QC_QHED.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aecbb0df-cf48-4739-80c7-c2fcb166bce0",
   "metadata": {},
   "source": [
    "**QHED FOR SMALL IMAGES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73eab714-ea2c-46a4-990f-69ebeacd73b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Importing standard Qiskit libraries \n",
    "from qiskit import *\n",
    "from qiskit import IBMQ\n",
    "from qiskit.compiler import transpile, assemble\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('dark_background')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8489fea-443d-4a76-825a-01eec13b66a0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# representing a binary image(8x8) in form of a numpy array\n",
    "\n",
    "img = np.array([  [0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                  [0, 1, 1, 1, 1, 0, 0, 0],\n",
    "                  [0, 1, 1, 1, 1, 0, 0, 0],\n",
    "                  [0, 1, 1, 1, 1, 1, 1, 0],\n",
    "                  [0, 1, 1, 1, 1, 1, 1, 0],\n",
    "                  [0, 0, 0, 1, 1, 0, 0, 0],\n",
    "                  [0, 0, 0, 1, 1, 0, 0, 0],\n",
    "                  [0, 0, 0, 0, 0, 0, 0, 0] ])\n",
    "\n",
    "\n",
    "# plot image function\n",
    "\n",
    "def plot_image(Image, title):\n",
    "    plt.title(title)\n",
    "    plt.xticks(range(Image.shape[0]))\n",
    "    plt.yticks(range(Image.shape[1]))\n",
    "    plt.imshow(Image, extent=[  0,Image.shape[0], Image.shape[1],0,], cmap='hot')\n",
    "    plt.show()\n",
    "    \n",
    "plot_image(img, 'Initial image')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "66c4a37e-14a3-4c34-8636-de00cc5ce4fb",
   "metadata": {},
   "source": [
    "**QPIE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4321aeb0-4e8d-4ee5-8df3-baa2cd3682da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Horizontal image normalized coefficients [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.         0.         0.         0.         0.20412415\n",
      " 0.20412415 0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.20412415 0.20412415 0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.         0.         0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.         0.         0.20412415 0.20412415 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n",
      "\n",
      "\n",
      "vertical image normalized coefficients [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.         0.         0.         0.         0.20412415\n",
      " 0.20412415 0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.20412415 0.20412415 0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.         0.         0.20412415 0.20412415 0.20412415\n",
      " 0.20412415 0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.20412415 0.20412415 0.         0.         0.\n",
      " 0.         0.         0.         0.20412415 0.20412415 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n",
      "\n",
      "size of 1d array (64,)\n",
      "size of 1d array (64,)\n"
     ]
    }
   ],
   "source": [
    "#we encode the intensities of image pixels as probability amplitudes of quantum states\n",
    "#we use normalization for this\n",
    "#we normalize the amplitudes to lie in the range (0,1)\n",
    "\n",
    "# Convert the raw pixel values to probability amplitudes\n",
    "def amplitude_encode(img_data):\n",
    "    \n",
    "    # Calculate the RMS value\n",
    "    rms = np.sqrt(np.sum(np.sum(img_data**2, axis=1)))\n",
    "    \n",
    "    # Create normalized image\n",
    "    image_norm = []\n",
    "    for arr in img_data:\n",
    "        for ele in arr:\n",
    "            image_norm.append(ele / rms)\n",
    "        \n",
    "    # Return the normalized image as a numpy array\n",
    "    return np.array(image_norm)\n",
    "\n",
    "\n",
    "#we now normalize image and get coefficients in both horizontal and vertical directions\n",
    "\n",
    "# Horizontal: Original image\n",
    "h_norm_image = amplitude_encode(img)\n",
    "print(\"Horizontal image normalized coefficients\",h_norm_image)\n",
    "\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "# Vertical: Transpose of Original image\n",
    "v_norm_image = amplitude_encode(img.T)\n",
    "print(\"vertical image normalized coefficients\",v_norm_image)\n",
    "\n",
    "print()\n",
    "print(\"size of 1d array\",h_norm_image.shape)\n",
    "print(\"size of 1d array\",v_norm_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25043251-bf58-41fa-87cb-d5570450afcd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n",
      "\n",
      "\n",
      "\n",
      "shift Amplitude permutation matrix by 1 unit to the right column wise\n",
      "\n",
      "[[0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [1. 0. 0. ... 0. 0. 0.]]\n",
      "\n",
      "we will later use this for while applying hadamard operation to image coefficient vectors\n",
      "\n",
      "(128, 128)\n"
     ]
    }
   ],
   "source": [
    "#we require N=log(8*8) qubits \n",
    "#N=6\n",
    "\n",
    "data_q = 6\n",
    "ancillary_q = 1\n",
    "total_q = data_q + ancillary_q\n",
    "\n",
    "# Initialize the amplitude permutation unitary\n",
    "Amp_permutation_unitary = np.identity(2**total_q)\n",
    "print(Amp_permutation_unitary)\n",
    "Amp_permutation_unitary=np.roll(Amp_permutation_unitary,1,axis=1)\n",
    "print()\n",
    "print()\n",
    "print()\n",
    "print(\"shift Amplitude permutation matrix by 1 unit to the right column wise\")\n",
    "print()\n",
    "print(Amp_permutation_unitary)\n",
    "print()\n",
    "print(\"we will later use this for while applying hadamard operation to image coefficient vectors\")\n",
    "print()\n",
    "print(Amp_permutation_unitary.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b587e8f5-d787-4374-be4f-3ecd75667a54",
   "metadata": {},
   "source": [
    "**Applying Hadamard Operation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "839ea319-940b-4df8-b46a-233befb739ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 872.774x618.722 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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98rgk6aL23ZTUb6KV8YAL5raFVpJ69+6tZcuW1VlfVFSk3NxceXh4qE+fPhYkO7P7xj6ji9t314p1L2l9znIF+gdr3OW/1e0//7NLnsIAAHBvH65/Rd98t6bWutc+ekSSFBd+JYUWxnPrQtuQrVu3qrq6WlFRUWrduu4nQ99++21J0rZt22otd+/eXf3793d5Pk8PT9145TTdeOU0l18LAOD+nrrvU6sjAC7VIgvt5s2bJTU83eDGG2+sd/n222/Xa6+95tJsAAAAOD8U2npUV1c3ZRwAAABcgBY5IfNshRYAAADmaJEjtBkZGVZHAAAAgJO0yBFaAAAAuA8KLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoLaLQOhwOpaamKiIiQr6+vurSpYumTp2q4uJiTZo0STabTfPmzbM6Zo1FGTP15zdu1MSZ4UpKsem2J7pbHQkAYLC8gzv02keP6rfPDdYNj3XU2D8G6J6/9dW/Vv1VJWXFVscDLpiX1QFcbePGjRo1apTsdrv8/f0VHR2t/Px8paWlaffu3Tp8+LAkqW/fvtYG/YlXP3hYAa2DFHnJZSouOWJ1HACA4T7MelVLvnheQ6LH6qpLJ8jT01ubdq/Wax/+UZ9telNpv/1Krbz9rI4JNJpbF1qHw6ExY8bIbrdr2rRpmjFjhgICAiRJs2fP1vTp0+Xl5SWbzaa4uDiL0/7X67/frU4dwiVJk+f2UUlZkcWJAAAmGxZ7g25JeEj+fm1r1o0Zcq8uCY7UwlV/1QfrX9G4y5MtTAhcGLeecjBlyhTl5eUpOTlZc+fOrSmzkpSamqr4+HhVVFSoe/fuCgwMtDBpbT+WWQAAnKFnl/61yuyPRsSPlyTl2rc0dSTAqdy20Obk5Cg9PV3BwcGaOXNmvfv069dPkhQfH1+z7u2339YvfvELdevWTa1bt1avXr30hz/8QUVFjJICANzLwaN5kqT2bS62OAlwYdy20C5atEhVVVWaMGGC2rRpU+8+fn6n5gv9tNDOnTtXnp6eeuKJJ/TBBx/ovvvu0wsvvKCrr75aVVVVTZIdAABXq6yq1L8++Ys8Pbw08tJbrY4DXBC3nUObkZEhSUpISGhwn7y8U/8y/WmhXbp0qTp27FizfOWVV6pjx46aMGGCPv/8cw0fPtxFiQEAaDovLLlf2/Z+qTtHPaEuF/W0Og5wQdy20O7du1eS1K1bt3q3V1RUKDMzU1LtQvvTMvuj/v37S5L279/fqCz9+/eX3W6vWfbx8tNLyTsbdS4AgLkioyJVVlHi9POe7/vKax8+ovcz5+maQXfrlpEPOT2PO3HVPUNdISEh2rBhQ6OOddtCW1x86rl6JSX1/xCmp6fL4XAoICBAYWFhZzzX6tWrJUm9e/duVBa73V6rDPt6t27UeQAAZivIz1dp+Qmnn/d83lde//gx/WvV4/r5gDs09RcvOj2Lu3HVPYNzuW2hDQkJUWFhobKzszVkyJBa2woKCpSSkiJJiouLk81ma/A8+/fv1yOPPKKrr7660c+qDQkJqbXs48Wz/gCgJerUubPLRmjPxesfP6Y3Vv5JSf1u1wM3vHzG9z+c4qp7hrpO70vnw20LbWJionJycjRr1iwlJSUpKipKkpSVlaWJEyfK4XBIOvMXKhQVFem6666Tj4+PXn311UZnOX34vLJMWp3W6NMBAAy1c8dOefo4/7zn8r7yxso/642Vf1LiZRP14E2vysPDbT8X7lSuumdwLrcttKmpqVq4cKH27dunmJgY9erVS6Wlpdq1a5dGjRql7t2766OPPqo1f/anSkpKNGbMGO3Zs0dr165Vp06dmiz7yq/f0IHCU3OAjxQfVEVlmf71yeOSpIvad1NSv4lNlgUAYL73M5/X6x/P0EXtuuqyyERl/Gdhre3tAy5Wv6gki9IBF85tC21oaKjWrl2rlJQUrVmzRrm5uYqOjtb8+fM1efJk9ejRQ5LqLbTl5eW64YYbtGHDBq1atUrR0dFNmv3D9a/om+/W1Fr32kePSJLiwq+k0AIAzsv2fVmSpANHvtfs9NvrbI8Lv5JCC6PZqqurq60O0dSKiooUGBgom82m48ePq3Xr/06mr6qq0s0336wlS5ZoxYoVGjlypNOvz5QDAGiZEqbIsikHaBxX3TM4l9uO0J7J1q1bVV1draioqFplVpJ+85vf6K233tLvf/97tW7dWl999VXNth49etT7WC8AAABYp0XOCN+8ebOk+qcbfPDBB5KkJ598UkOGDKn13/Lly5s0JwAAAM6uRY7QnqnQ5ubmNnEaAAAAXAhGaAEAAGC0FjlCm5GRYXUEAAAAOEmLHKEFAACA+6DQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYLQWUWgdDodSU1MVEREhX19fdenSRVOnTlVxcbEmTZokm82mefPmWR0TAIAWYfa/f6WkFJvVMeBGvKwO4GobN27UqFGjZLfb5e/vr+joaOXn5ystLU27d+/W4cOHJUl9+/a1Nuj/yzu4Q59kL9DXOz5WwaHdKqsoVaegHhoef6OuH3a//Hz8rY4IADDMvgPbteCTP2vn/mwdOpavyspyXdSuqwb2Gq0bR6SoQ2CnWvt/lPWa5r55hx686R/6+YBf1Tmf/XCuJs4MU1K/25V682tOyZi55T3tzt+oX/7sMaecDy2LW4/QOhwOjRkzRna7XdOmTVNBQYGys7Nlt9s1a9YsLV++XFlZWbLZbIqLi7M6riTpw6xX9c7ap9W5Qw/dlvioJl8zR10u6qnXPvyj7p83VCfLS6yOCAAwzMGjeTp8rECX9/kfTRo1U/eNfUaXRSVp+bqX9Otn+6mw6ECT5nngxr9r+RO1388yt7ynN1b+qUlzwH249QjtlClTlJeXp+TkZM2dO7fWttTUVC1cuFCbNm1SWFiYAgMDLUpZ27DYG3RLwkPy92tbs27MkHt1SXCkFq76qz5Y/4rGXZ5sYUIAgGkui7xKl0VeVWd9bNhwPb7gJn2c9ZrGJ6Q2WR4vT2/J07vJrldRWa6qqkr5ePs22TXRtNx2hDYnJ0fp6ekKDg7WzJkz692nX79+kqT4+PiadWvXrlViYqI6deqkVq1aKTQ0VOPHj1dOTk6T5O7ZpX+tMvujEfHjJUm59i1NkgMA4P4ubt9NklRUUnhB57EfzlVSik2vf/yYvtq2TL95doBGP+Sr8X/upJeWpaiysqLW/qfPoZ32wgit/PqfkqSkFFvNfx9lvSZJ+v7At0p759e6a26Mxv4xQNc+3Fq/fqafVqx7uU6W1z9+TEkpNuXat+rFJQ/olsdDdc1Dvvrmu890w2MdNXXe5fX+P7z56Rwlpdj0zXefXdCfBazhtiO0ixYtUlVVlSZMmKA2bdrUu4+fn5+k2oW2sLBQsbGxuueee3TRRRcpLy9PM2fO1JAhQ7RlyxaFhoY2Sf7THTyaJ0lq3+ZiS64PADBfWXmpSsqKVFZeqr0/bNPLK6ZLkgb2Gu2U86//doWWfvG/umbIvbp6wJ36Yuv7emvNXLXxa69br3q4weNuveoPqq6u0uY9azX95jdq1sd0HypJ2rT7U23+7jMN6n2tQoLCVFpWrM++eUtPvz1ZR4sP6paRD9U558yFE9TK2083DJ8mm82mi9t3U1K/2/X2Z09p34Ht6nJRz1r7f5j1qkI7RikufLhT/izQtNy20GZkZEiSEhISGtwnL+9USfxpoR07dqzGjh1ba78BAwaoZ8+eWrx4saZOneqCtGdWWVWpf33yF3l6eGnkpbc2+fUBAO5hxfqX9fx7v61ZDmnfXb+/ZYFiw4c55fx77Vv19we3KiSouyTp2iH36u6nYvV+5nNnLLT9opK0Kvtf2rxnrRL73VZne2K/iRoz5N5a634x7HdKmT9S/179pG688sFT0xh+oo1fO82++xN5ev636lwz+G69/dlT+nD9K5p87eya9Vv2ZGrfgW911+hZjfnfRjPgtoV27969kqRu3brVu72iokKZmZmSahfa+nTo0EGS5OXVuD+u/v37y2631yz7ePnppeSd53z8C0vu17a9X+rOUU/U+RclAMAckVGRKqtw/od7z/V95fKYcerasZdKyoq0a/9/9OW2JTpa7HBajqF9xtWUWUmy2WyKj0jQ+5nzVHKySH6t6v+N6dn89Ak/ZeWlKi0rVrWq1S/qZ/rmuzXad+BbhXWKrXXM9cPur1VmJf3/COyVWvn167pz1BM12z/MekWeHl76Wf/b61zbVfcMdYWEhGjDhg2NOtZtC21xcbEkqaSk/h/C9PR0ORwOBQQEKCwsrM72yspKVVVVae/evXrooYcUEhKim266qVFZ7Ha79u/fX7Ps6936nI997cNH9H7mPF0z6O56f6UCADBHQX6+SstPOP285/q+0rFdqDq2OzV17vI+4zQs9hdKThugk+UnGvUeY7PVfpZsp6DwOvsEtj41KHTsxKFGF9qSk0V6/ePHtOabN3XwyL4624/XMwc4tGNUvee6ZvDdmrlwgr7KWabL+4zTidLjWrPpTQ3ufa3aB9Sd1ueqewbncttCGxISosLCQmVnZ2vIkCG1thUUFCglJUWSFBcXV+cvpCRdeeWVNSO4ERERysjIUMeOHRud5ad8vPzO6bjXP35M/1r1uH4+4A5N/cWLjbo2AKD56NS5s8tGaBsjvHOcelxyqZZ+8b+1Cm0r71PnO9lAkSstK6613488PDwbvFZ1dXWjMkrSEwtv1bqcZRo96G7FhQ9XQOsO8rR5av23K7R47dOqrq6qc0yrBkr+FbG/UGDrKfpw/Su6vM84fbopXaVlxRo16K5693fVPUNdp/el8+G2hTYxMVE5OTmaNWuWkpKSFBV16l9qWVlZmjhxohyOU79iaegLFV555RUdOXJEe/bs0Zw5c/Szn/1MmZmZ6tq163lnOX34vLJMWp125mNe//gxvbHyT0rqd7seuOHleks3AMAsO3fslKeP8897Lu8rDSkrL9HxE4drrQsJOvWby+8P1P+Enx/X/7ifMzT0PldUckTrcpYp8bKJuv+0wZ3sXZ+c93V8vFopqd8v9W5mmhxH8/Xh+lcU3PYS9e95db37u+qewbnc9rFdqamp6tChg/bt26eYmBjFxsYqMjJSAwcOVHh4uEaOHCmp4fmzPXv21KBBg3TzzTdr1apVOn78uGbPnl3vvs72xso/642Vf1LiZRP14E2vysPDbW8TAKAJHD5mr3f9xl2rlWvfol7dBtdaH3nJZerYros+3fhvOY7m19pWXlGm9zPnyWazaUh07Q9RX4gfpyMcO61ce9hOjfqePsJ76FiBPqjnsV3nYtSgyaqqqtTLK6Yr5/uv9LP+v5LnGUaX0fy57QhtaGio1q5dq5SUFK1Zs0a5ubmKjo7W/PnzNXnyZPXo0UPS2T8QJknt2rVTRESEdu3a5erYej/zeb3+8Qxd1K6rLotMVMZ/Ftba3j7gYvWLSnJ5DgCA+0h75z4dOl6gvhEjdXG7biqrKNXOvK/16aZ/y69VgO659qla+3t6emnq9S/osX/+j+75W5yuHjhJnTv0UOHxH7RmU7pyf9iqW0Y+7NQPKvfuOljvZ87Tc+/8WgN7XyMvT2/16jpInYLC1C/yZ1r1nwVq5e2nqC4DdKBwr5Z/NV8hQWE6duLQeV+r28W91SfsCq3KXiCbzaarB9zptP8PWMNtC60k9e7dW8uWLauzvqioSLm5ufLw8FCfPn3Oep4DBw5o+/btGjRokCti1rJ9X9apax75XrPT637aMi78SgotAOC8JFx6i1Z+/bpWff2GjhQflE2nnst6zeB7dNOVKbqofd3pdIN6X6NnfpOp9E9na+WGf+rYiUPy9fFXxCWX6o9J6boyvnEflG4wY99btGv/f/Tppn/rs2/eUlV1lR686R/qFBSm39+6QC+v+L2+zFmqj7/+py4JjtQdo/4qTw9vzX3zjkZdb/Sgu7Vlz+eK75GgTh3qfpgNZrFVX8gsbUOtW7dOgwcPVs+ePfXtt9/W2nbbbbcpIiJCffv2Vbt27bRz5049/fTTKigoUFZWliIiIi74+hcy1wkAYK6EKWp2c2hbqjWb3tTjC8broVsXauSltzS4n6vuGZzLrUdoG7J582ZJ9U83GDx4sF5//XU9++yzKi0tVZcuXZSQkKCHH364wWfaAgAAs7z/xfNq6x+sK2KvtzoKnIBCe5rk5GQlJyc3dSQAAOBihUUH9J+dq7Rlz1pt/u4zTRo1Uz5erayOBSeg0AIAgBbh+x+2aebCW9XGr52uHXyvbrhymtWR4CQtstBmZGRYHQEAADSx+B4jtHJOi/voUIvAA04BAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRZAk5j2wghNe2FEo49f+uWL+lmqhw4dK3BeKACAW6DQAjDCkOixkqSvti21OAkAoLmh0AIwQnDbzooK7a/Mre9ZHQUA0MxQaAEYY2jMOG3claGSk0VWRwEANCMUWgCW+vWz/XXnnN41y39+/QZd/2hQzfJz7yYrKcWmopIjGhpzncorTipr+4dWRAUANFMUWgCWqays0F77VvXo3Ldm3a79/1F45/ia5d35GxXSvrva+LVT95AYdQ6O0Bdb3mv6sACAZotCC8Ay+w5uV1lFaU2hLS49Jnvhnprl6upq7Sn4Rj0uubTmmKHR12n9tytUWVlhQWIAQHNEoQVgmd35GyWppsB+l79J1dXVNcsFh77TiZPHa43gDo25TsdLCvXNns+aNiwAoNmi0AKwzHf5myRJEf9fWH8suBGdL621/NNC6+HhKUmqqqpskowAgOaPQgvAMrsLNikoIETtAy4+tZy/Ud5erdT14t412yUp4idTDr7Y8p78fdsqvseIJs8LAGieKLQALLP/4A51COxcs7w7f6O6XRwtL09vSVLWtx+oY7suuqhdl5p9vtj2vgb2Gl2zDwAAFFoAlqmsqtCxE4dUXV2tysoK5f7w3ycebNj+sXbkbdBVl06o2X/vDznKO7hDQ2OusygxAKA5otACsEx8jwT9ULhXzy6+V59veVflFSfVxred3l7zN/1lwY0K7Rilm0c+VLP/l1vfl7enjwb0GmVhagBAc+NldQAALde9Y/+mwqIftHzdS1q+7iVJ0uK1T8vHy1ejBt2lXyY9Jn/fwJr9v9j6vuJ7JNRaBwAAhRaAZdr6B+vJyR8p37Fbz737G32982M9edfH6hN2hXy8fWvte/iYXd/uW6ffjnveorQAgOaKQgvAcp2De6iiskydgsJ1WVRivft8uW2JJGkI82cBAKdhDi2AZmF3waZaz5s93dUD7tSyJ0oU3LZzg/sAAFomRmgBWO7gkTwdP3H4jIXW09NLnrxkAQDq4fYjtA6HQ6mpqYqIiJCvr6+6dOmiqVOnqri4WJMmTZLNZtO8efOsjllj34Htmrlwgu6c01vXPdJW1z7cWnfO7qUXlzygQ8cKrI4HNNpT932qp+77tN5tHduFauWcak1I/GPThgJaqNKyE5o4M1xJKTY9926y1XGAC+bWwx0bN27UqFGjZLfb5e/vr+joaOXn5ystLU27d+/W4cOHJUl9+/a1NuhPHDyap8PHCnR5n/9Rx7ah8vTw0h77Zi1f95JWb/q3XvzdRrVvc5HVMQEABvvnR4/qaPFBq2MATuO2hdbhcGjMmDGy2+2aNm2aZsyYoYCAAEnS7NmzNX36dHl5eclmsykuLs7itP91WeRVuizyqjrrY8OG6/EFN+njrNc0PiHVgmQAAHewMy9b73z+jCaPnq35y6ZZHQdwCredcjBlyhTl5eUpOTlZc+fOrSmzkpSamqr4+HhVVFSoe/fuCgxs/s+0vLh9N0lSUUmhxUkAAKaqrKrU029P1oCeV+uK2OutjgM4jVsW2pycHKWnpys4OFgzZ86sd59+/fpJkuLj4xs8z6hRo2Sz2fTYY4+5IuYZlZWX6mixQweP5GnD9o/1zOJ7JEkDe41u8iwAAPfwzmdPa9+Bb5U8rvl8dgRwBreccrBo0SJVVVVpwoQJatOmTb37+Pn5SWq40L755pvauHGjqyKe1Yr1L+v5935bsxzSvrt+f8sCxYYPsywTAMBcBYf36PWPZ2hC0qMKCeou++FcqyMBTuOWhTYjI0OSlJCQ0OA+eXl5kuovtMeOHdP999+vuXPn6rbbbnNNyLO4PGacunbspZKyIu3a/x99uW2JjhY7LMkCADDfs4vvVUiHcN0w/AGrowBO55aFdu/evZKkbt261bu9oqJCmZmZkuovtH/4wx8UFRWlCRMmOKXQ9u/fX3a7vWbZx8tPLyXvPOMxHduFqmO7UEnS5X3GaVjsL5ScNkAny0/olpEPXXAmAEDTi4yKVFlFidPPe7b3lU++XqDsnSv1t/s+k5ent9Ov785cdc9QV0hIiDZs2NCoY92y0BYXF0uSSkrq/wFMT0+Xw+FQQECAwsLCam3bsGGD/v73v+vrr792Wh673a79+/fXLPt6tz7vc4R3jlOPSy7V0i/+l0ILAIYqyM9XafkJp5/3TO8rZRUnNX/pAxrYa7TaB4Rov2OXJMlx9NT7UnHpUe137FJb/2C18Wvn9Gymc9U9g3O5ZaENCQlRYWGhsrOzNWTIkFrbCgoKlJKSIkmKi4uTzWar2VZZWal77rlHycnJiomJcWqen/Lx8mvUecrKS3T8xGFnRAIAWKBT584uG6FtSFl5iY4UH9S6nOVal7O8zvZV2Qu0KnuB7r5mjm4c8aDTs5nOVfcMdZ3el86HWxbaxMRE5eTkaNasWUpKSlJUVJQkKSsrSxMnTpTDcWou6ulfqDBv3jz98MMPTn+qwenD55Vl0uq0+vc9fMyuoMC6N3TjrtXKtW9RXI8RTs0GAGg6O3fslKeP8897pvcVXx9/PTLxrTrrjxYdVNq7v9aAnlfr6oGTFN6p+TyTvTlx1T2Dc7lloU1NTdXChQu1b98+xcTEqFevXiotLdWuXbs0atQode/eXR999FGt+bMOh0OPPPKI5s6dq4qKCh05cqRmW2lpqY4cOaLAwEB5eLj2SWdp79ynQ8cL1DdipC5u101lFaXamfe1Pt30b/m1CtA91z7l0usDANyLl6e3hsfdUGf9j0856NShR73bAZO45XNoQ0NDtXbtWl1zzTXy9fVVbm6ugoKCNH/+fC1fvlw7duyQVPsDYXl5eTp+/LjuuecetW/fvuY/SZo1a5bat2+v77//3uXZEy69RW39g7Xq6zf0v0um6pUVv9f2fet1zeB79NID3yjikr4uzwAAAGASW3V1dbXVIZpSUVGRAgMDZbPZdPz4cbVu3bpmfX2frEtISNDtt9+uX/3qVxo8eLB8fX0vOMOZfjUEAHBfCVPU5FMOcGFcdc/gXG455eBMtm7dqurqakVFRdWUWUlq06aNRowYUe8x3bt3b3AbAAAArOWWUw7OZPPmzZLO/JW3AAAAMEeLG6E930LbwmZkAAAAGIcRWgAAABitxY3QZmRkWB0BAAAATtTiRmgBAADgXii0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjOZldQA0H0kpNnUP6aO7Rs/SoN6jlXdwp+ak366jxQ75+7ZVyvjX1D0k5oznON9jmuIazfmY5pSrrLxUf/3Xzdr7wza18vZTuzYXacr1L+iS4IhzPseHWf/QU2/eqcduf1eX9xlHphac6UL2b+wxzvDgiwn6Ln+Tbkt6VNcPu9/l1wPgHIzQopanf71Wg3qPliQ9u/gejR50t16bvkPjE6ZrTvqvznr8+R7TFNdozsc0t1yjB92tf6Ru1/wHNmlIzHX621t3nfM57Idz9cG6v6t318FnzUOmlpGpsfs39hhnmHvvag2OHtsk1wLgPBRa1Kuw6IB25G1Q4mW3SZKGxf5CB4/s037HLqcd0xTXaM7HNLdcPt6+GtR7tGw2mySpd9fB+qEw95zOUVVVpb+9dZd+M+45eXu1ajALmVpOpsbu39hjALRsFFrU6+CRfQoK7CRPz1OzUmw2my5q31UHjnzvtGOa4hrN+ZjmmutH737+rIbEXHdO51j82d8U0/1yRYX2O+M5ydTyMjXlzyyAlos5tADqWLjqCeU7dmn2PavOuu8e+xat3bxYf/v1Z2QiEwBYokUUWofDodmzZ+udd95RXl6eOnbsqOuvv15PPPGEpkyZoldffVXPPfeckpOTrY5ar9KyE5r8VB/ZD+/R2KG/0W//Z57Lr9mxXRcdPlagysoKeXp6qbq6WgcKv9dF7bo67ZimuEZzPqa55nrr07n6fMs7mn33J/L1aX3Wc2TvWKkfCnP1q1mRkqTDx+165u27dfhYgcYMvY9MLTxTU/2c4+ySUmz1rvf18dfSvxY1cRrAudx+ysHGjRsVGxurOXPmyG63Kzo6WuXl5UpLS9P48eOVk5MjSerbt6+1Qc/gnx89qqPFB5v0mu3bXKSISy7TJ9kLJElrNy9WcLvQmk9Nz1r0S32++d0LOqYprtGcj2kuuX7q7TV/0+qNizRr8kq18Wt3TucYM/Q+pT9aoAUP52rBw7nq3XWw7r/hpZpCRKaWlcmqn3Ocm9iwYZp+8xu1/pt24ytWxwIumFuP0DocDo0ZM0Z2u13Tpk3TjBkzFBAQIEmaPXu2pk+fLi8vL9lsNsXFxVmctn4787L1zufPaPLo2Zq/bFqTXvv+X8zXnPRfaVHGE2rtG6iUm/5Rs21H3gaNu2LKBR/TFNdozsc0h1xPvXWXhkSPVeQll2n+smnqFBSuB19MkCT5eLXSc1PWnfUcZ0KmlpXJyp/zH/88hsaMPev6hvZ1dyFB4Ursd5vVMQCns1VXV1dbHcJVbr31Vi1atEjJycl67rnn6mzv27evNm3apLCwMH333XdNlquyTFqddg77VVXqt2kDFRTYScnj5mnizDCXTjlISrHp3T8X1hrdqc+RooOaufBWzbp75Tmf+3yPaYprNOdjmiqXq5Hp3LhLJnf5OZ/9718p4pK+LnkObcIUydPH6ac9p/eVpBSbkvrdrt/d8JIqKsvk16qN84O4IVfdMziX2xbanJwc9enTR0FBQdqzZ4/atKn7F3fSpEl69dVXNW7cOL377qlfX3366adKSEios298fLw2btzolGznWmjf+nSuXv94hv7+4FZJcnmhvelPIWofcLHuHDWz5lm0ANCSPPhign44nKtbrnpYowdNdvr5rS60vj7+KqsoVVVVpdr5d9SVfcfrjp8/Ln+/ts4P5SYotGZw2ykHixYtUlVVlSZMmFBvmZUkPz8/SafK6umef/55XXbZZTXL/v7+rgnagILDe/T6xzM0IelRhQR1l/1wrsuv+eYMu8uvAQDN2dx7V1sdwWV6dRmo4XE3qnNwhE6UHtP6b1fo/cx5+mb3Gj2b/AUjtjCa2xbajIwMSap3tPVHeXl5kuovtNHR0Ro8+Ny+NccVnl18r0I6hOuG4Q9YlgEA4D5+nFP9o6T+v1RYpzj948M/6J3Pn9WEq/5gUTLgwrltod27d68kqVu3bvVur6ioUGZmpqT6C60z9e/fX3b7f0c/fbz89FLyzgb3/+TrBcreuVJ/u+8zeXl6uzQbAKDpREZFqqyixOnnPdv7SkNuGpGiBSv/pPU5yym0DXDVPUNdISEh2rBhQ6OOddtCW1xcLEkqKan/hzA9PV0Oh0MBAQEKCwurs338+PFyOBzq0KGDxo4dqyeffFLBwcGNymK327V///6aZV/v1g3uW1ZxUvOXPqCBvUarfUBIzVc9Oo6eOr649Kj2O3aprX/wWT+8BQBoXgry81VafsLp5z3T+8qZeHl6q0NgZx0tdjg5kftw1T2Dc7ltoQ0JCVFhYaGys7M1ZMiQWtsKCgqUkpIiSYqLi6v5/nNJatu2rVJSUjR8+HC1adNGX375pWbOnKmvvvpKGzZskK+vb6Oy/JSPl1+D+5aVl+hI8UGty1mudTnL62xflb1Aq7IX6O5r5ujGEQ+edxYAgHU6de7sshHaxigrL9XBo3nq3c26KXbNnavuGeo6vS+dD7cttImJicrJydGsWbOUlJSkqKgoSVJWVpYmTpwoh+PUv0ZP/0KFSy+9VJdeemnN8ogRI9SnTx+NHTtWixYt0h133HHeWU4fPj/Tp1F9ffz1yMS36qw/WnRQae/+WgN6Xq2rB05SeKfm+dxcAEDDdu7YaclTDo4VH1Kgf4c661/76BFVVlVocO8xzg/lJlx1z+BcbltoU1NTtXDhQu3bt08xMTHq1auXSktLtWvXLo0aNUrdu3fXRx99dE7zZ6+99lr5+/trw4YNjSq058PL01vD426os/7Hpxx06tCj3u0AADTkX6seV87erxQfkaCL2nVV6ckirf92hTbuXq1eXQdp3BW/tToicEHcttCGhoZq7dq1SklJ0Zo1a5Sbm6vo6GjNnz9fkydPVo8ePSSd3wfCfjo1AQAAU8SHj9DeH7Zp5YZ/6tiJQ/K0eapzcKTuuPqvumH4A/LxPv/pdEBz4raFVpJ69+6tZcuW1VlfVFSk3NxceXh4qE+fPmc9z5IlS1RcXKyBAwe6IuY5CQnqrpVz3PI7MAAALja0z3Ua2uc6q2MALuPWhbYhW7duVXV1taKiotS6de1Pht52220KDw/XZZddVvOhsNmzZ6tv3766+eabLUoMAACAhrTIQrt582ZJ9U83iImJ0cKFC/XMM8+opKREoaGhmjx5smbMmCEfH2aFAwAANDcU2tM89NBDeuihh5o6EgAAABrJw+oAVjhToQUAAIBZWuQIbUZGhtURAAAA4CQtcoQWAAAA7oNCCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNHcvtA6HA6lpqYqIiJCvr6+6tKli6ZOnari4mJNmjRJNptN8+bNszomAAAAGsnL6gCutHHjRo0aNUp2u13+/v6Kjo5Wfn6+0tLStHv3bh0+fFiS1LdvX2uDniYpxVbvel8ffy39a1ETpwEAuItjJw5r0aon9MXW93TwaJ5atwpQ95A+uv1nf1Zs+DCr4wGN5raF1uFwaMyYMbLb7Zo2bZpmzJihgIAASdLs2bM1ffp0eXl5yWazKS4uzuK0dcWGDdPoQXfXWufl6W1RGgCA6X4o3KsHXxihkrIiXT1wkkKDo1RcelTfFXwjx7H9VscDLojbFtopU6YoLy9PycnJmjt3bq1tqampWrhwoTZt2qSwsDAFBgZalLJhIUHhSux3m9UxAABu4slFt6myqkLzH/hGHQI7WR0HcCq3nEObk5Oj9PR0BQcHa+bMmfXu069fP0lSfHx8nW3vvvuuhg4dKn9/f7Vt21aXX365tm7d6tLM9SmvKFPJSaYYAAAuzDfffaYtez7XTSNS1SGwkyoqy1VadsLqWIDTuOUI7aJFi1RVVaUJEyaoTZs29e7j5+cnqW6hTUtL07Rp0/S73/1Of/nLX3Ty5EmtW7dOJSUlLs/9U2s3v61V/1mgqqpKtfPvqCv7jtcdP39c/n5tmzQHAMB8679dIUm6qF1XPfLqGK3f/oGqqip1SXCkbkt8lN8IwnhuWWgzMjIkSQkJCQ3uk5eXJ6l2od29e7dSUlL09NNPKzk5uWb96NGjXZS0fr26DNTwuBvVOThCJ0qPaf23K/R+5jx9s3uNnk3+Qn6t6i/pAADUJ+/AdknS029PVufgSKWO/6fKK8u0eM1TmvXviaqoKtfVA+6wOCXQeG5ZaPfu3StJ6tatW73bKyoqlJmZKal2oX311Vfl7e2tyZMnOzVP//79Zbfba5Z9vPz0UvLOBvd/bsq6WstJ/X+psE5x+seHf9A7nz+rCVf9wan5AABNIzIqUmUVzv+N39neV06cPC5J8msVoLn3rpa3l48k6fKYcfrlk+H6xwcP62f9bpeHh1vORLwgrrpnqCskJEQbNmxo1LFuWWiLi4slqcFpAunp6XI4HAoICFBYWFjN+i+++EI9e/bUggUL9Pjjj2vfvn2KjIzUo48+qltuuaXReex2u/bv/+8nSH29W5/3OW4akaIFK/+k9TnLKbQAYKiC/HyVljt/7urZ3ldaeZ+aZpdw6S01ZVaSAlq315DosVr59evad3C7ul3c2+nZTOeqewbncstCGxISosLCQmVnZ2vIkCG1thUUFCglJUWSFBcXJ5vNVmvb/v379dBDD2nWrFnq0qWLXnnlFd16663q2LGjEhMTG53np3y8/M77HF6e3uoQ2FlHix2NygAAsF6nzp1dNkJ7JsFtQyVJ7QNC6mwL+v8nHhSVFDo9lztw1T1DXaf3pfPhloU2MTFROTk5mjVrlpKSkhQVFSVJysrK0sSJE+VwnCqFp3+hQlVVlYqKivTGG29o3LhxkqSrrrpK27Zt01/+8pdGF9rTh88ry6TVaed3jrLyUh08mqfe3QY3KgMAwHo7d+yUp8/Z9ztfZ3tf6dl1oJZ99aIcR/PqbHMcObWuXZuLnB/MDbjqnsG53HKyTGpqqjp06KB9+/YpJiZGsbGxioyM1MCBAxUeHq6RI0dKqvuEg6CgIEmqVVxtNpsSExO1ZcuWJsl+rPhQvetf++gRVVZVaHDvMU2SAwDgPi6PGafWrQK0KntBrcdBHjpWoMyt7ym0Y5QuCY6wMCFwYdxyhDY0NFRr165VSkqK1qxZo9zcXEVHR2v+/PmaPHmyevToIaluoY2JidG6devqO6VKS0tdnluS/rXqceXs/UrxEQm6qF1XlZ4s0vpvV2jj7tXq1XWQxl3x2ybJAQBwHwGt2+vua+fqmcX3aMpzg/XzAXeqorJMS798QRWVZfrNdc9ZHRG4ILbq6upqq0M0paKiIgUGBspms+n48eNq3fq/E+mXLFmi6667TosXL9b1118v6dQ0hL59+yooKEiffvqpUzKc6VdDX2x5X0u+/F/l2rfo2IlD8rR5qnNwpK6Mv0k3DH9APt6+TskAAGh6CVNkyZSDH63d/I7e/HS2cgs2y+bhod5dh2hi0gz1Cbvc+aHchKvuGZzLLUdoz2Tr1q2qrq5WVFRUrTIrSWPGjNGwYcN0991369ChQ+ratatefvllbd26VStXrmySfEP7XKehfa5rkmsBAFqWYbHXa1js9VbHAJyuxRXazZs3S6r/K29tNpuWLFmi6dOn6+GHH9axY8cUHx+vFStW1My7BQAAQPNCoT1Nu3btNH/+fM2fP78pYwEAAKCR3PIpB2dytkILAAAAs7S4EdqMjAyrIwAAAMCJWtwILQAAANwLhRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoturq6mqrQ7Q01dVSVbnVKQAATc3DW7LZnH9e3ldcx1X3DM5FoQUAAIDRmHIAAAAAo1FoAQAAYDQKLQAAAIxGoQUAAIDRKLQAAAAwGoUWAAAARqPQAgAAwGgUWgAAABiNQgsAAACjUWgBAABgNAotAAAAjEahBQAAgNEotAAAADAahRYAAABGo9ACAADAaBRaAAAAGI1CCwAAAKNRaAEAAGA0Ci0AAACMRqEFAACA0Si0AAAAMBqFFgAAAEaj0AIAAMBoFFoAAAAYjUILAAAAo/0fAnEtfXVzFAIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 872.774x618.722 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the circuit for horizontal scan\n",
    "\n",
    "qc_h = QuantumCircuit(total_q)\n",
    "qc_h.initialize(h_norm_image, range(1, total_q))\n",
    "qc_h.h(0)\n",
    "qc_h.unitary(Amp_permutation_unitary, range(total_q))\n",
    "qc_h.h(0)\n",
    "\n",
    "display(qc_h.draw('mpl', fold=-1))\n",
    "\n",
    "\n",
    "# Create the circuit for vertical scan\n",
    "qc_v = QuantumCircuit(total_q)\n",
    "qc_v.initialize(v_norm_image, range(1, total_q))\n",
    "qc_v.h(0)\n",
    "qc_v.unitary(Amp_permutation_unitary, range(total_q))\n",
    "qc_v.h(0)\n",
    "\n",
    "display(qc_v.draw('mpl', fold=-1))\n",
    "\n",
    "# Combine both circuits into a single list\n",
    "circ_list = [qc_h, qc_v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90922f1a-9b82-400f-9a86-12637d8c2b43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/2187344453.py:6: 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",
      "  print(\"print size is \",state_vector_h.size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print size is  128\n",
      "Horizontal scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Vertical scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1020620726 & -0.1020620726 & \\frac{\\sqrt{6}}{12} & 0 & 0.1020620726 & 0.1020620726 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulating cirucits\n",
    "back = Aer.get_backend('statevector_simulator')\n",
    "results = execute(circ_list, backend=back).result()\n",
    "state_vector_h = results.get_statevector(qc_h)\n",
    "state_vector_v = results.get_statevector(qc_v)\n",
    "print(\"print size is \",state_vector_h.size)\n",
    "from qiskit.visualization import array_to_latex\n",
    "print('Horizontal scan statevector:')\n",
    "display(array_to_latex(state_vector_h, max_size=128))\n",
    "print()\n",
    "print('Vertical scan statevector:')\n",
    "display(array_to_latex(state_vector_v, max_size=128))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8263bcb-286c-4133-b4a9-5b1f605e8ef2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  postprocessing for plotting the output (Classical)\n",
    "\n",
    "# Defining a lambda function for thresholding to binary values\n",
    "# returns true for specified Amplitude values else false\n",
    "threshold = lambda amp: (amp > 1e-15 or amp < -1e-15)\n",
    "\n",
    "# Selecting odd states from the raw statevector and\n",
    "# reshaping column vector of size 64 to an 8x8 matrix\n",
    "h_edge_scan_img = np.abs(np.array([1 if threshold(state_vector_h[(2*i)+1].real) else 0 for i in range(2**data_q)])).reshape(8, 8)\n",
    "v_edge_scan_img= np.abs(np.array([1 if threshold(state_vector_v[(2*i)+1].real) else 0 for i in range(2**data_q)])).reshape(8, 8).T\n",
    "\n",
    "# Plotting the Horizontal and vertical scans\n",
    "plot_image(h_edge_scan_img, 'Horizontal scan output')\n",
    "plot_image(v_edge_scan_img, 'Vertical scan output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a40b97f-4f26-4bb8-ade0-d06135a51cd8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combining the horizontal and vertical component of the result by or operator\n",
    "edge_scan_image = h_edge_scan_img | v_edge_scan_img\n",
    "\n",
    "# Plotting the original and edge-detected images\n",
    "plot_image(img, 'Original image')\n",
    "plot_image(edge_scan_image, 'Edge-Detected image')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "142ccee1-b080-4d79-971e-b011f62be951",
   "metadata": {},
   "source": [
    "**FOR LARGE IMAGE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9780fefc-350b-45f1-b412-fc6314d491d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/bin/xdg-open: 882: www-browser: not found\n",
