Challenge_Upload_V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "c8b2b244-bf08-47e0-ab55-bed9501f0d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import Aer, execute, QuantumCircuit\n",
    "from qiskit.quantum_info import Statevector, state_fidelity\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.visualization import plot_histogram\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.decomposition import NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "d491cb1e-b8fd-45e6-ba61-0d86f3012617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.68481034e+01, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.06836951e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 3.33475287e-16]])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am using the SVD descomposition:\n",
    "# https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html\n",
    "# And put the diagonal matrix into the quantum circuit.\n",
    "# The image is decreased in size by 2. (28x28) --> (14x14)\n",
    "# The porpuse is to have just 14 values to put into the quantum circuit.\n",
    "\n",
    "X = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "W = np.linalg.svd(X)\n",
    "np.dot(W[0] * W[1], W[2])\n",
    "np.diag(W[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8963d8fd-3d37-417c-834c-2655b2e16fc8",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "8d909095-8144-4210-973f-8dcc18a0ca2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images=np.load('data/images.npy')\n",
    "labels=np.load('data/labels.npy')\n",
    "images = (images/0.00392156862745098)\n",
    "select_image = 2\n",
    "plt.imshow(images[select_image])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97fd771c-4280-4250-a6ef-162e51b895bb",
   "metadata": {},
   "source": [
    "# Part 1 - Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "7aafa848-cf5d-4eaf-86ef-8622107de355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image before:\n",
      "The shape of the images is: (2000, 28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = np.array([14,14])\n",
    "area = 7*2\n",
    "Shots = 10000\n",
    "select_image = 2\n",
    "\n",
    "images2 = images\n",
    "images2 = images2.reshape(images2.shape[0], *(28,28,1))\n",
    "\n",
    "images2 = tf.image.resize(images2, (size[0],size[1])).numpy()\n",
    "\n",
    "print(\"Image before:\")\n",
    "plt.imshow(images[select_image])\n",
    "print(\"The shape of the images is: {}\".format(images.shape))\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "702ac603-fbe5-4b3b-8377-8f6c57ec3a14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image then:\n",
      "The shape of the images is: (2000, 14, 14, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Image then:\")\n",
    "print(\"The shape of the images is: {}\".format(images2.shape))\n",
    "Real_values = images2[select_image]\n",
    "plt.imshow(images2[select_image])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "17078d96-0749-4f2f-8bf7-26ee74bf48d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = np.asarray(images2[select_image])\n",
