameters[9+(layer)], 1)\n",
        "        circuit.barrier() #Create a barrier\n",
        "        \n",
        "\n",
        "    #Creating a parameters dictionary\n",
        "    params_dict = {}\n",
        "    i = 0\n",
        "    for p in parameters:\n",
        "        #The name of the value will be the string identifier and an integer specifying the vector length\n",
        "        params_dict[p] = params[i] \n",
        "        i += 1\n",
        "    #Assign parameters using the assign_parameters method\n",
        "    circuit = circuit.assign_parameters(parameters = params_dict)\n",
        "    return circuit"
      ],
      "metadata": {
        "id": "8SLPA1absQUv"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#An example with 6 quibits, one layer and 10 parameters\n",
        "vqc(n,num_layers,params).draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "LFsvV1rlsSTu",
        "outputId": "33f95d11-0e0c-4f8f-cba1-0c14badac8da"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1541.66x618.722 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fucntion to make a swap test\n",
        "'''\n",
        "Input: Number of qubits\n",
        "Output: Quantum circuit\n",
        "'''\n",
        "def swap_test(n):\n",
        "    qubits_values = 2*n+1 #Create a new qubit value to create our circuit\n",
        "    qc = QuantumCircuit(qubits_values) #Create the quantum circuit with the qubits value\n",
        "    qc.h(0) #Applying a H gate to the first qubit\n",
        "    for i in range(n):\n",
        "        #Applying a cswap gate between the first quibit and the i+1 and 2*n-i qubits\n",
        "        qc.cswap(0,i+1,2*n-i) \n",
        "    qc.h(0) #Applying a H gate to the first qubit\n",
        "    qc.barrier() #Create a barrier\n",
        "    return qc\n",
        "#Example of a swap test with 2 quibits\n",
        "swap_test(2).draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "oWn425PBsUVo",
        "outputId": "cbbd72ff-b0c7-461c-f949-1dd82428da71"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 538.33x451.5 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "size_reduce = 2 #Number of qubits we want to reduce\n",
        "circuit_init = input_data(n,x_train[0]) #Create a inicial circuit\n",
        "circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        "circuit_swap_test = swap_test(size_reduce) #Create a swap test circuit\n",
        "\n",
        "#Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "circuit_full = QuantumCircuit(n+size_reduce+1,1)\n",
        "\n",
        "#Combine the initial circuit, the quantum variatinal circuit and the swap test\n",
        "#For the initial circuit and QVC we start at the qubit size_reduce + 1\n",
        "#For the swap test we start at the qubit 0\n",
        "circuit_full = circuit_full.compose(circuit_init,[i for i in range(size_reduce+1,n+size_reduce+1)])\n",
        "circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(size_reduce+1,n+size_reduce+1)])\n",
        "circuit_full = circuit_full.compose(circuit_swap_test,[i for i in range(2*size_reduce+1)])\n",
        "circuit_full.draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "H8BfivwKsWbf",
        "outputId": "9e319077-fad9-426d-bf59-87616469bd75"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2210.55x1789.28 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We generate the inverse of the QVC\n",
        "vqc(n,num_layers,params).inverse().draw(output = \"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "UkaJ44zBsYvu",
        "outputId": "2641e794-763f-4b3c-eaec-cd820ccd6225"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1541.66x618.722 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from random import shuffle\n",
        "from scipy.optimize import minimize "
      ],
      "metadata": {
        "id": "Q9ABVb5_sajj"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to identify a function cost\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Function cost\n",
        "'''\n",
        "def objective_function(params):\n",
        "    costo = 0\n",
        "    shuffle(x_train) #reorganize the order of the train set items\n",
        "    lenght= 5 #We only will consider the first five elements of the taining set\n",
        "    #For each item of the trainig set\n",
        "    for i in range(lenght):\n",
        "\n",
        "        circuit_init = input_data(n,x_train[i])#Create a inicial circuit\n",
        "        circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        "        circuit_swap_test = swap_test(size_reduce) #Create a swap test circuit\n",
        "\n",
        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "        circuit_full = QuantumCircuit(n+size_reduce+1,1)\n",
        "        \n",
        "        #Combine the initial circuit, the quantum variatinal circuit and the swap test\n",
        "        #For the initial circuit and QVC we start at the qubit size_reduce + 1\n",
        "        #For the swap test we start at the qubit 0\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(size_reduce+1,n+size_reduce+1)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(size_reduce+1,n+size_reduce+1)])\n",
        "        circuit_full = circuit_full.compose(circuit_swap_test,[i for i in range(2*size_reduce+1)])\n",