      "/usr/bin/xdg-open: 882: links2: not found\n",
      "/usr/bin/xdg-open: 882: elinks: not found\n",
      "/usr/bin/xdg-open: 882: links: not found\n",
      "/usr/bin/xdg-open: 882: lynx: not found\n",
      "/usr/bin/xdg-open: 882: w3m: not found\n",
      "xdg-open: no method available for opening '/tmp/tmpq43e8pjp.PNG'\n",
      "/usr/bin/xdg-open: 882: www-browser: not found\n",
      "/usr/bin/xdg-open: 882: links2: not found\n",
      "/usr/bin/xdg-open: 882: elinks: not found\n",
      "/usr/bin/xdg-open: 882: links: not found\n",
      "/usr/bin/xdg-open: 882: lynx: not found\n",
      "/usr/bin/xdg-open: 882: w3m: not found\n",
      "xdg-open: no method available for opening '/tmp/tmph9xqg8yu.PNG'\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from numpy import asarray\n",
    "image = Image.open('cat.png')\n",
    "image.show()\n",
    "new_image = image.resize((32, 32)).convert('1')\n",
    "new_image.save('IMAGE_32.png')\n",
    "new_image.show()\n",
    "imgg=asarray(new_image)\n",
    "print(imgg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "862ae0a8-edc7-4037-93f1-f1519164333b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size= (32, 32)\n"
     ]
    }
   ],
   "source": [
    "def plot_image(Image, title):\n",
    "    plt.title(title)\n",
    "    plt.xticks(range(Image.shape[0]))\n",
    "    plt.yticks(range(Image.shape[1]))\n",
    "    plt.imshow(Image, extent=[  0,Image.shape[0], Image.shape[1],0,], cmap='hot')\n",
    "    plt.show()\n",
    "    \n",
    "plot_image(imgg, 'Initial image')\n",
    "print(\"size=\",imgg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2237fbe0-73ce-45c3-b8a7-9d1c4185ea4a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Horizontal image normalized coefficients [0.03737175 0.03737175 0.03737175 ... 0.03737175 0.03737175 0.03737175]\n",
      "\n",
      "\n",
      "vertical image normalized coefficients [0.03737175 0.03737175 0.03737175 ... 0.03737175 0.03737175 0.03737175]\n",
      "\n",
      "size of 1d array (1024,)\n",
      "size of 1d array (1024,)\n"
     ]
    }
   ],
   "source": [
    "#we encode the intensities of image pixels as probability amplitudes of quantum states\n",
    "#we use normalization for this\n",
    "#we normalize the amplitudes to lie in the range (0,1)\n",
    "\n",
    "# Convert the raw pixel values to probability amplitudes\n",
    "\n",
    "def amplitude_encode(img_data):\n",
    "    \n",
    "    # Calculate the RMS value\n",
    "    rms_32 = np.sqrt(np.sum(np.sum(img_data**2, axis=1)))\n",
    "    \n",
    "    # Create normalized image\n",
    "    image_norm = []\n",
    "    for arr in img_data:\n",
    "        for ele in arr:\n",
    "            image_norm.append(ele / rms_32)\n",
    "        \n",
    "    # Return the normalized image as a numpy array\n",
    "    return np.array(image_norm)\n",
    "\n",
    "\n",
    "#we now normalize image and get coefficients in both horizontal and vertical directions\n",
    "\n",
    "# Horizontal: Original image\n",
    "h_norm_image_32 = amplitude_encode(imgg)\n",
    "print(\"Horizontal image normalized coefficients\",h_norm_image_32)\n",
    "\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "# Vertical: Transpose of Original image\n",
    "v_norm_image_32 = amplitude_encode(imgg.T)\n",
    "print(\"vertical image normalized coefficients\",v_norm_image_32)\n",
    "\n",
    "print()\n",
    "print(\"size of 1d array\",h_norm_image_32.shape)\n",
    "print(\"size of 1d array\",v_norm_image_32.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f1682f7-3147-4cac-9797-db63318f409d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n",
      "\n",
      "\n",
      "\n",
      "shift Amplitude permutation matrix by 1 unit to the right column wise\n",
      "\n",
      "[[0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [1. 0. 0. ... 0. 0. 0.]]\n",
      "\n",
      "we will later use this for while applying hadamard operation to image coefficient vectors\n",
      "\n",
      "(2048, 2048)\n"
     ]
    }
   ],
   "source": [
    "#we require N=log(32*32) qubits \n",
    "#N=10\n",
    "\n",
    "data_q_32 = 10\n",
    "ancillary_q_32 = 1\n",
    "total_q_32 = data_q_32 + ancillary_q_32\n",
    "\n",
    "# Initialize the amplitude permutation unitary\n",
    "Amp_permutation_unitary_32 = np.identity(2**total_q_32)\n",
    "print(Amp_permutation_unitary_32)\n",
    "Amp_permutation_unitary_32=np.roll(Amp_permutation_unitary_32,1,axis=1)\n",
    "print()\n",
    "print()\n",
    "print()\n",
    "print(\"shift Amplitude permutation matrix by 1 unit to the right column wise\")\n",
    "print()\n",
    "print(Amp_permutation_unitary_32)\n",
    "print()\n",
    "print(\"we will later use this for while applying hadamard operation to image coefficient vectors\")\n",
    "print()\n",
    "print(Amp_permutation_unitary_32.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05c415c9-5cfe-4f66-8953-c5902ed4b5ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1475.88x953.167 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1475.88x953.167 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the circuit for horizontal scan\n",
    "\n",
    "qc_h_32 = QuantumCircuit(total_q_32)\n",
    "qc_h_32.initialize(h_norm_image_32, range(1, total_q_32))\n",
    "qc_h_32.h(0)\n",
    "qc_h_32.unitary(Amp_permutation_unitary_32, range(total_q_32))\n",
    "qc_h_32.h(0)\n",
    "\n",
    "display(qc_h_32.draw('mpl', fold=-1))\n",
    "\n",
    "\n",
    "# Create the circuit for vertical scan\n",
    "qc_v_32 = QuantumCircuit(total_q_32)\n",
    "qc_v_32.initialize(v_norm_image_32, range(1, total_q_32))\n",
    "qc_v_32.h(0)\n",
    "qc_v_32.unitary(Amp_permutation_unitary_32, range(total_q_32))\n",
    "qc_v_32.h(0)\n",
    "\n",
    "display(qc_v_32.draw('mpl', fold=-1))\n",
    "\n",
    "# Combine both circuits into a single list\n",
    "circ_list_32 = [qc_h_32, qc_v_32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "224a4383-18fd-4312-9522-4f002a777a84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/2048049645.py:6: 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",
      "  print(\"size is \",state_vector_h_32.size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size is  2048\n",
      "Horizontal scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & \\cdots & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Vertical scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & \\cdots & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0 & 0.0373717546 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulating cirucits\n",
    "back = Aer.get_backend('statevector_simulator')\n",
    "results = execute(circ_list_32, backend=back).result()\n",
    "state_vector_h_32 = results.get_statevector(qc_h_32)\n",
    "state_vector_v_32 = results.get_statevector(qc_v_32)\n",
    "print(\"size is \",state_vector_h_32.size)\n",
    "from qiskit.visualization import array_to_latex\n",
    "print('Horizontal scan statevector:')\n",
    "display(array_to_latex(state_vector_h_32, max_size=128))\n",
    "print()\n",
    "print('Vertical scan statevector:')\n",
    "display(array_to_latex(state_vector_v_32, max_size=128))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "845b8b32-1b36-47bf-baa6-41c1fdffec07",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  postprocessing for plotting the output (Classical)\n",
    "\n",
    "# Defining a lambda function for thresholding to binary values\n",
    "# returns true for specified Amplitude values else false\n",
    "threshold = lambda amp: (amp > 1e-15 or amp < -1e-15)\n",
    "\n",
    "# Selecting odd states from the raw statevector and\n",
    "# reshaping column vector of size 64 to an 8x8 matrix\n",
    "h_edge_scan_img_32 = np.abs(np.array([1 if threshold(state_vector_h_32[2*(i)+1].real) else 0 for i in range(2**data_q_32)])).reshape(32, 32)\n",
    "v_edge_scan_img_32= np.abs(np.array([1 if threshold(state_vector_v_32[2*(i)+1].real) else 0 for i in range(2**data_q_32)])).reshape(32, 32).T\n",
    "\n",
    "# Plotting the Horizontal and vertical scans\n",
    "plot_image(h_edge_scan_img_32, 'Horizontal scan output')\n",
    "plot_image(v_edge_scan_img_32, 'Vertical scan output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "158f4bf6-9c06-4bae-9a32-e47cde592409",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combining the horizontal and vertical component of the result by or operator\n",
