INEQR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "febfd05ba11b2fb8",
   "metadata": {},
   "source": [
    "# Improved Novel Enhanced Quantum Representation (INEQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d366abb068fbc478",
   "metadata": {},
   "source": [
    "The Improved Novel Enhanced Quantum Representation (INEQR) method was introduced to improve the existing NEQR method and support encoding of non-square images with unequal horizontal ($2^{n_1}$) and vertical ($2^{n_2}$) dimensions.\n",
    "\n",
    "QIR Method | Qubit Req. | Image Size | Intensity Range | Position Encoding | Color Encoding |\n",
    "|----------|------------|------------|-----------------|-------------------|----------------|\n",
    "|INEQR|$n_1 + n_2 + q$|$2^{n_1} \\times 2^{n_2}$|$[0, 2^q - 1]$|Basis Encoding|Basis Encoding|\n",
    "\n",
    "***\n",
    "\n",
    "INEQR supports representation of grayscale images of dimensions $2^{n_1} \\times 2^{n_2}$, given as,\n",
    "\n",
    "\\begin{equation}\n",
    "    |I\\rangle = \\frac{1}{2^{\\frac{n_1 + n_2}{2}}} \\sum_{Y=0}^{2^{n_1} - 1} \\sum_{X=0}^{2^{n_2} - 1} |f(Y, X)\\rangle |YX\\rangle = \\frac{1}{2^{\\frac{n_1 + n_2}{2}}} \\sum_{Y=0}^{2^{n_1} - 1} \\sum_{X=0}^{2^{n_2} - 1} \\bigotimes_{i = 0}^{q-1} |C_{YX}^{i}\\rangle |YX\\rangle\n",
    "\\end{equation}\n",
    "\n",
    "Where, \n",
    "\n",
    "\\begin{equation}\n",
    "    |YX\\rangle = |y_0 y_1\\dots y_{n_1 - 1}\\rangle |x_0 x_1\\dots x_{n_2 - 1}\\rangle \\quad \\text{and} \\quad y_i, x_i \\in \\{ 0, 1\\}\n",
    "\\end{equation}\n",
    "\n",
    "$x_i, y_i$ represent the binary bits of the pixel values. For grayscale images, pixel values are in the range $[0, 2^q - 1]$, where $q = 8$.\n",
    "\n",
    "**Limitations**: Does not support encoding of colored images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1cfac627adf5c2",
   "metadata": {},
   "source": [
    "### INEQR encoding with _piQture_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T05:30:59.731393Z",
     "start_time": "2024-06-19T05:30:57.870352Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from piqture.data_loader.mnist_data_loader import load_mnist_dataset\n",
    "from piqture.embeddings.image_embeddings.ineqr import INEQR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e95193e-1c14-4819-a679-ce735addff56",
   "metadata": {},
   "source": [
    "#### Loading MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98efb64af5448a2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T05:31:04.705046Z",
     "start_time": "2024-06-19T05:30:59.732480Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Resize images to 2x2\n",
    "img_size = 2\n",
    "train_dataset, test_dataset = load_mnist_dataset(img_size)\n",
    "\n",
    "# Retrieve a single image from the dataset\n",
    "image, label = train_dataset[187]\n",
    "image_size = tuple(image.squeeze().size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dcb1db8b-7720-46c1-8672-1600f4a42cc4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T05:31:04.710749Z",
     "start_time": "2024-06-19T05:31:04.706699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  2 \n",
      "Pixel values:  [[[53, 67], [74, 60]]]\n"
     ]
    }
   ],
   "source": [
    "# Change pixel values from tensor to list\n",
    "pixel_vals = (image * 255).round().to(torch.uint8)\n",
    "pixel_vals = pixel_vals.tolist()\n",
    "print(\"Label: \", label, \"\\nPixel values: \", pixel_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db522489-5315-4fb4-bd12-8ac17dbae4bc",
   "metadata": {},
   "source": [
    "#### Building the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cac55161-c396-4c75-b9ff-1eb301ed2373",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-19T05:31:05.278631Z",
     "start_time": "2024-06-19T05:31:04.711653Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1792.5x869.556 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding = INEQR(image_size, pixel_vals).ineqr()\n",
    "embedding.draw(\"mpl\", style=\"iqp\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}