        "        circuit_full.measure(0, 0) #Measure the first qubit\n",
        "        #qc.draw()\n",
        "        shots= 1024 #Number of shots\n",
        "        #Execute the circuit in the qasm_simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('qasm_simulator'),shots=shots )\n",
        "        counts = job.result().get_counts() #Count the results of the execution\n",
        "        probs = {} #Calculate the probabilities of 0 and 1 state\n",
        "        for output in ['0','1']:\n",
        "            if output in counts:\n",
        "                probs[output] = counts[output]/shots #Calculate the average of a state\n",
        "            else:\n",
        "                probs[output] = 0\n",
        "        costo += (1 +probs['1'] -  probs['0']) #Update the actual function cost\n",
        "    \n",
        "    return costo/lenght\n",
        "\n",
        "for i in range(1):\n",
        "    #Minimization of the objective_fucntion by a COBYLA method\n",
        "    minimum = minimize(objective_function, params, method='COBYLA', tol=1e-6)\n",
        "    params = minimum.x #Get the solution array\n",
        "    #Show the cost of the solution array\n",
        "    print(\" cost: \",objective_function(params))\n",
        "    print(params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tt1onG5AseO_",
        "outputId": "ec44ca98-8fdd-4997-9fe6-93f2485da3f9"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " cost:  0.3109375\n",
            "[ 3.44082583  0.54829884  1.42174213  1.9999793   1.60072113  0.46583859\n",
            "  1.08855381  0.09157144 -0.11166155  0.27931699]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to compress the test set values\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with compress values\n",
        "'''\n",
        "def compress_result_test(params):\n",
        "    reduce = [] #List to save the compress values\n",
        "    #For each row in the test set we will\n",
        "    for i in range(len(x_test)):\n",
        "        \n",
        "        circuit_init = input_data(n,x_test[i]) #Create a inicial circuit\n",
        "        circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        " \n",
        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "        circuit_full = QuantumCircuit(n,n-size_reduce)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        len_cf = len(circuit_full) #Known the length of the circuit\n",
        "        #For each n - the the desired qubits to reduce we will\n",
        "        for i in range(n-size_reduce):\n",
        "            circuit_full.measure(size_reduce+i, i) #Measure the circuit in the position size_reduce+i \n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('qasm_simulator'),shots=1024 )\n",
        "        result = job.result().get_counts() #Get the results of the execution\n",
        "        #Get the probabilities of each state\n",
        "        probs = {k: np.sqrt(v / 1024) for k, v in result.items()}\n",
        "        reduce.append(probs) #Save the probabilities\n",
        "        \n",
        "    return reduce\n",
        "\n",
        "#Call the compress_result_test function with the parameters defined above\n",
        "reduce_img =compress_result_test(params)\n",
        "test_reduce = [] #List to save the new values of the image reduction\n",
        "#for each value in the reduce_img list\n",
        "for i in reduce_img:\n",
        "    index_image = [] #List to save the reduction values\n",
        "    #We now take in count we want a 4X4 image\n",
        "    for j in range(16):\n",
        "        bin_index = bin(j)[2:] #We take the binary value of j from the 2 position to the end\n",
        "        while len(bin_index) <4: #While bin_index is less than 4\n",
        "            bin_index = '0'+bin_index #We concatenate a 0 string at the beginnig\n",
        "        try:   \n",
        "            #We try to save the element of the row in the position bin_index\n",
        "            index_image.append(i[bin_index]) \n",
        "        except:\n",
        "            index_image.append(0) #If we can't, we only save a 0\n",
        "    \n",
        "    #We save the new imagen values in the test_recuce list\n",
        "    test_reduce.append(np.array(index_image))"
      ],
      "metadata": {
        "id": "vRmHbVpZshO6"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to compress the training set values\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with compress values\n",
        "'''\n",
        "def compress_result_train(params):\n",
        "    reduce = [] #List to save the compress values\n",
        "    #For each row in the training set we will\n",
        "    for i in range(len(x_train)):\n",
        "        circuit_init = input_data(n,x_train[i]) #Create a inicial circuit\n",
        "        circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        "        \n",
        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "        circuit_full = QuantumCircuit(n,n-size_reduce)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        len_cf = len(circuit_full) #Known the length of the circuit\n",
        "        #For each n - the the desired qubits to reduce we will\n",