    "edge_scan_image_32 = h_edge_scan_img_32 | v_edge_scan_img_32\n",
    "\n",
    "# Plotting the original and edge-detected images\n",
    "plot_image(imgg, 'Original image')\n",
    "plot_image(edge_scan_image_32, 'Edge-Detected image')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a7d74fd4-4fb8-46c4-b4f2-8fd99e4828eb",
   "metadata": {},
   "source": [
    "**FOR RGB IMAGE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b000970-3e53-4827-98f8-ea7ca4696857",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/bin/xdg-open: 882: www-browser: not found\n",
      "/usr/bin/xdg-open: 882: links2: not found\n",
      "/usr/bin/xdg-open: 882: elinks: not found\n",
      "/usr/bin/xdg-open: 882: links: not found\n",
      "/usr/bin/xdg-open: 882: lynx: not found\n",
      "/usr/bin/xdg-open: 882: w3m: not found\n",
      "xdg-open: no method available for opening '/tmp/tmprqb24vj4.PNG'\n"
     ]
    }
   ],
   "source": [
    "from qiskit import *\n",
    "from qiskit import IBMQ\n",
    "from qiskit.compiler import transpile, assemble\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "import skimage.color\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import math\n",
    "\n",
    "from PIL import Image\n",
    "from numpy import asarray\n",
    "image_o = Image.open('Apple1.jpg').convert('1')\n",
    "image_o.show()\n",
    "new_image_o = Image.open('Apple1.jpg')\n",
    "\n",
    "imggg=asarray(image_o)\n",
    "imggg_=asarray(new_image_o)\n",
    "#print(imggg.shape)\n",
    "#for item in imggg:\n",
    "#    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0090746-17d2-4c0d-935e-e56fee097cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size= (32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "plot_image(imggg_, 'Initial image')\n",
    "print(\"size=\",imggg_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b27dbb73-5e90-4968-9b2c-1f721e1adc55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Horizontal image normalized coefficients [0.04062222 0.04062222 0.04062222 ... 0.04062222 0.04062222 0.04062222]\n",
      "\n",
      "\n",
      "vertical image normalized coefficients [0.04062222 0.04062222 0.04062222 ... 0.04062222 0.04062222 0.04062222]\n",
      "\n",
      "size of 1d array (1024,)\n",
      "size of 1d array (1024,)\n"
     ]
    }
   ],
   "source": [
    "#we encode the intensities of image pixels as probability amplitudes of quantum states\n",
    "#we use normalization for this\n",
    "#we normalize the amplitudes to lie in the range (0,1)\n",
    "\n",
    "# Convert the raw pixel values to probability amplitudes\n",
    "\n",
    "def amplitude_encode(img_data):\n",
    "    \n",
    "    # Calculate the RMS value\n",
    "    rms_32_rgb = np.sqrt(np.sum(np.sum(img_data**2, axis=1)))\n",
    "    \n",
    "    # Create normalized image\n",
    "    image_norm = []\n",
    "    for arr in img_data:\n",
    "        for ele in arr:\n",
    "            image_norm.append(ele / rms_32_rgb)\n",
    "        \n",
    "    # Return the normalized image as a numpy array\n",
    "    return np.array(image_norm)\n",
    "\n",
    "\n",
    "#we now normalize image and get coefficients in both horizontal and vertical directions\n",
    "\n",
    "# Horizontal: Original image\n",
    "h_norm_image_32_rgb = amplitude_encode(imggg)\n",
    "print(\"Horizontal image normalized coefficients\",h_norm_image_32_rgb)\n",
    "\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "# Vertical: Transpose of Original image\n",
    "v_norm_image_32_rgb = amplitude_encode(imggg.T)\n",
    "print(\"vertical image normalized coefficients\",v_norm_image_32_rgb)\n",
    "\n",
    "print()\n",
    "print(\"size of 1d array\",h_norm_image_32_rgb.shape)\n",
    "print(\"size of 1d array\",v_norm_image_32_rgb.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30ed9f91-ff46-4258-9530-701963441275",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n",
      "\n",
      "\n",
      "\n",
      "shift Amplitude permutation matrix by 1 unit to the right column wise\n",
      "\n",
      "[[0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [1. 0. 0. ... 0. 0. 0.]]\n",
      "\n",
      "we will later use this for while applying hadamard operation to image coefficient vectors\n",
      "\n",
      "(2048, 2048)\n"
     ]
    }
   ],
   "source": [
    "#we require N=log(32*32) qubits \n",
    "#N=10\n",
    "\n",
    "data_q_32_rgb = 10\n",
    "ancillary_q_32_rgb = 1\n",
    "total_q_32_rgb = data_q_32_rgb + ancillary_q_32_rgb\n",
    "\n",
    "# Initialize the amplitude permutation unitary\n",
    "Amp_permutation_unitary_32_rgb = np.identity(2**total_q_32_rgb)\n",
    "print(Amp_permutation_unitary_32_rgb)\n",
    "Amp_permutation_unitary_32_rgb=np.roll(Amp_permutation_unitary_32_rgb,1,axis=1)\n",
    "print()\n",
    "print()\n",
    "print()\n",
    "print(\"shift Amplitude permutation matrix by 1 unit to the right column wise\")\n",
    "print()\n",
    "print(Amp_permutation_unitary_32_rgb)\n",
    "print()\n",
    "print(\"we will later use this for while applying hadamard operation to image coefficient vectors\")\n",
    "print()\n",
    "print(Amp_permutation_unitary_32_rgb.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34f2c02c-d655-42c8-8ef7-3442569eb662",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yR7uuZgcBAAC7hH27dWvSGULsPE15LYusod1BfRRCGxk5cmTmzJmTiRMn5qSTTkq/fv2SJDNnzsxZZ52VqqqqJMmQIUN2ao6GTvlatya5Z/Lm9x/U44g89NSdefJfD2Zg72PXb1+zdnX+uXh2BvY+bnujAgAA7BBzn5qb5k30/6u39l6K7dOU15LGsaj0RiZMmJAuXbpkwYIFGTBgQAYOHJi+fftm2LBh6d27d0488cQkddcP2pWdMPi9KSsry2+nf6fO9tse+J+sXvtqTjxsTGmCAQAAACVjhtBGunfvnunTp2f8+PG59957M3/+/PTv3z9XX311zj333Bx44IFJdp9CqNe+A3Pa0Z/KjTOuyld+dkaGHfz2/Ov5Ofn9nydnUO/jc+JhZ5Y6IgAAwC7prod+keeXvf7BQy+tfCHV69bkl3+8JEmyd6eeOenws0oZD7aLQqgehxxySG655ZZNtq9YsSLz589Ps2bNcuihh5YgWeN84rTvZJ9OB+S2B36UB+fcmo7tuub0Yz6ds9/2tR3+6WgAAABvFLc/+JP8/Z/31tl2zR1fSpIM6n28QojdmkJoGzz++OOpra1Nv3790rbtpivSX3/99UmSJ554os7XBxxwQIYOHdp0QTfSvFnzvPv4C/Lu4y8oWQYAAIDdzbc+8adSR4CdRiG0DR599NEkm79d7N3vfne9X5999tm55pprdmo2AAAAgIZSCG2DrRVCtbW1TRkHAAAAoFEsILMNtlYIAQAAAOwOzBDaBtOmTSt1BAAAAIDtZoYQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCW1BVVZUJEyakT58+ad26dXr06JHzzz8/K1euzDnnnJOysrJcddVVpY65VVOmXZqv/eLdOevS3jlpfFk+8I0DSh0JAABgl7bwhadyzR1fzqevPCrv+speOe2LHfKxbw/JL+/+elatWVnqeLDdyksdYFc1e/bsjBo1KpWVlWnXrl369++fxYsXZ/LkyZk3b16WLl2aJBkyZEhpgzbAT/9wUTq07Zy++70pK1e9VOo4AAAAu7zbZ/40N93/vQzvf1rectiYNG/eIo/MuyfX3P7F3PfIrzP5039NqxZtSh0TGk0hVI+qqqqMHj06lZWVueCCC3LxxRenQ4cOSZLLLrssF154YcrLy1NWVpZBgwaVOO3W/fxz87Jvl95JknMnHZpVa1aUOBEAAMCu7diB78r7R3w+7drssX7b6OEfz35d++a6u7+ePzz4k5x+zNgSJoTt45axeowbNy4LFy7M2LFjM2nSpPVlUJJMmDAhgwcPTnV1dQ444IB07NixhEkb5t9lEAAAAA1zUI+hdcqgfzth8HuTJPMrH2vqSLBDKYQ2MmfOnEydOjVdu3bNpZdeWu+Yww8/PEkyePDg9duuv/76vPOd70zPnj3Ttm3bHHzwwfnCF76QFSvMxgEAAHijeOHlhUmSTu33KXES2D4KoY1MmTIlNTU1GTNmTNq3b1/vmDZtXr9PdMNCaNKkSWnevHm+8Y1v5A9/+EM+8YlP5Ac/+EFOPvnk1NTUNEl2AAAAdp51Nevyyz/+d5o3K8+Jh51Z6jiwXawhtJFp06YlSUaMGLHZMQsXvt4Ib1gI3Xzzzdlrr73Wf3388cdnr732ypgxY/LnP/85xx133DblGDp0aCorK7c6rmV5m/xo7NxtOjcAAMCuqG+/vllTvapJHqsx76V+cNNn8sSzf8lHRn0jPfY+aCcle2NoymtZZBUVFZk1a1ajjlUIbeTZZ59NkvTs2bPe/dXV1ZkxY0aSuoXQhmXQvw0dOjRJsmjRom3OUVlZ2aDjWrdou83nBgAA2BUtWbw4q9e+2iSPta3vpa65/Uu5ccZVOeXI8/L+Ez+/k1K9cTTltaRxFEIbWblyZZJk1ar6m8ypU6emqqoqHTp0SK9evbZ4rnvuuSdJcsghh2xzjoqKigaNa1nuYw4BAIA3hn27dWvSGUIN9fM7v5Jf3n1J3nbEh3P+O3+4E1O9cTTltSyyhnYH9VEIbaSioiLLli3Lww8/nOHDh9fZt2TJkowfPz5JMmjQoJSVlW32PIsWLcqXvvSlnHzyyRkyZMg252jolK91a5J7Jm/z6QEAAHY5c5+am+Ytm+axGvpe6ud3fiW/uOurOenws/PZd/14i+8D+f+a8lrSOBaV3sjIkSOTJBMnTsxTTz21fvvMmTMzYsSIVFVVJckWS54VK1bkHe94R1q2bJmf/vSnOzUvAAAAO8cv7vpafnHXVzPyTWflv97z0zRr5i00bxxmCG1kwoQJue6667JgwYIMGDAgBx98cFavXp2nn346o0aNygEHHJA77rijzvpBG1q1alVGjx6dZ555JtOnT8++++7bxN/Bpu566Bd5ftnrayO9tPKFVK9bk1/+8ZIkyd6deuakw88qZTwAAIBdzo0zvpef33lx9t5z/7yp78hM+9t1dfZ36rBPDu93UonSwfZTCG2ke/fumT59esaPH59777038+fPT//+/XP11Vfn3HPPzYEHHpgk9RZCa9euzbve9a7MmjUrd999d/r379/U8et1+4M/yd//eW+dbdfc8aUkyaDexyuEAAAANvLkgplJkudf+lcum3r2JvsH9T5eIcRuray2tra21CF2FytWrEjHjh1TVlaW5cuXp23b/78qfU1NTd73vvflpptuym233ZYTTzyxSTJZQwgAAHijGDEuu9waQjROU15LGscMoW3w+OOPp7a2Nv369atTBiXJpz71qfzmN7/J5z73ubRt2zZ//etf1+878MAD6/1YegAAAIBSsCLWNnj00UeT1H+72B/+8IckyTe/+c0MHz68zp9bb721SXMCAAAAbIkZQttgS4XQ/PnzmzgNAAAAQOOYIbQNtlQIAQAAAOwuzBDaBtOmTSt1BAAAAIDtZoYQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQmgzqqqqMmHChPTp0yetW7dOjx49cv7552flypU555xzUlZWlquuuqrUMQEAAAC2WXmpA+yKZs+enVGjRqWysjLt2rVL//79s3jx4kyePDnz5s3L0qVLkyRDhgwpbdAGWPjCU/njw9fmoafuzJIX52VN9ers2/nAHDf43Tnj2M+kTct2pY4IAACwy1nw/JO59o9fy9xFD+fFVxZn3bq12XvP/TPs