image-tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "configrc.store_credentials:WARNING:2023-01-28 15:09:33,029: Credentials already present. Set overwrite=True to overwrite.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import math\n",
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, execute, Aer, IBMQ\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit_braket_provider import AWSBraketProvider, BraketLocalBackend\n",
    "from dotenv import load_dotenv\n",
    "# Load the API key from the .env file\n",
    "load_dotenv()\n",
    "IBMQ.save_account(os.getenv('IBM_TOKEN'))\n",
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.transform import resize\n",
    "import imageio.v2 as image\n",
    "from numpy import linalg as LA\n",
    "import numpy as np\n",
    "\n",
    "def encode_image(filepath, qubits):\n",
    "    # Load the image data into a numpy array\n",
    "    if filepath.endswith(\".png\") or filepath.endswith(\".jpg\"):\n",
    "        image_data = image.imread(filepath)\n",
    "    else:\n",
    "        image_data=np.load(filepath)\n",
    "    # Convert the image to grayscale\n",
    "    image_data = np.dot(image_data[...,:3], [0.299, 0.587, 0.114])\n",
    "    # Normalize the image\n",
    "    image_data = image_data / LA.norm(image_data)\n",
    "    # Resize the image\n",
    "    image_data = resize(image_data, (int(np.sqrt(qubits)), int(np.sqrt(qubits))), anti_aliasing=True)\n",
    "    # Flatten the image\n",
    "    image_data = image_data.flatten()\n",
    "    # Normalize the image\n",
    "    image_data = image_data / LA.norm(image_data)\n",
    "    return image_data\n",
    "\n",
    "def encode_images(path=\"/home/josh/Repos/Logos/\", qubits=16):\n",
    "    encoded_images = {}\n",
    "    files = os.listdir(path)\n",
    "    for file in files:\n",
    "        if file.endswith(\".png\") or file.endswith(\".jpg\"):\n",
    "            encoded_images[file] = encode_image(path + file, qubits)\n",
    "    return encoded_images\n",
    "\n",
    "def decode_image(image, qubits, images=None, path=\"/home/josh/Repos/Logos/\"):\n",
    "    if images is None:\n",
    "        images = encode_images(path, qubits)\n",
    "    for img in images:\n",
    "        if np.array_equal(images[img], image):\n",
    "            return img\n",
    "    return \"Image not found\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"/home/josh/Repos/Logos/\"\n",
    "qubits = 16\n",
    "images = encode_images(path, qubits)\n",
    "len(images)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = \"/home/josh/Repos/Logos/Wallpaper.jpg\"\n",
    "image_data = encode_image(fp, qubits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Wallpaper.jpg'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode_image(image_data, qubits, images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_circuit(qubits, image_data):\n",
    "    # Create a quantum circuit\n",
    "    qr = QuantumRegister(qubits)\n",
    "    cr = ClassicalRegister(qubits)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    # Initialize the quantum circuit\n",
    "    qc.initialize(image_data, qr)\n",
    "    # Add the measurement\n",
    "    qc.measure(qr, cr)\n",
    "    return qc\n",
    "\n",
    "qc = get_circuit(4, image_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">      »\n",
       "q0_0: »\n",
       "      »\n",
       "q0_1: »\n",
       "      »\n",
       "q0_2: »\n",
       "      »\n",
       "q0_3: »\n",
       "      »\n",
       "c0: 4/»\n",
       "      »\n",
       "«      ┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐»\n",
       "«q0_0: ┤0                                                                                                                                                     ├»\n",
       "«      │                                                                                                                                                      │»\n",
       "«q0_1: ┤1                                                                                                                                                     ├»\n",
       "«      │  Initialize(0.010589,0.034829,0.035161,0.011702,0.12665,0.51696,0.48561,0.11636,0.14616,0.49077,0.42398,0.10129,0.032945,0.073937,0.070474,0.030092) │»\n",
       "«q0_2: ┤2                                                                                                                                                     ├»\n",
       "«      │                                                                                                                                                      │»\n",
       "«q0_3: ┤3                                                                                                                                                     ├»\n",
       "«      └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘»\n",
       "«c0: 4/════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════»\n",
       "«                                                                                                                                                              »\n",
       "«      ┌─┐         \n",
       "«q0_0: ┤M├─────────\n",
       "«      └╥┘┌─┐      \n",
       "«q0_1: ─╫─┤M├──────\n",
       "«       ║ └╥┘┌─┐   \n",
       "«q0_2: ─╫──╫─┤M├───\n",
       "«       ║  ║ └╥┘┌─┐\n",
       "«q0_3: ─╫──╫──╫─┤M├\n",
       "«       ║  ║  ║ └╥┘\n",
       "«c0: 4/═╩══╩══╩══╩═\n",
       "«       0  1  2  3 </pre>"
      ],
      "text/plain": [
       "      »\n",
       "q0_0: »\n",
       "      »\n",
       "q0_1: »\n",
       "      »\n",
       "q0_2: »\n",
       "      »\n",
       "q0_3: »\n",
       "      »\n",
       "c0: 4/»\n",
       "      »\n",
       "«      ┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐»\n",
       "«q0_0: ┤0                                                                                                                                                     ├»\n",
       "«      │                                                                                                                                                      │»\n",
       "«q0_1: ┤1                                                                                                                                                     ├»\n",
       "«      │  Initialize(0.010589,0.034829,0.035161,0.011702,0.12665,0.51696,0.48561,0.11636,0.14616,0.49077,0.42398,0.10129,0.032945,0.073937,0.070474,0.030092) │»\n",
       "«q0_2: ┤2                                                                                                                                                     ├»\n",
       "«      │                                                                                                                                                      │»\n",
       "«q0_3: ┤3                                                                                                                                                     ├»\n",
       "«      └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘»\n",
       "«c0: 4/════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════»\n",
       "«                                                                                                                                                              »\n",
       "«      ┌─┐         \n",
       "«q0_0: ┤M├─────────\n",
       "«      └╥┘┌─┐      \n",
       "«q0_1: ─╫─┤M├──────\n",
       "«       ║ └╥┘┌─┐   \n",
       "«q0_2: ─╫──╫─┤M├───\n",
       "«       ║  ║ └╥┘┌─┐\n",
       "«q0_3: ─╫──╫──╫─┤M├\n",
       "«       ║  ║  ║ └╥┘\n",
       "«c0: 4/═╩══╩══╩══╩═\n",
       "«       0  1  2  3 "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0010': 1, '0100': 16, '0001': 2, '0111': 12, '1011': 8, '1110': 8, '0101': 279, '1010': 179, '1101': 3, '0110': 247, '1000': 15, '1001': 254}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the circuit on the local simulator\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "job = execute(qc, backend, shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts(qc)\n",
    "print(counts)\n",
    "plot_histogram(counts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}