        "        for i in range(n-size_reduce):\n",
        "            circuit_full.measure(size_reduce+i, i) #Measure the circuit in the position size_reduce+i \n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('qasm_simulator'),shots=1024 )\n",
        "        result = job.result().get_counts() #Get the results of the execution\n",
        "        #Get the probabilities of each state\n",
        "        probs = {k: np.sqrt(v / 1024) for k, v in result.items()}\n",
        "        reduce.append(probs) #Save the probabilities\n",
        "        \n",
        "    return reduce\n",
        "        \n",
        "#Call the compress_result_train function with the parameters defined above\n",
        "reduce_img =compress_result_train(params)\n",
        "train_reduce = [] #List to save the new values of the image reduction\n",
        "#for each value in the reduce_img list\n",
        "for i in reduce_img:\n",
        "    index_image = [] #List to save the reduction values\n",
        "    #We now take in count we want a 4X4 image\n",
        "    for j in range(16):\n",
        "        bin_index = bin(j)[2:] #We take the binary value of j from the 2 position to the end\n",
        "        while len(bin_index) <4: #While bin_index is less than 4\n",
        "            bin_index = '0'+bin_index #We concatenate a 0 string at the beginnig\n",
        "        try:  \n",
        "            #We try to save the element of the row in the position bin_index\n",
        "            index_image.append(i[bin_index])\n",
        "        except:\n",
        "            index_image.append(0) #If we can't, we only save a 0\n",
        "            \n",
        "    #We save the new imagen values in the train_recuce list\n",
        "    train_reduce.append(np.array(index_image))"
      ],
      "metadata": {
        "id": "mFWei3NtskE7"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "\n",
        "#subplot(r,c) provide the no. of rows and columns\n",
        "f, axarr = plt.subplots(5,1) \n",
        "\n",
        "# use the created array to output your multiple images. In this case I have stacked 4 images vertically\n",
        "axarr[0].imshow(x_test[0].reshape(8,8)*255)\n",
        "axarr[1].imshow(x_test[1].reshape(8,8)*255)\n",
        "axarr[2].imshow(x_test[2].reshape(8,8)*255)\n",
        "axarr[3].imshow(x_test[3].reshape(8,8)*255)\n",
        "axarr[4].imshow(x_test[4].reshape(8,8)*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "g0JFLTR5smiO",
        "outputId": "2fe8a033-eb8d-4fe3-8e02-3321822c1e69"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd8bb469130>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#subplot(r,c) provide the no. of rows and columns\n",
        "f, axarr = plt.subplots(5,1) \n",
        "\n",
        "# use the created array to output your multiple images. In this case I have stacked 4 images vertically\n",
        "axarr[0].imshow(test_reduce[0].reshape(4,4)*255)\n",
        "axarr[1].imshow(test_reduce[1].reshape(4,4)*255)\n",
        "axarr[2].imshow(test_reduce[2].reshape(4,4)*255)\n",
        "axarr[3].imshow(test_reduce[3].reshape(4,4)*255)\n",
        "axarr[4].imshow(test_reduce[4].reshape(4,4)*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xCv4awvSso6G",
        "outputId": "49f0fafa-632d-4ce3-8aa8-1c1ee2a73332"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd8b64ccf10>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We generate the inverse of the QVC\n",
        "vqc(n,num_layers,params).inverse().draw(output = \"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "0E7TAw7usrPR",
        "outputId": "0c75c502-e053-471e-e4ca-c8897097aa5f"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1541.66x618.722 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to decode the test set values compressed\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with decode values\n",
        "'''\n",
        "\n",
        "def decoder_result_test(params):\n",
        "    reduce = [] #List to save the decoded values\n",
        "    #For each row in the test set reduced we will\n",
        "    for i in range(len(test_reduce)):\n",
        "\n",
        "        #Create a initial circuit with 6 qubits and a list of 48 zeros and the i row of the test reduced values\n",
        "        circuit_init = input_data(6,np.concatenate((np.zeros(48), test_reduce[i]), axis=0))\n",
        "        #Create the inverse VQC \n",
        "        circuit_vqc = vqc(n,num_layers,params).inverse()\n",
        "        \n",
        "        #Create a new circuit to combine the inicial circuit and the VQC\n",
        "        circuit_full = QuantumCircuit(n,n)\n",
        "        \n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('statevector_simulator') )\n",
        "        result = job.result().get_statevector() #Get the results of the execution\n",
        "        reduce.append(result) #Save the results\n",
        "    return reduce\n",
        "        \n",
        "#Call the decoder_result_test function\n",
        "decoder =decoder_result_test(params)"
      ],
      "metadata": {
        "id": "kGXZ3ulkstvK"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "\n",
        "#subplot(r,c) provide the no. of rows and columns\n",