4Lfn3SeMT5eO+5Y6ImwXhdBGqqqqMnr06FRWVuaCCy7IxRdfnA4dOiRJLrvsslx44YUpLy9PWVlZBg0aVOK0W3f7zJ/mpvu/l+H9T8tbDhuT5s1b5JF59+Sa27+Y+x75dSZ/+q9p1aJNqWMCAADsUl54eWGWvrIkxxz6H9lrj+5p3qw8z1Q+mlsf+FHueeRX+eF/zk6n9nuXOiY0mkJoI+PGjcvChQszduzYTJo0qc6+CRMm5LrrrssjjzySXr16pWPHjiVK2XDHDnxX3j/i82nXZo/120YP/3j269o319399fzhwZ/k9GPGljAhAADArudNfd+SN/V9yybbB/Y6Lpdc+57cOfOavHfEhBIkgx3DGkIbmDNnTqZOnZquXbvm0ksvrXfM4YcfniQZPHjw+m3Tp0/PyJEjs++++6ZVq1bp3r173vve92bOnDlNkntLDuoxtE4Z9G8nDH5vkmR+5WNNHQkAAGC3tU+nnkmSFauWlTgJbB8zhDYwZcqU1NTUZMyYMWnfvn29Y9q0ef32qg0LoWXLlmXgwIH52Mc+lr333jsLFy7MpZdemuHDh+exxx5L9+7dmyT/tnjh5YVJkk7t9ylxEgAAgF3XmrWrs2rNiqxZuzrPPvdEfnzbhUmSYQe/vcTJYPsohDYwbdq0JMmIESM2O2bhwteLlA0LodNOOy2nnXZanXFHHHFEDjrooNxwww05//zzd0LaxltXsy6//ON/p3mz8px42JmljgMAALDLuu3BH+d7v//0+q8rOh2Qz73/2gzsfWwJU8H2Uwht4Nlnn02S9OzZs9791dXVmTFjRpK6hVB9unTpkiQpL2/cj3jo0KGprKzc6riW5W3yo7Fzt+ncP7jpM3ni2b/kI6O+kR57H9SofAAAADta3359s6Z6VZM8VkPfSx0z4PTsv9fBWbVmRZ5e9Lf85Ymb8vLKqiZIuHtrymtZZBUVFZk1a1ajjlUIbWDlypVJklWr6n/STp06NVVVVenQoUN69eq1yf5169alpqYmzz77bD7/+c+noqIi73nPexqVpbKyMosWLdrquNYt2m7Tea+5/Uu5ccZVOeXI8/L+Ez/fqGwAAAA7w5LFi7N67atN8lgNfS+1157ds9eery8Dcsyhp+fYge/M2MlH5LW1r3pPtQVNeS1pHIXQBioqKrJs2bI8/PDDGT58eJ19S5Ysyfjx45MkgwYNSllZ2SbHH3/88etnEPXp0yfTpk3LXnvt1egsDdGyvOEfGf/zO7+SX959Sd52xIdz/jt/2KhcAAAAO8u+3bo16QyhxujdbVAO3O+w3Hz/9xVCW9CU17LIGtod1EchtIGRI0dmzpw5mThxYk466aT069cvSTJz5sycddZZqap6fVrgkCFD6j3+Jz/5SV566aU888wzufzyy/PWt741M2bMyP7777/NWRo65WvdmuSeyVsf9/M7v5Jf3PXVnHT42fnsu35cb6EFAABQSnOfmpvmLZvmsRr6Xqo+a9auyvJXl+7YQG8wTXktaRwfO7+BCRMmpEuXLlmwYEEGDBiQgQMHpm/fvhk2bFh69+6dE088Mcnm1w866KCDcuSRR+Z973tf7r777ixfvjyXXXZZU34L9frFXV/LL+76aka+6az813t+mmbNXHYAAIAtWfpK/Wu6zn76nsyvfCwH9zyqiRPBjmWG0Aa6d++e6dOnZ/z48bn33nszf/789O/fP1dffXXOPffcHHjggUm2vqB0kuy5557p06dPnn766Z0de4tunPG9/PzOi7P3nvvnTX1HZtrfrquzv1OHfXJ4v5NKlA4AAGDXNPm3n8iLy5dkSJ8Ts8+ePbOmenXmLnwof3rkV2nTqkM+duq3Sh0RtotCaCOHHHJIbrnllk22r1ixIvPnz0+zZs1y6KGHbvU8zz//fJ588skceeSROyNmgz25YObreV76Vy6bevYm+wf1Pl4hBAAAsJERh70/dz3089z90C/y0soXUpay7NOpZ0456mN5z/Hjs3enbV8aBHYlCqEGevzxx1NbW5t+/fqlbdu6q9F/4AMfSJ8+fTJkyJDsueeemTt3bq644oqUl5fnP//zP0uU+HUT3ndNJrzvmpJmAAAA2N0cP/g9OX5w4z41GnYHCqEGevTRR5PUf7vYUUcdlZ///Of57ne/m9WrV6dHjx4ZMWJELrroovTs2bOpowIAAABskUKogbZUCI0dOzZjx45t6kgAAAAAjeLjphpoS4UQAAAAwO7EDKEGmjZtWqkjAAAAAOwQZggBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiG0GVVVVZkwYUL69OmT1q1bp0ePHjn//POzcuXKnHPOOSkrK8tVV11V6phbteD5J3PpdWPykcsPyTu+tEdOvahtPnLZwfnhTZ/Ni68sKXU8AACA3cbqNa/mrEt756TxZbnyd2NLHQe2S3mpA+yKZs+enVGjRqWysjLt2rVL//79s3jx4kyePDnz5s3L0qVLkyRDhgwpbdAGeOHlhVn6ypIcc+h/ZK89uqd5s/I8U/lobn3gR7nnkV/lh/85O53a713qmAAAALu8n93x5by88oVSx4AdQiG0kaqqqowePTqVlZW54IILcvHFF6dDhw5JkssuuywXXnhhysvLU1ZWlkGDBpU47da9qe9b8qa+b9lk+8Bex+WSa9+TO2dek/eOmFCCZAAAALuPuQsfzm///J2c+/bLcvUtF5Q6Dmw3t4xtZNy4cVm4cGHGjh2bSZMmrS+DkmTChAkZPHhwqqurc8ABB6Rjx44lTLp99unUM0myYtWyEicBAADYta2rWZcrrj83Rxx0ct488IxSx4EdQiG0gTlz5mTq1Knp2rVrLr300nrHHH744UmSwYMHb/Y8o0aNSllZWb7yla/sjJiNsmbt6ry8siovvLQws568M9+54WNJkmEHv73EyQAAAHZtv73viix4/h8Ze/quv44sNJRbxjYwZcqU1NTUZMyYMWnfvn29Y9q0aZNk84XQr3/968yePXtnRWy02x78cb73+0+v/7qi0wH53PuvzcDex5YwFQAAwK5tydJn8vM7L86Yk76cis4HpHLp/FJHgh1CIbSBadOmJUlGjBix2TELFy5MUn8h9Morr+Qzn/lMJk2alA984APblWXo0KGprKzc6riW5W3yo7FztzrumAGnZ/+9Ds6qNSvy9KK/5S9P3JSXV1ZtV0YAAIAdqW+/vllTvapJHquh76W+e8PHU9Gld9513GebINUbR1NeyyKrqKjIrFmzGnWsQmgDzz77bJKkZ8+e9e6vrq7OjBkzktRfCH3hC19Iv379MmbMmO0uhCorK7No0aKtjmvdom2DzrfXnt2z157dkyTHHHp6jh34zoydfEReW/tq3n/i57crKwAAwI6wZPHirF77apM8VkPeS/3xoWvz8Ny78u1P3Jfy5i2aINUbR1NeSxpHIbSBlStXJklWraq/xZw6dWqqqqrSoUOH9OrVq86+WbNm5X/+53/y0EMP7ZAsFRUVDRrXsrxNo87fu9ugHLjfYbn5/u8rhAAAgF3Cvt26NekMoS1ZU/1arr75sxl28NvTqUNFFlU9nSSpevn1/3G/cvXLWVT1dPZo1zXt2+y5s+PudpryWhZZQ7uD+iiENlBRUZFly5bl4YcfzvDhw+vsW7JkScaPH58kGTRoUMrKytbvW7duXT72sY9l7NixGTBgwA7J0tApX+vWJPdMbtxjrFm7KstfXdq4gwEAAHawuU/NTfOWTfNYW3svtWbtqry08oU8MOfWPDDn1k323/3wtbn74Wtz3imX590n/NdOTLp7asprSeMohDYwcuTIzJkzJxMnTsxJJ52Ufv36JUlmzpyZs846K1VVr6+5M2TIkDrHXXXVVXnuued2qU8V+7elr1Smc8dNG8PZT9+T+ZWPZdCBJzR9KAAAgF1c65bt8qWzfrPJ9pdXvJDJv/tkjjjo5Jw87Jz03ndQCdLB9lMIbWDChAm57rrrsmDBggwYMCAHH3xwVq9enaeffjqjRo3KAQcckDvuuKPO+kFVVVX50pe+lEmTJqW6ujovvfTS+n2rV6/OSy+9lI4dO6ZZs2Yl+I6Syb/9RF5cviRD+pyYffbsmTXVqzN34UP50yO/SptWHfKxU79VklwAAAC7svLmLXLcoHdtsv3fnzK2b5cD690Pu4vStBS7qO7du2f69Ok55ZRT0rp168yfPz+dO3fO1VdfnVtvvTVPPfVUkroLSi9cuDDLly/Pxz72sXTq1Gn9nySZOHFiOnXqlH/9618l+X6SZMRh788e7brm7od+ke/fdH5+ctvn8uSCB3PKUR/Ljz779/TZb0jJsgEAAAClUVZbW1tb6hC7gxUrVqRjx44pKyvL8uXL07Zt2/Xb61vvZ8SIETn77LPzoQ99KEcddVRat269U3JtzxpCAAAAu5IR47LLrCHE9mnKa0njuGWsgR5//PHU1tamX79+68ugJGnfvn1OOOGEeo854IADNrsPAAAAoFTcMtZAjz76aJK6t4sBAAAA7I7MEGqgbS2E3IkHAAAA7KrMEGogM4QAAACANwozhBpo2rRppY4AAAAAsEOYIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAACwi7vsVx/KSePLSh2DNxCF0BZUVVVlwoQJ6dOnT1q3bp0ePXrk/PPPz8qVK3POOeekrKwsV111ValjbrPVa17NWZf2zknjy3Ll78aWOg4AAMAu6aTxZfX+Gf2F9puMvWPmNTlpfFnumHlNveeqXDo/J40vy2W/+tAOyzfjsd/n53d+ZYedj2IpL3WAXdXs2bMzatSoVFZWpl27dunfv38WL16cyZMnZ968eVm6dGmSZMiQIaUN2gg/u+PLeXnlC6WOAQAAsMsb2OvYvP3I8+psK2/eoslzfPbd/5PPvPOHdbbNeOz3ueuhn+WDb/1Kk+dh96cQqkdVVVVGjx6dysrKXHDBBbn44ovToUOHJMlll12WCy+8MOXl5SkrK8ugQYNKnHbbzF34cH775+/k3LdflqtvuaDUcQAAAHZpFZ17Z+ThHyh1jNdLqCYsoqrXrU1Nzbq0bNG6yR6TpuWWsXqMGzcuCxcuzNixYzNp0qT1ZVCSTJgwIYMHD051dXUOOOCAdOzYsYRJt826mnW54vpzc8RBJ+fNA88odRwAAIDdwtrqNVn12oodes5/30L28zu/kr8+cUs+9d0j8vbPt857v7ZvfnTL+KxbV11n/MZrCF3wgxNy10M/S1L31rZ/37L2r+f/kcm//WQ+OmlATvtih5x6Udt88juH57YHfrxJlp/f+ZWcNL4s8ysfzw9v+mzef0n3nPL51vn7P+/Lu76yV86/6ph6v4df/+nynDS+LH//53076KdCUzJDaCNz5szJ1KlT07Vr11x66aX1jjn88MPzyCOPZPDgweu3/elPf8qIESM2GTt48ODMnj17Z8XdJr+974oseP4f+fIHbyh1FAAAgN3C9Eevz91/uzY1NeuyZ7u9cvyQ9+bDb7sk7drssUPO/+A/bsvN938/pwz/eE4+4iO5//Eb85t7J6V9m0458y0Xbfa4M9/yhdTW1uTRZ6bnwvf9Yv32AQccnSR5ZN6f8ug/78uRh5yais69snrNytz399/kiuvPzcsrX8j7T/z8Jue89LoxadWiTd513AUpKyvLPp165qTDz871930rC55/Mj32PqjO+Ntn/jTd9+qXQb2P2yE/C5qWQmgjU6ZMSU1NTcaMGZP27TddKCxJ2rRpkyR1CqF/+973vpc3velN679u167dzgm6jZYsfSY/v/PijDnpy6nofEAql84vdSQAAIBd2sE9huW4Qe9Ot6598urqV/LgP27LjTOuyt/n3Zvvjr0/bVrV/55xWzxb+Xj+578eT0XnA5Ikpw7/eM771sDcOOPKLRZCh/c7KXc//Ms8+sz0em9pG3n4WRk9/ON1tr3z2P/M+KtPzK/u+Wbeffx/bbIWUvs2e+ay8/6Y5s3/f1VwylHn5fr7vpXbH/xJzj31svXbH3tmRhY8/4989O0TG/NtswtQCG1k2rRpSVLvbJ9/W7hwYZL6C6H+/fvnqKOO2u4cQ4cOTWVl5VbHtSxvkx+NnbvVcd+94eOp6NI77zrus9udDQAAYGfo269v1lSvapLHash7qSvHPVDn65OGfjC99h2U/739C/ntn7+bMW/5wnbnOPrQ09eXQUlSVlaWwX1G5MYZV2XVaysaXTq1afn/JyesWbs6q9esTG1qc3i/t+bv/7w3C57/R3rtO7DOMWcc+5k6ZVCS/5sBdHzueujn+ciob6zff/vMn6R5s/K8dejZ9T5+U17LIquoqMisWbMadaxCaCPPPvtskqRnz5717q+urs6MGTOS1F8I7SiVlZVZtGjRVse1btF2q2P++NC1eXjuXfn2J+4ryWr4AAAADbFk8eKsXvtqkzxWQ95L1ec9J4zPtXd9NQ/OubVRhVBZWVmdr/ft3HuTMR3bdkmSvPLqi40uhFa9tiI/v/Mruffvv84LLy3YZP/yVcs22dZ9r371nuuUo87LpdeNyV/n3JJjDj09r65ennsf+XWOOuTUdOqwT73HNOW1pHEUQhtZuXJlkmTVqvqbzKlTp6aqqiodOnRIr169Ntn/3ve+N1VVVenSpUtOO+20fPOb30zXrl23OUdFRUWDxrUsb7PF/WuqX8vVN382ww5+ezp1qMiiqqeTJFUvv142rVz9chZVPZ092nVN+zZ7bnNOAACAHWXfbt2adIZQY5Q3b5EuHbvl5ZVVdba3avH6+V7bTAmyes3KOuP+rVmz5pt9rNra2kZlTJJvXHdmHphzS95+5HkZ1Pu4dGjbJc3LmufBf9yWG6Zfkdramk2OabWZkuzNA9+Zjm3H5fYHf5JjDj09f3pkalavWZlRR350s4/flNeyyBraHdRHIbSRioqKLFu2LA8//HCGDx9eZ9+SJUsyfvz4JMmgQYPqNLt77LFHxo8fn+OOOy7t27fPX/7yl1x66aX561//mlmzZqV16237qL6GTvlatya5Z/Lm969ZuyovrXwhD8y5NQ/MuXWT/Xc/fG3ufvjanHfK5Xn3Cf+1TRkBAAB2pLlPzU3zlk3zWFt7L7U5a9auzgsvL8whPesuFVLR+fUJA/96fk69x/17+7/H7Qgbzzb6txWrXsoDc27JyDedlc+884d19j389B+3+XFalrfKSYd/ML+bMTlVLy/O7Q/+JF332C9DDzp5s8c05bWkcRRCGxk5cmTmzJmTiRMn5qSTTkq/fq9PmZs5c2bOOuusVFW93gIPGTKkznGHHXZYDjvssPVfn3DCCTn00ENz2mmnZcqUKfnwhz/cZN/Dhlq3bJcvnfWbTba/vOKFTP7dJ3PEQSfn5GHnpPe+g0qQDgAAYNf0ysoX07Fdl022X3PHl7KupjpHHTK6zva++70pe+3ZI3+a/au8b8Tn03WPbuv3ra1ekxtnXJWysrIM73/aDsv479vJXnl1aTq27bx+e7Oy12cdbTzD6MVXluQP9XzsfEOMOvLc3DD9ivz4tgsz519/zZlv+UKab2F2E7s+hdBGJkyYkOuuuy4LFizIgAEDcvDBB2f16tV5+umnM2rUqBxwwAG54447GrR+0Kmnnpp27dpl1qxZJSuEypu3yHGD3rXJ9n9/yti+XQ6sdz8AAECR/fLuSzLn2b9mcJ8R2XvP/bP6tRV58B+3Zfa8e3Lw/kfm9Dd/us745s3Lc/4ZP8hXfvYf+di3B+XkYeekW5cDs2z5c7n3kamZ/9zjef+JF23y0e3b45D9j8qNM67Klb/9ZIYdckrKm7fIwfsfmX0798rhfd+au/92bVq1aJN+PY7I88ueza1/vToVnXvllVdf3ObH6rnPITm015tz98PXpqysLCcf8ZEd9n1QGgqhjXTv3j3Tp0/P+PHjc++992b+/Pnp379/rr766px77rk58MADk2zbgtKbm8YHAADArmlw7xPy7HNP5K5ZP8srr76Y5mXN061r33z45K/nXcd9Ni1bbLosyJGHnJLvfGpGpv7psvXHtW7ZLn32OyxfPGlqjh/8nh2accSQ9+fpRX/Lnx75Ve77+29SU1uT/3rP/2bfzr3yuTOvzY9v+1z+Mufm3PnQz7Jf17758Kivp3mzFpn068ZNWHj7keflsWf+nMEHjsi+XTZdDJvdS1nt9qxSVTArVqxIx44dU1ZWluXLl6dt2y2vSn/jjTfm9NNPz89+9rN88IMf3CmZGnvfKwAAwK5mxLjs8msIFdm9j/w6l1z73nz+zOty4mHv3+LYpryWNI4ZQtvg8ccfT21tbfr167dJGfSBD3wgvXv3zpve9Kb1i0pfdtllGTJkSN73vveVKDEAAADsGDfe/73s0a5r3jzwjFJHYQdQCG2DRx99NEn9t4sNGDAg1113Xb7zne9k1apV6d69e84999xcfPHFadlSLQoAAMDuZ9mK5/O3uXfnsWem59F/3pdzRl2aluWtSh2LHUAhtA22VAh9/vOfz+c///mmjgQAAAA