    "T = T.reshape(14,14)\n",
    "W = np.linalg.svd(T)\n",
    "# Convert: np.dot(W[0]*W[1], W[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "1479028d-264e-4760-b544-e59921ed1bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#images3 = images3.reshape(images3.shape[0], *(1,area,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237d27db-8014-45c6-bf7c-1bba65f141d0",
   "metadata": {},
   "source": [
    "# Encoding Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "e264f414-2081-4900-ad81-f2e876aeda9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Encode(select_image, area):\n",
    "    \n",
    "    T = np.asarray(images2[select_image])\n",
    "    T = T.reshape(14,14)\n",
    "    W = np.linalg.svd(T)\n",
    "\n",
    "    p = ParameterVector('p', area) \n",
    "    qc = QuantumCircuit(area)\n",
    "    for i in range(area):\n",
    "        qc.rx(p[i],i)\n",
    "    # bind parameters\n",
    "    for i in range(area):\n",
    "        qc = qc.bind_parameters({p[i]: np.pi/2*(W[1][i]/(W[1].max()))})\n",
    "    \n",
    "    qc.measure_all()\n",
    "    backend = Aer.get_backend('qasm_simulator')\n",
    "    job = execute(qc, backend, shots=Shots)\n",
    "    res = job.result().get_counts(qc)\n",
    "    return res, W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "4a872cb9-e322-4724-b5d0-694a7575fba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res, W = Encode(select_image, area)\n",
    "plot_histogram(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d9f3f7-9945-4090-b573-87b5bf845cbd",
   "metadata": {},
   "source": [
    "# Part 2 - Decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "e2814e4e-b5e0-47e1-9f31-d105c982556e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Decode(hist, w, area):\n",
    "    keys = np.array(list(hist.keys()))\n",
    "    for k in range(len(keys)):\n",
    "        keys[k] = int(keys[k],2)\n",
    "    keys = np.array(keys, dtype='int64')\n",
    "    values = np.array(list(hist.values()))\n",
    "    values = values/Shots\n",
    "    new_dict = {keys[i]: values[i] for i in range(len(keys))}\n",
    "    parameter_list = []\n",
    "    \n",
    "    if (1 in keys):\n",
    "        parameter_list.append(2*np.arctan(np.sqrt(new_dict[1]/new_dict[0])))  \n",
    "    else:\n",
    "        parameter_list.append(0)\n",
    "    \n",
    "    value = 1\n",
    "    for j in range(area-1):\n",
    "        value *= 2\n",
    "        if (value in keys):\n",
    "            parameter_list.append(2*np.arctan(np.sqrt(new_dict[value]/new_dict[0])))\n",
    "        else:\n",
    "            parameter_list.append(0)\n",
    "    #    else:\n",
    "    #        parameter_list.append(2*np.arctan(np.sqrt(new_dict[value]/new_dict[0])))\n",
    "    \n",
    "    parameter_list = np.array(parameter_list, dtype='float32')\n",
    "    parameter_list = (parameter_list*w[1].max())/(np.pi/2)\n",
    "    #parameter_list = parameter_list.reshape(size[0],size[1])\n",