        "f, axarr = plt.subplots(5,1) \n",
        "\n",
        "# use the created array to output your multiple images. In this case I have stacked 4 images vertically\n",
        "axarr[0].imshow(decoder[0].real.reshape(8,8)*255)\n",
        "axarr[1].imshow(decoder[1].real.reshape(8,8)*255)\n",
        "axarr[2].imshow(decoder[2].real.reshape(8,8)*255)\n",
        "axarr[3].imshow(decoder[3].real.reshape(8,8)*255)\n",
        "axarr[4].imshow(decoder[4].real.reshape(8,8)*255)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "9tFAIQhssvjW",
        "outputId": "95719a00-7f7c-4950-ac81-4716b73ed91f"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-90-826eccac2d46>:7: DeprecationWarning: The return type of saved statevectors has been changed from a `numpy.ndarray` to a `qiskit.quantum_info.Statevector` as of qiskit-aer 0.10. Accessing numpy array attributes is deprecated and will result in an error in a future release. To continue using saved result objects as arrays you can explicitly cast them using  `np.asarray(object)`.\n",
            "  axarr[0].imshow(decoder[0].real.reshape(8,8)*255)\n",
            "<ipython-input-90-826eccac2d46>:8: DeprecationWarning: The return type of saved statevectors has been changed from a `numpy.ndarray` to a `qiskit.quantum_info.Statevector` as of qiskit-aer 0.10. Accessing numpy array attributes is deprecated and will result in an error in a future release. To continue using saved result objects as arrays you can explicitly cast them using  `np.asarray(object)`.\n",
            "  axarr[1].imshow(decoder[1].real.reshape(8,8)*255)\n",
            "<ipython-input-90-826eccac2d46>:9: DeprecationWarning: The return type of saved statevectors has been changed from a `numpy.ndarray` to a `qiskit.quantum_info.Statevector` as of qiskit-aer 0.10. Accessing numpy array attributes is deprecated and will result in an error in a future release. To continue using saved result objects as arrays you can explicitly cast them using  `np.asarray(object)`.\n",
            "  axarr[2].imshow(decoder[2].real.reshape(8,8)*255)\n",
            "<ipython-input-90-826eccac2d46>:10: DeprecationWarning: The return type of saved statevectors has been changed from a `numpy.ndarray` to a `qiskit.quantum_info.Statevector` as of qiskit-aer 0.10. Accessing numpy array attributes is deprecated and will result in an error in a future release. To continue using saved result objects as arrays you can explicitly cast them using  `np.asarray(object)`.\n",
            "  axarr[3].imshow(decoder[3].real.reshape(8,8)*255)\n",
            "<ipython-input-90-826eccac2d46>:11: DeprecationWarning: The return type of saved statevectors has been changed from a `numpy.ndarray` to a `qiskit.quantum_info.Statevector` as of qiskit-aer 0.10. Accessing numpy array attributes is deprecated and will result in an error in a future release. To continue using saved result objects as arrays you can explicitly cast them using  `np.asarray(object)`.\n",
            "  axarr[4].imshow(decoder[4].real.reshape(8,8)*255)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd8aeae3bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to decode the training set values compressed\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with decode values\n",
        "'''\n",
        "def decoder_result_train(params):\n",
        "    reduce = [] #List to save the decoded values\n",
        "    #For each row in the test set reduced we will\n",
        "    for i in range(len(train_reduce)):\n",
        "        #Create a initial circuit with 6 qubits and a list of 48 zeros and the i row of the test reduced values\n",
        "        circuit_init = input_data(n,np.concatenate((np.zeros(48), train_reduce[i]), axis=0))\n",
        "        #Create the inverse VQC \n",
        "        circuit_vqc = vqc(n,num_layers,params).inverse()\n",
        "\n",
        "        #Create a new circuit to combine the inicial circuit and the VQC\n",
        "        circuit_full = QuantumCircuit(n,n)\n",
        "        \n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('statevector_simulator') )\n",
        "        result = job.result().get_statevector() #Get the results of the execution\n",
        "        reduce.append(result) #Save the results\n",
        "    return reduce\n",
        "     \n",
        "#Call the decoder_result_train function    \n",
        "decoder_train =decoder_result_train(params)"
      ],
      "metadata": {
        "id": "R9B7aroasx1G"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading the MNIST set divided by a train set and a test set\n",
        "(x_train_c, y_train_c), (x_test_c, y_test_c) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "# Rescale the images from [0,255] to the [0.0,1.0] range.\n",
        "x_train_c, x_test_c = x_train_c[..., np.newaxis]/255.0, x_test_c[..., np.newaxis]/255.0"
      ],
      "metadata": {
        "id": "fks_K2Hus2sV"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_c, y_train_c = filter_01(x_train_c, y_train_c) #Filter the train set\n",
        "x_test_c, y_test_c = filter_01(x_test_c, y_test_c) #Filter the test set"
      ],
      "metadata": {
        "id": "qIjRu_nftCj9"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#resizing the images from 28x28 to 8x8 by the nearest method\n",