7zb+eeyKXXndm2rfZM6ce9fG86/gLSh2JHUQhtA22VAgBAADAG83gA0/IXZdbeviNSCG0DaZNm1bqCAAAAADbrVmpAwAAAADQtBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhtBlVVVWZMGFC+vTpk9atW6dHjx45//zzs3LlypxzzjkpKyvLVVddVeqYAAAAANusvNQBdkWzZ8/OqFGjUllZmXbt2qV///5ZvHhxJk+enHnz5mXp0qVJkiFDhpQ2aAOdNL6s3u2tW7bLzV9f0cRpAAAAdh+vvLo0U+7+Ru5//Pd54eWFaduqQw6oODRnv/VrGdj72FLHg0ZTCG2kqqoqo0ePTmVlZS644IJcfPHF6dChQ5Lksssuy4UXXpjy8vKUlZVl0KBBJU7bcAN7HZu3H3lenW3lzVuUKA0AAMCu77llz+a/fnBCVq1ZkZOHnZPuXftl5eqX888lf0/VK4tKHQ+2i0JoI+PGjcvChQszduzYTJo0qc6+CRMm5LrrrssjjzySXr16pWPHjiVKue0qOvfOyMM/UOoYAAAAu41vTvlA1tVU5+rP/j1dOu5b6jiwQ1lDaANz5szJ1KlT07Vr11x66aX1jjn88MOTJIMHD95k3+9+97scffTRadeuXfbYY48cc8wxefzxx3dq5m2xtnpNVr3mFjEAAICt+fs/78tjz/w57zlhQrp03DfV69Zm9ZpXSx0LdhgzhDYwZcqU1NTUZMyYMWnfvn29Y9q0aZNk00Jo8uTJueCCC/Kf//mf+e///u+89tpreeCBB7Jq1aqdnrshpj96fe7+27WpqVmXPdvtleOHvDcfftsladdmj1JHAwAA2OU8+I/bkiR777l/vvTT0XnwyT+kpmZd9uvaNx8Y+WV3YLDbUwhtYNq0aUmSESNGbHbMwoULk9QthObNm5fx48fniiuuyNixY9dvf/vb376Tkm6bg3sMy3GD3p1uXfvk1dWv5MF/3JYbZ1yVv8+7N98de3/atKq//AIAACiqhc8/mSS54vpz061r30x478+ydt2a3HDvtzLxV2elumZtTj7iwyVOCY2nENrAs88+myTp2bNnvfurq6szY8aMJHULoZ/+9Kdp0aJFzj333B2WZejQoamsrNzquJblbfKjsXO3OObKcQ/U+fqkoR9Mr30H5X9v/0J+++fvZsxbvrBdWQEAAHaEvv36Zk1109xlsbX3Uq++tjxJ0qZVh0z6+D1pUd4ySXLMgNPzwW/2zv/+4aK89fCz06yZlVjq05TXssgqKioya9asRh2rENrAypUrk2Szt3lNnTo1VVVV6dChQ3r16rV++/3335+DDjoo1157bS655JIsWLAgffv2zZe//OW8//3vb1SWysrKLFq09VXrW7do26jzv+eE8bn2rq/mwTm3KoQAAIBdwpLFi7N6bdOs07O191KtWry+XMiIw96/vgxKkg5tO2V4/9Ny10M/z4IXnkzPfQ7ZqTl3V015LWkchdAGKioqsmzZsjz88MMZPnx4nX1LlizJ+PHjkySDBg1KWVlZnX2LFi3K5z//+UycODE9evTIT37yk5x55pnZa6+9MnLkyEZlaYiW5W22+dzJ6x8536Vjt7y8sqpRxwNQDBf84IQkybc+8adGHX/zX36YK3/3yUz54iKfzgLAVu3brVuTzhDakq57dE+SdOqw6Xuzzv/3b9qKVct2fLA3iKa8lkXW0O6gPgqhDYwcOTJz5szJxIkTc9JJJ6Vfv35JkpkzZ+ass85KVdXr5cmQIUPqHFdTU5MVK1bkF7/4RU4//fQkyVve8pY88cQT+e///u9GFUINnfK1bk1yz+RtPn3WrF2dF15emEN6HrXtBwNAAw3vf1qu/N0n89cnbs4pR51X6jgA7OLmPjU3zVtufdyOsLX3UgftPyy3/PWHqXp54Sb7ql56fdue7ffeWfF2e015LWkcNztuYMKECenSpUsWLFiQAQMGZODAgenbt2+GDRuW3r1758QTT0yy6SeMde7cOUnqFD9lZWUZOXJkHnvssab7BurxysoX691+zR1fyrqa6hx1yOgmTgRAkXTdo1v6dR+aGY//vtRRAGCbHDPg9LRt1SF3P3xtVr22Yv32F19ZkhmP/z7d9+qX/br2KWFC2D5mCG2ge/fumT59esaPH59777038+fPT//+/XP11Vfn3HPPzYEHHphk00JowIABeeCBB+o7ZVavXr3Tc2/JL+++JHOe/WsG9xmRvffcP6tfW5EH/3FbZs+7Jwfvf2ROf/OnS5oPgDe+owecnmv/+LWsem2FT7YEYLfRoW2nnHfqpHznho9l3JVH5W1HfCTV69bk5r/8INXr1uRT77iy1BFhu5ghtJFDDjkkt9xyS5YvX57ly5fngQceyHnnnZeVK1dm/vz5adasWQ499NA6x7zjHe9Iktx5553rt9XU1OSuu+7KEUcc0aT5Nza49wlp27pj7pr1s/zgps/k53denFdeXZoPn/z1fOvjf1q/UBoAbItPfndoPnL5/19E82s/f1fO+HLn9V9f+buxOWl8WVaseilHD3hH1la/lplP3l6KqADQaKccdV6+/MEb0rpV+/zsji/lumlfT/e9DsrlH7snQw96a6njwXYxQ6iBHn/88dTW1qZfv35p27buavSjR4/Osccem/POOy8vvvhi9t9///z4xz/O448/nrvuuqtEiV939KHvyNGHvqOkGQB4Y1m3rjrPVj6eow89ff22pxf9Lb27/f8ZtPMWz05FpwPSvs2ead9mz3Tr2if3P/b7HDfoXSVIDACNd+zAM3LswDNKHQN2ODOEGujRRx9NsuntYsnr6wXddNNNeec735mLLroop512Wp599tncdttt69cdAoA3igUvPJk11atzYLchSZKVq19J5bJn1n9dW1ubZ5b8PQfud9j6Y47u/448+I/bsm5ddQkSAwCwMYVQA22pEEqSPffcM1dffXVeeOGFvPbaa3nwwQfztre9rSkjAkCTmLd4dpKsL4D+ufiR1NbWrv96yYv/zKuvLV//dZIcPeAdWb5qWf7+zH1NGxYAgHophBpoa4UQABTFPxc/kiTp83+Fz78Loj7dDqvz9YaFULNmzZMkNTXrmiQjAABbZg2hBpo2bVqpIwDALmHekkfSuUNFOnXY5/WvF89Oi/JW2X+fQ9bvT5I+G9wydv9jv0+71ntk8IEnNHleAAA2ZYYQALBNFr3wVLp07Lb+63mLZ6fnPv1T3rxFkmTmP/6Qvfbskb337LF+zP1P3JhhB799/RgAAEpLIQQAbJN1NdV55dUXU1tbm3XrqjP/ucfX3x4268k789TCWXnLYWPWj3/2uTlZ+MJTOXqAT70EANhVKIQAgG0y+MAReW7Zs/nuDR/Pnx/7XdZWv5b2rffM9fd+O/997bvTfa9+ed+Jn18//i+P35gWzVvmiINHlTA1AAAbsoYQALBNPn7at7NsxXO59YEf5dYHfpQkuWH6FWlZ3jqjjvxoPnjSV9Kudcf14+9//MYMPnBEnW0AAJSWQggA2CZ7tOuab557RxZXzcuVv/tUHpp7Z7750TtzaK83p2WL1nXGLn2lMv9Y8EA+ffr3SpQWAID6KIQAgEbp1vXAVK9bk307986b+o2sd8xfnrgpSTLc+kEAALsUawgBAI02b8kj6xeUrs/JR3wkt3xjVbru0W2zYwAAaHpmCAEAjfLCSwuz/NWlWyyEmjcvT3MvNwAAdjleoQEAm/WtT/xps/v22rN77rq8tunCAACww7hlDAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACqa81AF2ZVVVVbnsssvy29/+NgsXLsxee+2VM844I9/4xjcybty4/PSnP82VV16ZsWPHljrqVr3y6tJMufsbuf/x3+eFlxembasOOaDi0Jz91q9lYO9jSx0PAABgl/LzO7+SX9z11c3ub96sPLdPXNuEiWDHUghtxuzZszNq1KhUVlamXbt26d+/fxYvXpzJkydn3rx5Wbp0aZJkyJAhpQ3aAM8tezb/9YMTsmrNipw87Jx079ovK1e/nH8u+XuqXllU6ngAAAC7nDcfeka6demzyfZnlvw9v7738hzVf3QJUsGOoxCqR1VVVUaPHp3KyspccMEFufjii9OhQ4ckyWWXXZYLL7ww5eXlKSsry6BBg0qcduu+OeUDWVdTnas/+/d06bhvqeMAAADs8np3G5Te3TZ9v/ed6z+WJBk17JymjgQ7lDWE6jFu3LgsXLgwY8eOzaRJk9aXQUkyYcKEDB48ONXV1TnggAPSsWPHEibdur//87489syf854TJqRLx31TvW5tVq95tdSxAAAAdjur1qzMPY/8Knvt0T1DDzq51HFguyiENjJnzpxMnTo1Xbt2zaWXXlrvmMMPPzxJMnjw4PXbTjjhhJSVldX75+Mf/3iTZK/Pg/+4LUmy957750s/HZ1TLmqT0V9olw9N7Jc/PnRtyXIBAADsbu575Dd5dfUreevQD6V5s+aljgPbxS1jG5kyZUpqamoyZsyYtG/fvt4xbdq0SVK3EPr+97+fV155pc64W2+9NZdccklOPfXUnRd4KxY+/2SS5Irrz023rn0z4b0/y9p1a3LDvd/KxF+dleqatTn5iA+XLB8AAMDu4vaZP0lZWVneNuwjpY4C200htJFp06YlSUaMGLHZMQsXLkxStxDq37//JuO+/vWvZ6+99srJJ2/7VMKhQ4emsrJyq+NalrfJj8bO3ez+V19bniRp06pDJn38nrQob5kkOWbA6fngN3vnf/9wUd56+Nlp1sxkMQAAoLT69uubNdWrmuSxtvZeamMLnn8yjz3z5xzW5y3Zt3OvnZjsjaEpr2WRVVRUZNasWY06ViG0kWeffTZJ0rNnz3r3V1dXZ8aMGUnqFkIbe+GFF3L77bfnk5/8ZMrLt/3HXFlZmUWLtv4JYK1btN3i/lYtXp/NNOKw968vg5KkQ9tOGd7/tNz10M+z4IUn03OfQ7Y5IwAAwI60ZPHirF7bNGuebu291MZuf/AnSZJRR350Z8R5w2nKa0njKIQ2snLlyiTJqlX1N5lTp05NVVVVOnTokF69Nt8KT5kyJdXV1TnrrLMalaOioqJB41qWt9ni/q57dE+SdOqw6fk6/98njq1YtWwb0wEAAOx4+3br1qQzhBpq3brq3PXQz9OxbZccc+h/7MRUbxxNeS2LrKHdQX0UQhupqKjIsmXL8vDDD2f48OF19i1ZsiTjx49PkgwaNChlZWWbPc8vfvGLHHLIIRk6dGijcjR0yte6Nck9kze//6D9h+WWv/4wVS8v3GRf1Uuvb9uz/d6NyggAALAjzX1qbpq33Pq4HWFr76U29Jcnbs6yFc/lP958flqWt9q5wd4gmvJa0jgWjtnIyJEjkyQTJ07MU089tX77zJkzM2LEiFRVVSVJhgwZstlz/OMf/8isWbMaPTtoRzpmwOlp26pD7n742qx6bcX67S++siQzHv99uu/VL/t17VPChAAAALu222f+3+1iw84pcRLYcRRCG5kwYUK6dOmSBQsWZMCAARk4cGD69u2bYcOGpXfv3jnxxBOTbHn9oF/84hcpKyvLmDFjmir2ZnVo2ynnnTopVS8vyrgrj8r19347v5r2zYy78qhUr1uTT73jylJHBAAA2GVVvbw4M5+8PQf3GJZe+w4sdRzYYRRCG+nevXumT5+eU045Ja1bt878+fPTuXPnXH311bn11lvXzxraXCFUW1ubX/7ylznhhBOy//77N2X0zTrlqPPy5Q/ekNat2udnd3wp1037errvdVAu/9g9GXrQW0sdDwAAYJd156xrUlOzzmLSvOGU1dbW1pY6xO5ixYoV6dixY8rKyrJ8+fK0bbvpqvT33ntvTjjhhPz0pz/Nhz/84Z2eaVvuewUAANiVjRiXXXINIbZdU15LGscMoW3w+OOPp7a2Nn379q23DEpev12sTZs2ede73tXE6QAAAAAaRiG0DR599NEkm79dbPXq1bn++utz+umnp0OHDk0ZDQAAAKDBfOz8NthaIdS6deu89NJLTZgIAAAAYNuZIbQNtlYIAQAAAOwOzBDaBtOmTSt1BAAAAIDtZoYQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQmgzqqqqMmHChPTp0yetW7dOjx49cv7552flypU555xzUlZWlquuuqrUMQEAAAC2WXmpA+yKZs+enVGjRqWysjLt2rVL//79s3jx4kyePDnz5s3L0qVLkyRDhgwpbdAG+PmdX8kv7vrqZvc3b1ae2yeubcJEAAAAu4dVr63I7/48OffMnpLnls1Pi+at0n2vfnn7keflrUPPTllZWakjQqMphDZSVVWV0aNHp7KyMhdccEEuvvjidOjQIUly2WWX5cILL0x5eXnKysoyaNCgEqfdujcfeka6demzyfZnlvw9v7738hzVf3QJUgEAAOzaampqctFPRuWJ+ffnpKFn5/RjPp3X1r6ae/42JZN+/eH86/k5OfeUiaWOCY2mENrIuHHjsnDhwowdOzaTJk2qs2/ChAm57rrr8sgjj6RXr17p2LFjiVI2XO9ug9K726bF1Xeu/1iSZNSwc5o6EgAAwC7vH/96II898+eccexn8onTrli/ffTwT+Yjlx+cW/96tUKI3Zo1hDYwZ86cTJ06NV27ds2ll15a75jDDz88STJ48OA626dPn563vOUt6dq1a/bcc88cddRR+e1vf7vTMzfGqjUrc88jv8pee3TP0INOLnUcAACAXc7K115JknTp2K3O9hblLbNHu65p3bJdKWLBDqMQ2sCUKVNSU1OTMWPGpH379vWOadOmTZK6hdAjjzySk046Kc2bN88111yTqVOnpkePHnnXu96VW265pUmyb4v7HvlNXl39St469ENp3qx5qeMAAADscg7uMSzt2+yZX//pstz7yG/y/LJ/5V/P/yM/ue3zmbvwoXzwrV8pdUTYLm4Z28C0adOSJCNGjNjsmIULFyapWwhNnTo1ZWVl+f3vf5+2bdsmSUaOHJnevXvnl7/8ZU499dSdmHrb3T7zJykrK8vbhn2k1FEAAAB2SR3adsrXPnRTvn39R3PJte9Zv71tqw758gdvyDGHnl66cLADKIQ28OyzzyZJevbsWe/+6urqzJgxI0ndQmjNmjVp2bLl+tlDSdK8efN06NAhNTU1jcoydOjQVFZWbnVcy/I2+dHYuQ0+74Lnn8xjz/w5h/V5S/bt3KtR2QAAAHaGvv36Zk31qiZ5rIa8l2rTqn0O2OfQDO9/Wvr3PDrLVy3NTfd/L5ded2a++qEbc3i/k5ok6+6oKa9lkVVUVGTWrFmNOlYhtIGVK1cmSVatqv9JO3Xq1FRVVaVDhw7p1ev/lylnnXVWvve97+WCCy5Y/ylkV199debOnZvvf//7jcpSWVmZRYsWbXVc6xZtt+m8tz/4kyTJqCM/2qhcO9NJ48tyQMWh+ejbJ+bIQ96ehS/MzeVTz87LK6vSrvUeGf/ea3JAxYBNjmvouNtn/m++9euP5Ctn/259m7+lY9dUv5arb74gs566Iy3LW+fAfQfnc2deu9n8Dc3R0LE7O+8bPXNj8+7ozG/kn/HumHl3y7s7Zt5Vfifv6My7Ut7dMbPfyZ4XTfV3rzGZd8ef8Y7wXz8ckX8ufiQfOOnLOePYz+z0x2uoJYsXZ/XaV5vksbb2XuqZJY/m/KuOzsdPuyKjh398/fYRQ96fc791aK64/tz87HPzLMOxGU15LWkchdAGKioqsmzZsjz88MMZPnx4nX1LlizJ+PHjkySDBg1KWVnZ+n2DBw/O3XffnTPOOCNXXPH66vPt2rXLb37zmxx33HGNztIQLcvbbH3Q/1m3rjp3PfTzdGzbJccc+h+NyrWzXfHJ6WnfZs8kyXdv+FjefuR5edsRH8p9f78+l0/9UL53/sxNjmnIuMql8/OHB/4nh+x/VIOP/cltn0tZWVmumfBUysrKsvSVLc/Yamjehoxtirxv9MyNzbujM7+Rf8a7Y+bdLe/umHlX+Z28IzPvanl3x8x+J3teNNXfvcZk3h1/xjvCpI/fk8t+9aEmeaxtsW+3bk06Q2hLbph+RdZUr87xg95dZ3vrlm1z5CGn5MYZV+W5pfPTreuBOzPmbqspr2WRNbQ7qI9FpTcwcuTIJMnEiRPz1FNPrd8+c+bMjBgxIlVVVUmSIUOG1Dlu7ty5ee9735sjjjgit912W+644468853vzPve97716xJtq1mzZmXhwoVb/TP3qYbfLvaXJ27OshXP5S1v+kBalrdqVK6msmzF83lq4ayMfNMHkiTHDnxnXnhpQRZVPb3N42pqavLt33w0nzr9yrTY4Pve0rGr1qzM7Q/+JB8++evry7/OHTf/F62heRsytinyvtEzNzbvjs78Rv4Z746Zd7e8u2PmXeV38o7MvKvl3R0z+53sedFUf/eSFOJ58UY396m5DXoftCP+bO29VNXLr9+xsa523Sb71q2rfv2/NdU7