    "    \n",
    "    return parameter_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "3b4a2f33-6497-4183-b082-a38f5eea4784",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Testing(image, area):\n",
    "    res, W = Encode(image, area)\n",
    "    dec = Decode(res, W, area)\n",
    "    \n",
    "    real_image = images2[image]\n",
    "    gen_image = np.dot(W[0] * dec, W[2])\n",
    "    \n",
    "    return real_image, gen_image\n",
    "\n",
    "real, gen = Testing(1, area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "69667f7b-34ff-4dbf-9366-aef5c6f09903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pwKeAMyfb35eKZjZRUOZCgll1FTt9F3hXVqdOXGY2QatYRkljXNvqKkraicZioysnxJMOaHv5DuCBrE59qWhm3UqaxxURo5JadRWHgEtbdRWB1RGxEvigpGOAEeBpMi4TwYnLzLZDUd4M1Ky6ihFx7lT7dOIys4kGvPJDHk5cZtbFzyqaWe1UfSFBJy4z6+YzLjOrlRpUsnbiMrNuTlzThIrP1dWssRIPpL9mDHCwYySGktrPnL+1eOOEf28Axiv+lz+J1gTUKnPiMrMuqnjideIys4k8j8vM6qjq0yEyL+QlXSppo6R7295bIOlGSQ80/7t7bw/TzPqq4nUV84xAXgYs7Xjv48BNEXEAcFPztZlNEyWuDtETmYkrIm4FNnW8/U7g8ubXl5Nj/Rwzq4kAIvJtA1J0jGvPiHi8+fUGYM8d7ShpGbAMYDZzCoYzs36q+hhX8uB8RIS045PG5vrTywHmaUHF71WYWR3mcRWdZfd7SXsBNP+7sbxDMrOBynuZOMBLxaKJayV/WqXwTOCH5RyOmVVBmYPzOQrCfkTSWkn3SLpJ0r5ZfeaZDnEVcBtwoKT1ks4GLgKOlfQAcEzztZlNF/0tCHs3sCQiXgesoFEUdlKZY1wRceoOvnV0Vlszq6cSx7i2FYQFkNQqCLu2tUNE/Kxt/9uB07M69cx5M5sogLHcmauUgrBtzgZ+khXUicvMuvS7ICyApNOBJcDbs/Z96SSuGUprP1Z8aZoYTVsiZVjFY784PpwUeyjxuY6tCR97ys8NEI/unNQ+Serv26CVd8cwV0HYZnmyTwJvj4gtWZ26IKyZdelzQdhDga8DJ0ZErqlVTlxmNlHeO4o5EldEjAKtgrDrgKtbBWElndjc7XPALsD3Ja2RtHIH3W3z0rlUNLNcBCj/4HymHAVhj5lqn05cZtalzErWveDEZWYTeQVUM6ufwT6HmIcTl5l1qfrqEE5cZtbNZ1xmVitR7l3FXnDiMrNu1c5bTlxm1s3TIcysfpy4zKxWApjuxTLMbHoR4UtFM6uh8Wqfcjlx5TU0VLjp3PkvJIV+MYqvqTVXW5Nip66J9Yadiv+K3fhC2npaY/NHC7fVUNrCKVHxM5ZJ+VLRzOrIl4pmVj8VT1xeSNDMOpRbEDZHXcW3SbpL0qikk/P0maeu4qWSNkq6t+29z0n6VbOA47WS5uf6Ccys+lpVfvJsGXLWVXwYOAu4Mu8h5jnjugxY2vHejcAhzQKO9wMX5A1oZtWniFxbDtvqKkbEVqBVV3GbiPhdRNzDFG4JZCauiLgV2NTx3g3NtaShUcBxcd6AZlYD+S8VF0pa3bYt6+hpe3UVF6UeXhmD8+8FvldCP2ZWBQGM5x6cL62u4lQkJS5JnwRGgSsm2WcZsAxgNnNSwplZX5S6AmquuopTVfiuoqSzgBOAd8cks+0iYnlELImIJcPMKhrOzPqpvLuKmXUViyiUuCQtBT5Go4Dj86kHYWYVEsDYeL4tq6scdRUlvUnSeuBvgK9Lui+r38xLRUlXAUfSGIRbD1xI4y7iLOBGSQC3R8T7M38KM6uBgCjvmZ8cdRXvYIo3+DITV0Scup23vzmVIGZWMxWfOe9HfsxsoqndVRwIJy4z6+YzLlt00tqk9v+2y18UbqvZs5NiM5K2LE6MFF9aZvz5tPs+r447CreNWS/xO+BOXGZWKxEwlrYOW685cZlZN59xmVntOHGZWb2E7yqaWc0ERIkTUHvBicvMuuV4nGeQnLjMbKIIlyczsxry4LyZ1U34jMvM6qXUhQR7wonLzCbyQ9ZmVjcBRMUf+XFBWDObKJoLCebZcshREHaWpO81v79K0n5ZfTpxmVmXGI9cW5acBWHPBp6OiFcBXwI+m9WvE5eZdSvvjCuzIGzz9eXNr1cAR6u5JvyO9HWM6zmefvKnseKhSXZZCDy5w++mXHZnt508dm9NHvsPCT1nt63uzz3I2C8OMHaafVM7eI6nr/9prFiYc/fZkla3vV4eEcvbXm+vIOxhHX1s2yciRiU9C/wZk3xGfU1cEbHHZN+XtHoQxSUd27FfKrHziIilgz6GLL5UNLNeylMQdts+kmYCuwFPTdapE5eZ9VKegrArgTObX58M3DxZkWmo3jyu5dm7OLZjO3ZdNMesWgVhh4BLWwVhgdURsZJGucNvS/oNsIlGcpuUMhKbmVnl+FLRzGrHicvMaqcSiSvrkYAext1H0s8krZV0n6Rz+xW77RiGJN0t6Ud9jjtf0gpJv5K0TtJb+hj7vObnfa+kqyQlFn/MjHeppI2S7m17b4GkGyU90Pzv7n2M/bnm536PpGslze9F7Ols4Ikr5yMBvTIKnB8RBwOHAx/oY+yWc4F1fY4J8BXguog4CHh9v45B