        "x_train_c = tf.image.resize(x_train_c, (8,8), method='nearest', preserve_aspect_ratio=True).numpy()\n",
        "x_test_c = tf.image.resize(x_test_c, (8,8), method='nearest', preserve_aspect_ratio=True).numpy()"
      ],
      "metadata": {
        "id": "ezwcJGhqtE_J"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalize the training set data\n",
        "for i in range(len(x_train_c)):\n",
        "    x_train_c[i] = Normalize(x_train_c[i])\n",
        "\n",
        "#Normalize the test set data\n",
        "for i in range(len(x_test)):\n",
        "    x_test_c[i] = Normalize(x_test_c[i])"
      ],
      "metadata": {
        "id": "r5yaNyPbtHMR"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshaping the train and test test to a 64x1 matriz\n",
        "x_train_c = x_train_c.reshape(len(x_train_small), 64)\n",
        "x_test_c = x_test_c.reshape(len(x_test_small), 64)\n",
        "\n",
        "x_train_c.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5xmyrrstI6M",
        "outputId": "a6eeb067-8b1b-4ac5-82cd-679527c38ca4"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12665, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to compress the training set values\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with compress values\n",
        "'''\n",
        "def compress_result_train(params):\n",
        "    reduce = [] #List to save the compress values\n",
        "    #For each row in the training set we will\n",
        "    for i in range(len(x_train_c)):\n",
        "        circuit_init = input_data(n,x_train_c[i]) #Create a inicial circuit\n",
        "        circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        "\n",
        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "        circuit_full = QuantumCircuit(n,n-size_reduce)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        len_cf = len(circuit_full) #Known the length of the circuit\n",
        "        #For each n - the the desired qubits to reduce we will\n",
        "        for i in range(n-size_reduce):\n",
        "            circuit_full.measure(size_reduce+i, i) #Measure the circuit in the position size_reduce+i\n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('qasm_simulator'),shots=1024 )\n",
        "        result = job.result().get_counts() #Get the results of the execution\n",
        "        #Get the probabilities of each state\n",
        "        probs = {k: np.sqrt(v / 1024) for k, v in result.items()}\n",
        "        reduce.append(probs) #Save the probabilities\n",
        "        \n",
        "    return reduce\n",
        "\n",
        "#Call the compress_result_train function with the parameters defined above\n",
        "reduce_train_c = compress_result_train(params)"
      ],
      "metadata": {
        "id": "2Xvmn23MtLhq"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to compress the test set values\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with compress values\n",
        "'''\n",
        "def compress_result_test(params):\n",
        "    \n",
        "    reduce = [] #List to save the compress values\n",
        "    #For each row in the test set we will\n",
        "    for i in range(len(x_test_c)):\n",
        "        circuit_init = input_data(n,x_test_c[i]) #Create a inicial circuit\n",
        "        circuit_vqc = vqc(n,num_layers,params) #Create a quantum variational circuit\n",
        "    \n",
        "        #Create a new circuit based on the size of the initial circuit and the desired qubits to reduce\n",
        "        circuit_full = QuantumCircuit(n,n-size_reduce)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        len_cf = len(circuit_full) #Known the length of the circuit\n",
        "        #For each n - the the desired qubits to reduce we will\n",
        "        for i in range(n-size_reduce):\n",
        "            circuit_full.measure(size_reduce+i, i) #Measure the circuit in the position size_reduce+i \n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('qasm_simulator'),shots=1024 )\n",
        "        result = job.result().get_counts() #Get the results of the execution\n",
        "        #Get the probabilities of each state\n",
        "        probs = {k: np.sqrt(v / 1024) for k, v in result.items()}\n",
        "        reduce.append(probs) #Save the probabilities\n",
        "        \n",
        "    return reduce\n",
        "\n",
        "#Call the compress_result_test function with the parameters defined above\n",
        "reduce_test_c = compress_result_test(params)"
      ],
      "metadata": {
        "id": "wSrTVNZqtN6f"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_reduce = [] #List to save the new values of the image reduction\n",
        "#for each value in the reduce_img list\n",
        "for i in reduce_test_c:\n",
        "    index_image = [] #List to save the reduction values\n",
        "    #We now take in count we want a 4X4 image\n",
        "    for j in range(16):\n",
        "        bin_index = bin(j)[2:] #We take the binary value of j from the 2 position to the end\n",
        "        while len(bin_index) <4: #While bin_index is less than 4\n",
        "            bin_index = '0'+bin_index #We concatenate a 0 string at the beginnig\n",