0iLgQ8CSiDiExoBt5mBsosuAzrlJHwduiogDgJuar/sV+0bgkIh4HXA/cEGPYk9bA09c5HskoCci4vGIuKv59XM0/ngX9SM2gKTFwDuAS/oVsxl3N+BtNO7mEBFbI+KZPh7CTGDn5pydOcBjvQwWEbfSuFvVrv0xk8uBd/UrdkTcEBGtEt+305jbZFNQhcS1vUcC+pY8WppPpB8KrOpj2C8DHwP6vdzk/sATwLeal6mXSJrbj8AR8SjweeBh4HHg2Yi4oR+xO+wZEY83v94A7DmAYwB4L/CTAcWurSokroGTtAvwA+DDEZHyZOBUYp4AbIyIO/sRr8NM4I3A1yLiUGAzvbtUmqA5lvROGslzb2CupNP7EXtHmpMd+z4vSNInaQxXXNHv2HVXhcSV55GAnpE0TCNpXRER1/QrLnAEcKKk39G4PD5K0nf6FHs9sD4iWmeXK2gksn44BvhtRDwRESPANcBb+xS73e8l7QXQ/O/GfgaXdBZwAvDurFni1q0KiSvPIwE90Vw645vAuoj4Yj9itkTEBRGxOCL2o/Ez3xwRfTnziIgNwCOSDmy+dTSwth+xaVwiHi5pTvPzP5rB3Jxof8zkTOCH/QosaSmNIYITI+L5fsWdTgaeuJqDlK1HAtYBV0fEfX0KfwRwBo2znTXN7fg+xR60c4ArJN0DvAH4TD+CNs/yVgB3Ab+k8TvY00dgJF0F3AYcKGm9pLOBi4BjJT1A4yzwoj7G/g9gV+DG5u/cf/Yi9nTmR37MrHYGfsZlZjZVTlxmVjtOXGZWO05cZlY7TlxmVjtOXGZWO05cZlY7/w+eU9rcrgfLSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(real)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "5b636c88-f0f8-4ff1-9875-333585c6a852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(gen)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "6563f368-251b-4ace-acae-e93fcd0be097",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Testing_grad(image, area):\n",
    "    res, W = Encode(image, area)\n",
    "    dec = Decode(res, W, area)\n",
    "    \n",
    "    real_image = images2[image]\n",
    "    gen_image = np.dot(W[0] * dec, W[2])\n",
    "    \n",
    "    return mean_squared_error(real_image.reshape(1,-1)[0], gen_image.reshape(1,-1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "77087873-fe98-4f9a-b6f7-55d8352a429d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998789019809919"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Testing_grad(1,area)\n",
    "1-meas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "29a34866-a24b-4f52-a8d7-a37c59e1b774",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9998288535776948\n"
     ]
    }
   ],
   "source": [
    "# Fidelity:\n",
    "\n",
    "mse = 0\n",
    "count = 0\n",
    "for i in range(len(images2)):\n",
    "    count += 1\n",
    "    #print(count) # to measure the progress.\n",
    "    res, w = Encode(i, area)\n",
    "    dec = Decode(res, w, area)\n",
    "    mse += Testing_grad(i,area)\n",
    "\n",
    "print(1-mse/len(images2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "de3a0820-b3a2-4848-bd70-f78de23fba55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9998288535776948\n"
     ]
    }
   ],
   "source": [
    "print(1-mse/len(images2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6deb40f-917e-4db0-8e78-4c0f6339fa41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "987a45b1-92f8-4bfe-b204-534f7fa040bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2910a59e-093a-4918-bbb6-10e73d48d384",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c91dbb46-d035-4e7d-98ac-71c358ed16f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [Qiskit]",
   "language": "python",
   "name": "python3_qiskit_9y9siy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}