        "        try:   \n",
        "            #We try to save the element of the row in the position bin_index\n",
        "            index_image.append(i[bin_index])\n",
        "        except:\n",
        "            index_image.append(0) #If we can't, we only save a 0\n",
        "            \n",
        "    #We save the new imagen values in the test_recuce list\n",
        "    test_reduce.append(np.array(index_image))"
      ],
      "metadata": {
        "id": "SiVWOmj8tP8b"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_reduce = [] #List to save the new values of the image reduction\n",
        "#for each value in the reduce_img list\n",
        "for i in reduce_train_c:\n",
        "    index_image = [] #List to save the reduction values\n",
        "    #We now take in count we want a 4X4 image\n",
        "    for j in range(16):\n",
        "        bin_index = bin(j)[2:] #We take the binary value of j from the 2 position to the end\n",
        "        while len(bin_index) <4: #While bin_index is less than 4\n",
        "            bin_index = '0'+bin_index #We concatenate a 0 string at the beginnig\n",
        "        try:  \n",
        "            #We try to save the element of the row in the position bin_index\n",
        "            index_image.append(i[bin_index])\n",
        "        except:\n",
        "            index_image.append(0) #If we can't, we only save a 0\n",
        "        \n",
        "    #We save the new imagen values in the train_recuce list\n",
        "    train_reduce.append(np.array(index_image))"
      ],
      "metadata": {
        "id": "4HzULpbMtR85"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to decode the training set values compressed\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with decode values\n",
        "'''\n",
        "def decoder_result_train_c(params):\n",
        "    reduce = [] #List to save the decoded values\n",
        "    #For each row in the test set reduced we will\n",
        "    for i in range(len(train_reduce)):\n",
        "        #Create a initial circuit with 6 qubits and a list of 48 zeros and the i row of the test reduced values\n",
        "        circuit_init = input_data(n,np.concatenate((np.zeros(48), train_reduce[i]), axis=0))\n",
        "        circuit_vqc = vqc(n,num_layers,params).inverse() #Create the inverse VQC \n",
        "\n",
        "        #Create a new circuit to combine the inicial circuit and the VQC\n",
        "        circuit_full = QuantumCircuit(n,n)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('statevector_simulator') ) \n",
        "        result = job.result().get_statevector() #Get the results of the execution\n",
        "        reduce.append(result) #Save the results\n",
        "    return reduce\n",
        "\n",
        "#Call the decoder_result_train function\n",
        "decoder_train_c =decoder_result_train_c(params)\n",
        "len(decoder_train_c) #Get the length of the decoder train list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAYSHdDttUFi",
        "outputId": "e286a278-abb3-4fa3-ebb8-0eae94dde7e2"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12665"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to decode the test set values compressed\n",
        "'''\n",
        "Input: An array of parameters(vector of complex amplitudes)\n",
        "Output: Array with decode values\n",
        "'''\n",
        "def decoder_result_test_c(params):\n",
        "    reduce = [] #List to save the decoded values\n",
        "    #For each row in the test set reduced we will\n",
        "    for i in range(len(test_reduce)):\n",
        "\n",
        "        #Create a initial circuit with 6 qubits and a list of 48 zeros and the i row of the test reduced values\n",
        "        circuit_init = input_data(6,np.concatenate((np.zeros(48), test_reduce[i]), axis=0))\n",
        "        #Create the inverse VQC \n",
        "        circuit_vqc = vqc(n,num_layers,params).inverse()\n",
        "\n",
        "        #Create a new circuit to combine the inicial circuit and the VQC\n",
        "        circuit_full = QuantumCircuit(n,n)\n",
        "\n",
        "        #Combine the initial circuit, the quantum variatinal circuit\n",
        "        circuit_full = circuit_full.compose(circuit_init,[i for i in range(n)])\n",
        "        circuit_full = circuit_full.compose(circuit_vqc,[i for i in range(n)])\n",
        "        #We will execute the full circuit in the qasm simulator\n",
        "        job = execute( circuit_full, Aer.get_backend('statevector_simulator') )\n",
        "        result = job.result().get_statevector() #Get the results of the execution\n",
        "        reduce.append(result) #Save the results\n",
        "    return reduce\n",
        "        \n",
        "#Call the decoder_result_test function\n",
        "decoder_c =decoder_result_test_c(params)"
      ],
      "metadata": {
        "id": "Wye44B_DtWNz"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "\n",
        "\n",
        "\n",
        "df = pd.DataFrame(train_reduce) #Dataframe for the training reduce set\n",
        "df[16] = y_train #The class of each value is at the end of the dataframe\n",
        "df.to_csv(\"train_1.csv\",index=False) #Create the csv file with the results\n",
        "\n",
        "\n",
        "\n",
        "df = pd.DataFrame(test_reduce) #Dataframe for the test reduce set\n",
        "df[16] = y_test #The class of each value is at the end of the dataframe\n",
        "df.to_csv(\"test_1.csv\",index=False) #Create the csv file with the results"
      ],
      "metadata": {
        "id": "eMwBv5iPtYfo"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scikit Imports\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "# Qiskit Imports\n",
        "from qiskit import Aer, execute\n",
        "from qiskit.circuit import QuantumCircuit, Parameter, ParameterVector\n",
        "from qiskit.circuit.library import PauliFeatureMap, ZFeatureMap, ZZFeatureMap\n",
        "from qiskit.circuit.library import TwoLocal, NLocal, RealAmplitudes, EfficientSU2\n",
        "from qiskit.circuit.library import HGate, RXGate, RYGate, RZGate, CXGate, CRXGate, CRZGate\n",
        "from qiskit_machine_learning.kernels import QuantumKernel\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import csv"
      ],
      "metadata": {
        "id": "KW_h1Un3ta1c"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_train = [] #List from the sample train\n",
        "label_train = [] #List from the label train\n",
        "with open('train_1.csv', newline='') as csvfile: #Open the csv file that we before create\n",
        "    reader = csv.reader(csvfile, delimiter=',') #Reading the csv file and get the values by commas\n",
        "    next(reader, None) #We ignore the header\n",
        "    for row in reader: #We go for each row of the file\n",
        "        sample_train.append(row[0:-1]) #The values of the samples are all except the last cell\n",
        "        label_train.append(row[-1]) #The class of that row is the last cell\n",
        "sample_train = np.array(sample_train).astype(np.float) #Convert the sample train to a numpy float array\n",
        "label_train = np.array(label_train).astype(np.float) #Convert the label train to a numpy float array\n",
        "sample_train.shape, label_train.shape #Show the shape of the arrays"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIei5YORtchr",
        "outputId": "aa83dccf-ab8d-48c0-e9e8-2a084424ee34"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-105-062e607c369d>:9: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sample_train = np.array(sample_train).astype(np.float) #Convert the sample train to a numpy float array\n",
            "<ipython-input-105-062e607c369d>:10: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  label_train = np.array(label_train).astype(np.float) #Convert the label train to a numpy float array\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12665, 16), (12665,))"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_test = [] #List from the sample test\n",
        "label_test = [] #List from the label test\n",
        "with open('test_1.csv', newline='') as csvfile: #Open the csv file that we before create\n",
        "    reader = csv.reader(csvfile, delimiter=',') #Reading the csv file and get the values by commas\n",
        "    next(reader, None) #We ignore the header\n",
        "    for row in reader: #We go for each row of the file\n",
        "        sample_test.append(row[0:-1]) #The values of the samples are all except the last cell\n",
        "        label_test.append(row[-1]) #The class of that row is the last cell\n",
        "sample_test = np.array(sample_test).astype(np.float) #Convert the sample test to a numpy float array\n",
        "label_test = np.array(label_test).astype(np.float) #Convert the label test to a numpy float array\n",
        "sample_test.shape, label_test.shape #Show the shape of the arrays"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNLOklUEteQz",
        "outputId": "8248c8c9-4490-417b-d7fb-a171fcb25cfe"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-106-216e1b077fa8>:9: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  sample_test = np.array(sample_test).astype(np.float) #Convert the sample test to a numpy float array\n",
            "<ipython-input-106-216e1b077fa8>:10: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  label_test = np.array(label_test).astype(np.float) #Convert the label test to a numpy float array\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2115, 16), (2115,))"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_train = sample_train.reshape(len(sample_train), 16) #Reshape the sample train to a 16X1 array\n",
        "sample_test = sample_test.reshape(len(sample_test), 16) #Reshape the sample test to a 16X1 array"
      ],
      "metadata": {
        "id": "JekB14lRtgNG"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = 700 #Number of samples of the training sample\n",
        "sample_train = sample_train[:train_size] #Reduce the sample train\n",
        "label_train = label_train[:train_size] #Reduce the label train\n",
        "\n",
        "test_size = 300 #Number of samples of the test sample\n",
        "sample_test = sample_test[:test_size] #Reduce the sample test\n",
        "label_test = label_test[:test_size] #Reduce the label test"
      ],
      "metadata": {
        "id": "EbCJoN9Ith69"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#map the classical vector to a quantum state with the ZZFeatureMap, 16 qubits, 2 repetiton and a linear entanglement\n",
        "zz_map = ZZFeatureMap(feature_dimension=16, reps=2, entanglement='linear', insert_barriers=True)\n",
        "#Create the quantum kernel with the ZZFeatureMap\n",
        "zz_kernel = QuantumKernel(feature_map=zz_map, quantum_instance=Aer.get_backend('statevector_simulator'))\n",
        "#Show the ZZFeatureMap with 16 qubits\n",
        "zz_map.decompose().draw(output=\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OWxoF06OtkMR",
        "outputId": "e9fbe4d6-f5ce-4588-9fbe-208ea294b413"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2228.78x9899.56 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We create the matrix_train with the sample train values\n",
        "matrix_train = zz_kernel.evaluate(x_vec=sample_train)\n",
        "#We create the matrix test with the sample test and sample train values\n",
        "matrix_test = zz_kernel.evaluate(x_vec=sample_test, y_vec=sample_train)"
      ],
      "metadata": {
        "id": "5UiLf9_0tl7f"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We use a SVC with a precomputed kerner\n",
        "zzpc_svc = SVC(kernel='precomputed') \n",
        "#We fit the model with the train matrix and the label train\n",
        "zzpc_svc.fit(matrix_train, label_train)\n",
        "#We calculate the score of the model with the matrix test and label test\n",
        "zzpc_score = zzpc_svc.score(matrix_test, label_test)\n",
        "\n",
        "#We show the score value\n",
        "print(f'Precomputed kernel classification test score: {zzpc_score}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIubWRe0toGz",
        "outputId": "39226f0a-9c48-47bd-c950-d786fb22ec56"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precomputed kernel classification test score: 0.9266666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make a prediction using the test matrix\n",
        "predictions = zzpc_svc.predict(matrix_test)"
      ],
      "metadata": {
        "id": "XwWqUdyPtrDp"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We need to see how many prediction go wrong\n",
        "for prediction,label in zip(predictions,label_test):\n",
        "    if(prediction != label): #If the prediction is different from the class\n",
        "        print(prediction, label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_H1lWa7TttfV",
        "outputId": "862dd6ca-128c-4d7a-8232-48f3e6ac28f1"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "0.0 1.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "0.0 1.0\n",
            "1.0 0.0\n",
            "0.0 1.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n",
            "0.0 1.0\n",
            "1.0 0.0\n",
            "1.0 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_0, class_1 = [], []\n",
        "for i in range(len(label_test)):\n",
        "    if (predictions[i] == label_test[i]):\n",
        "        if (label_test[i] == 0):\n",
        "            class_0.append(sample_test[i])\n",
        "        else:\n",
        "            class_1.append(sample_test[i])\n",
        "\n",
        "print(len(class_0))\n",
        "print(len(class_1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEoP51-LaE4K",
        "outputId": "17b14d53-5184-4192-878e-59e06e926532"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "105\n",
            "173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "len0 = len(class_0)\n",
        "len1 = len(class_1)\n",
        "\n",
        "idx0, idx1 = random.randint(0, len0-1), random.randint(0, len0-1)\n",
        "print(idx0, idx1)\n",
        "\n",
        "print(\"Fidelity of two images from the same class: \", compute_fidelity(4, input_data(4, class_0[idx0]), input_data(4, class_0[idx1])))\n",
        "\n",
        "idx0, idx1 = random.randint(0, len1-1), random.randint(0, len1-1)\n",
        "print(idx0, idx1)\n",
        "\n",
        "print(\"Fidelity of two images from the same class: \", compute_fidelity(4, input_data(4, class_1[idx0]), input_data(4, class_1[idx1])))\n",
        "\n",
        "idx0, idx1 = random.randint(0, len0-1), random.randint(0, len1-1)\n",
        "print(idx0, idx1)\n",
        "\n",
        "print(\"Fidelity of two images from two different classes: \", compute_fidelity(4, input_data(4, class_0[idx0]), input_data(4, class_1[idx1])))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRQ9tQwqbPl4",
        "outputId": "7ad67281-2b7a-4661-9077-4f26089d250d"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67 10\n",
            "Fidelity of two images from the same class:  0.826171875\n",
            "107 150\n",
            "Fidelity of two images from the same class:  0.984375\n",
            "73 165\n",
            "Fidelity of two images from two different classes:  0.5703125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusion\n",
        "\n",
        "We can see that the fidelity of two images from the same class is maximized while the fidelity of two images from different classes is minimized. This indicates that the classifier is working correctly. Additionally, the classifier was able to achieve an accuracy score of approximately 92.6% in the test set."
      ],
      "metadata": {
        "id": "CnJjvAxE6iOW"
      }
    }
  ]
}