/ofwBtGU17LIfxp7u1iiEKpjwoQJ6dKlSxYsWJABAwZk4MCB6du3b4YNG5bevXvnxBNPTLLpR85fdNFFadu2bX73u99l1KhReetb35qf/exnOfLII3PBBReU4lup1+0z/+92sWHnlDjJ1r3w0oJ07rhvmjd/fRJbWVlZ9u60f55/6V/bPO6G+76dAQcck37dD2/wsUuq5qVD286ZMu0b+eR3h+Y/v39sHp5793bnbcjYpsj7Rs/c2Lw7OvMb+We8O2be3fLujpl3ld/JOzLzrpZ3d8zsd7LnRVP93UtSiOcFTafnPv2TJHfOvKbO9hWrXsr9T9yYDm06pVvXPiVIBjuGQmgD3bt3z/Tp03PKKaekdevWmT9/fjp37pyrr746t9566/pZQxsXQo8++mgGDx6c8vK6d+ANHTo0c+bMabL8W1L18uLMfPL2HNxjWHrtO7DUcZrMM5WPZfqjN2TMyC9u03Hraqrz3LJn03Pv/vn++bPyqXdMztevfW+WLX9uJyV93e6WN9n9Mjc2b7L7Zfa8aLjdLW8is+dF/Xa3zH4ne17Ux/OiaZ4XbN0Zx34mHdp2zk/+8Ll8c8pZufkvP8x1d38jn7jisCx9ZUk+dPIl1g9it2YNoY0ccsghueWWWzbZvmLFisyfPz/NmjXLoYceWmdfRUVFZs+enerq6jql0MyZM7Pffvvt9MwNceesa1JTs26XXEy6Pnvt2SNLX1mSdeuq07x5eWpra/P8sn9l7z3336Zxj/1zep5bNj8fmtg3SbJ0eWW+c/15WfrKkrx50Ds3e2z7NnumWVmznPimMUmSPvsdlorOvfLMkkfTqcM+jc67tbEPP3VXk+R9o2dubN7RR39ii8d6XuzemXe3vLtj5l3ld7Lnxa6V2e9kz4um+ruXJHt32v8N/7yg6ezTqWeu+vSDufaPX8vfnr47f5r9q7Rq0SYHdhuS80Z/K8cOPKPUEWG7mCHUQI8//nhqa2vTt2/ftG1bdzX6T33qU5k7d27+4z/+I7fcckv+8Ic/5Kyzzsq9996b888/v0SJ6zrzLRflrstr8/Yjzy11lAbp1H7v9NnvTfnjw69/wsL0R29I1z27Z7+NpmRubdzooz+RqV9ekmsvmp9rL5qfQ/Y/Kp95148y+uhPbPHYPdp1zZA+b8msJ+9IkixZ+kwqlz6T/fc5JEkyccoH8+dHf7fNebc2tqnyvtEzNzbv1o71vNi9M5cib9GeF7vK7+Rd/Xnh757fyZ4XOydvkkI8L2ha3boemAnv+1mmfHFhbp+4Njde8kq+/cn7lEG8IZTV1tbWljrE7uDHP/5xzj333LznPe/J1KlTN9l/yy23ZOLEiXniiSeybt269OvXL5/5zGdy5pln7tRc69Yk90zeqQ/RZE4aX5bffW3Z+k8ZW/D8k7l86ofyyqsvpm3rjhn/nv9df7vbt37z0Qzvf1qOHnDaFsdt7IIfnJAzjv3M+o8A3dKxS178Z771m3Py8sqqNCtrlg+M/HKOHfTOJMk5l/fPhPf9PAf1GLr+3FvL0ZjMOzPvtvyMG/L97WqZG5u3KZ8Xu/vPeHfM3NR5i/i82FV+J+/Kzwt/9/xO9rzYeX/3ivK82DjXlrZvbmySXParD6XPfkN2qY+dHzEuad6yaR7rjfRealfUlNeSxlEINdD555+fyZMn5+tf/3ouuuiiUsdZ7430S2zjQmhX9dKKF3LpdWdm4nl3lTpKg+xueROZm8LuljeRuSnsbnmT3S/z7pY3kbkp7G55E5mbQlPkVQi9cd5L7YoUQrs+hVADnXjiibnnnntyyy235JRTTil1nPXeSL/E3vPVinTqsE8+MurSHHnI20sdBwAA3rD+64cj8tzS+Xn/Wy7apZaVUAi9cSiEdn0WlW6gadOmlTrCG96vL64sdQQAACiESR+/p9QRgBKzqDQAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCaDOqqqoyYcKE9OnTJ61bt06PHj1y/vnnZ+XKlTnnnHNSVlaWq666qtQxAQAAALZZeakD7Ipmz56dUaNGpbKyMu3atUv//v2zePHiTJ48OfPmzcvSpUuTJEOGDClt0AZa9dqK/O7Pk3PP7Cl5btn8tGjeKt336pe3H3le3jr07JSVlZU6IgAAwC5n2fLn8rM7L86Dc27NshXPpVOHihxz6H/k7Ld+Ne3b7FnqeLBdFEIbqaqqyujRo1NZWZkLLrggF198cTp06JAkueyyy3LhhRemvLw8ZWVlGTRoUInTbl1NTU0u+smoPDH//pw09Oycfsyn89raV3PP36Zk0q8/nH89PyfnnjKx1DEBAAB2KctWPJ9PX3lkXnxlcU456mM5YJ9DM/+5x3LLX36QR/95X77zqRlp3bJtqWNCoymENjJu3LgsXLgwY8eOzaRJk+rsmzBhQq677ro88sgj6dWrVzp27FiilA33j389kMee+XPOOPYz+cRpV6zfPnr4J/ORyw/OrX+9WiEEAACwkSl3fyPPLXs2nz/zupx42PvXb+/f8+hcet2ZueG+b2fMyC+WMCFsH2sIbWDOnDmZOnVqunbtmksvvbTeMYcffniSZPDgwXW2//GPf8xRRx2V1q1bZ++9987HP/7xvPzyyzs989asfO2VJEmXjt3qbG9R3jJ7tOua1i3blSIWAADALu2RefekVYs2GTHkfXW2nzD4vWlZ3jp3zPzfEiWDHUMhtIEpU6akpqYmY8aMSfv27esd06ZNmyR1C6F77703J598cvbbb7/87ne/y9e//vVcf/31Of3001NbW9sk2Tfn4B7D0r7Nnvn1ny7LvY/8Js8v+1f+9fw/8pPbPp+5Cx/KB9/6lZLmAwAA2BWtrX4tLctbb7LmarNmzdKqRZssWfrPvLyyqkTpYPu5ZWwD06ZNS5KMGDFis2MWLlyYpG4h9LWvfS19+/bNb37zmzRr9nrH1qVLl7zzne/MrbfemlNPPXUnpt6yDm075Wsfuinfvv6jueTa96zf3rZVh3z5gzfkmENPL1k2AACAXVXPfQZkwQtP5ulFs9NnvyHrtz+9aHaWr1qWJHl+2b+yR7uuJUoI20chtIFnn302SdKzZ89691dXV2fGjBlJ6hZCDzzwQD784Q+vL4OS5K1vfWuS5Pe//32jCqGhQ4emsrJyq+NalrfJj8bO3eKYNq3a54B9Ds3w/qelf8+js3zV0tx0//dy6XVn5qsfujGH9ztpm/MBAADsaH379c2a6lVN8lhbey91xrGfyf2P/z6XXPuefOK076RXxaGZ/9zj+cFNn0l58xapXrc2r619tUmy7o6a8loWWUVFRWbNmtWoYxVCG1i5cmWSZNWq+p+0U6dOTVVVVTp06JBevXqt3968efO0bNmyztgWLVqkrKwsjz/+eKOyVFZWZtGiRVsd17rFlle1f2bJozn/qqPz8dOuyOjhH1+/fcSQ9+fcbx2aK64/Nz/73Lw0b9a8UTkBAAB2lCWLF2d1E5UsW3svNbD3sblozK/y/RvH5Ys/PSVJ0qxZ84wa9tH03GdAZjz2u7Rttet/0FCpNOW1pHEUQhuoqKjIsmXL8vDDD2f48OF19i1ZsiTjx49PkgwaNKjOfaT9+vXLAw88UGf8zJkzU1tbm6VLlzY6S0O0LG+zxf03TL8ia6pX5/hB766zvXXLtjnykFNy44yr8tzS+enW9cBG5QQAANhR9u3WrUlnCG3N8YPfnTcPPCPPLHk0q15bnu57H5RO7ffO2MnD0rxZebp17dMESXdPTXkti6yh3UF9FEIbGDlyZObMmZOJEyfmpJNOSr9+/ZK8Xu6cddZZqap6fcGwIUOG1Dlu3Lhx+eAHP5hLLrkkH//4x7Nw4cJ88pOfTPPmzevcRrYtGjrla92a5J7Jm99f9fLrs4zW1a7b9Nh11a//t6Z62wMCAADsYHOfmpvmLbc+bkfY2nupf2verHmdNYSWvlKZpxf9LYN6H5/WLbc8y6jImvJa0jg+ZWwDEyZMSJcuXbJgwYIMGDAgAwcOTN++fTNs2LD07t07J554YpJNP3L+Ax/4QC688ML893//d/baa68MHTo0I0aMyJAhQ7LvvvuW4ltZr+c+/ZMkd868ps72Fateyv1P3JgObTpptQEAABqgpqYm37txXGpq1+XMt3yh1HFgu5ghtIHu3btn+vTpGT9+fO69997Mnz8//fv3z9VXX51zzz03Bx74+m1VGxdCZWVl+eY3v5kvfOELeeaZZ7Lffvtljz32SJcuXfLpT3+6FN/Kemcc+5nc9dDP85M/fC7PVD6aAQcck+WvLs0fHvifLH1lST79H9+zfhAAAMBGVr22ImMnD8sxh/5HKjr3ysrVL+ee2VMyd+FD+fDJX8+QPpv/dGrYHZTV1tbWljrE7mDFihXp2LFjysrKsnz58rRtu+Wpgf/zP/+TT33qU5kzZ876ImlnaMg0x8VV83LtH7+Wvz19d5Ytfy6tWrTJgd2G5D+O/UyOHXjGTssGAACwLUaMyy5zy9ja6jW5bOrZ+cezf82Ly5ekdYu26dfjiLzzuM/miIPe1jQhd2NNeS1pHDOEGujxxx9PbW1t+vXrt0kZNGvWrNx1111505velOrq6vzxj3/M5MmTM2nSpJ1aBjVUt64HZsL7flbqGAAAALuNFuUt84UxU0odA3YahVADPfroo0k2vV0sSVq1apWbb745l156aaqrqzNw4MBMnTo173rXu5o6JgAAAMBWKYQaaEuF0MCBA3P//fc3dSQAAACARvEpYw20pUIIAAAAYHdihlADTZs2rdQRAAAAAHYIM4QAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFU7hCqKqqKhMmTEifPn3SunXr9OjRI+eff35WrlyZc845J2VlZbnqqqtKHXOHWrb8uXznho/nzEt6ZNTnWubMr++f7914flaseqnU0QAAAHZZU6Zdmq/94t0569LeOWl8WT7wjQO2OH7Ovx7IhKtH5rQvdsg7vtgxn/+fk/P0otlNkhW2VXmpAzSl2bNnZ9SoUamsrEy7du3Sv3//LF68OJMnT868efOydOnSJMmQIUNKG3QHWrbi+Xz6yiPz4iuLc8pRH8sB+xya+c89llv+8oM8+s/78p1PzUjrlm1LHRMAAGCX89M/XJQObTun735vysqt/A/1J579a/7rhyeka8f9cvZbv5YkuXHGVfnsD47Ndz91f3rtO7AJEkPDFaYQqqqqyujRo1NZWZkLLrggF198cTp06JAkueyyy3LhhRemvLw8ZWVlGTRoUInT7jhT7v5Gnlv2bD5/5nU58bD3r9/ev+fRufS6M3PDfd/OmJFfLGFCAACAXdPPPzcv+3bpnSQ5d9KhWbVmxWbHfv/GcWnRvGW+/cn70nWP/ZIkxw9+T865/JD88OYLMvG8O5skMzRUYW4ZGzduXBYuXJixY8dm0qRJ68ugJJkwYUIGDx6c6urqHHDAAenYsWMJk+5Yj8y7J61atMmIIe+rs/2Ewe9Ny/LWuWPm/5YoGQAAwK7t32XQ1iyqejpPLpiZ4wa9e30ZlCRd99gvxw16d/729B+z9JXKnRUTGqUQhdCcOXMyderUdO3aNZdeemm9Yw4//PAkyeDBg9dv+3eBNGzYsLRq1SplZWWbfYxnnnkmp512Wjp06JBOnTrlgx/8YF588cUd+400wtrq19KyvPUm2Zs1a5ZWLdpkydJ/5uWVVSVKBwAAsPt7csHMJMkhPYdvsu+QnkeltrY2cxc91NSxYIsKUQhNmTIlNTU1GTNmTNq3b1/vmDZt2iSpWwg9/fTTueGGG1JRUZEjjjhis+dfvnx5RowYkYULF2bKlCn50Y9+lOnTp+fUU09NTU3Njv1mtlHPfQZk+aplmyxk9vSi2Vm+almS5Pll/ypBMgAAgDeGF19ZnCR1Zgf9W5eOr2+renlRk2aCrSnEGkLTpk1LkowYMWKzYxYuXJikbiF03HHHZcmSJUmSr3zlK5kxY0a9x/7oRz/KokWLct9992X//fdPknTv3j1HH310brrpppx++unbnHno0KGprNz6lMKW5W3yo7FzN7v/jGM/k/sf/30uufY9+cRp30mvikMz/7nH84ObPpPy5i1SvW5tXlv76jbnAwAA2NH69uubNdWrmuSxtvZealu8tub191Qtyltt+jgtWr8+pmDvu5ryWhZZRUVFZs2a1ahjC1EIPfvss0mSnj171ru/urp6fdmzYSHUrFnDJlDdcsstefOb37y+DEqS4cOHp3fv3rn55psbVQhVVlZm0aKtN8itW2z5E8IG9j42F435Vb5/47h88aenJEmaNWueUcM+mp77DMiMx36Xtq3eOGsmAQAAu68lixdndRMVJ1t7L7UtWv3fJzevrX5tk31r1q5+fcwOfLzdQVNeSxqnEIXQypUrkySrVtXfTk6dOjVVVVXp0KFDevXqtc3nf+KJJ/Lud797k+0DBgzIE088sc3nS15v+RqiZXmbrY45fvC78+aBZ+SZJY9m1WvL033vg9Kp/d4ZO3lYmjcrT7eufRqVEQAAYEfat1u3Jp0htKN06dgtSf23hb34yuvb6rud7I2sKa9lkTW0O6hPIQqhioqKLFu2LA8//HCGD6+7yNeSJUsyfvz4JMmgQYO2uHD05ixbtix77rnnJts7d+6cJ598slGZGzrla92a5J7JWx/XvFnz9NlvyPqvl75SmacX/S2Deh+f1i2L1VQDAAC7prlPzU3zlk3zWA19L9UQB/V4fc3ZOc/+JW8/8qN19s159q8pKytL3/0O3zEPtptoymtJ4xRiUemRI0cmSSZOnJinnnpq/faZM2dmxIgRqap6/VO2hgwZUop4Ta6mpibfu3FcamrX5cy3fKHUcQAAAHZr+3Xtk37dh+a+v/8mVS8vXr+96uXFue/vv8mQA09M546Nn8kBO0MhZghNmDAh1113XRYsWJABAwbk4IMPzurVq/P0009n1KhROeCAA3LHHXfUWT9oW3Tq1CkvvfTSJtuXLl2azp07b2f67bPqtRUZO3lYjjn0P1LRuVdWrn4598yekrkLH8qHT/56hvTZ/ELbAAAARXbXQ7/I88teX5P2pZUvpHrdmvzyj5ckSfbu1DMnHX7W+rGffMd3M/6HI/LZ7x+b09/86STJ7/98ZWpqa/Kx0d9q+vCwFYUohLp3757p06dn/PjxuffeezN//vz0798/V199dc4999wceOCBSdLoQuiQQw6pd62gJ554Iscdd9x2Zd9e5c1bpne3wbnnb9flxeVL0rpF2/TrcUS+8dHbc8RBbytpNgAAgF3Z7Q/+JH//5711tl1zx5eSJIN6H1+nEBpwwNGZ9PE/5X/v+GL+9/YvpqysLP17Hp0vnfWbHNitce81YWcqRCGUvF7a3HLLLZtsX7FiRebPn59mzZrl0EMPbdS5Tz311Fx00UVZuHBhunfvniR54IEHMm/evFx++eXblXt7tShvmS+MmVLSDAAAALujb33iT9s0vv8Bw3P5x+7eOWFgBytMIbQ5jz/+eGpra9OvX7+0bbvp4srXX399kqyfAfTvrw844IAMHTo0SXLeeeflyiuvzDve8Y589atfzerVqzNhwoQMGzYs73jHO5roOwEAAABomMIXQo8++miSzd8utvHHyf/767PPPjvXXHNNkqRjx46ZNm1azj///Lzvfe9LeXl5Tj311FxxxRVp1qwQ63YDAAAAuxGF0FYKodra2gad58ADD6z3ljQAAACAXU3hp69srRACAAAAeKMp/AyhadOmlToCAAAAQJMq/AwhAAAAgKJRCAEAAAAUjEIIAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABRMWW1tbW2pQ9B4tbVJzdpSpwAAANh+zVokZWVN81jeS+1cTXktaRyFEAAAAEDBuGUMAAAAoGAUQgAAAAAFoxACAAAAKBiFEAAAAEDBKIQAAAAACkYhBAAAAFAwCiEAAACAglEIAQAAABSMQggAAACgYBRCAAAAAAWjEAIAAAAoGIUQAAAAQMEohAAAAAAKRiEEAAAAUDAKIQAAAICCUQgBAAAAFIxCCAAAAKBgFEIAAAAABaMQAgAAACgYhRAAAABAwSiEAAAAAApGIQQAAABQMAohAAAAgIJRCAEAAAAUjEIIAAAAoGD+HzjgDO25/W0TAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1475.88x953.167 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1475.88x953.167 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the circuit for horizontal scan\n",
    "\n",
    "qc_h_32_rgb = QuantumCircuit(total_q_32_rgb)\n",
    "qc_h_32_rgb.initialize(h_norm_image_32_rgb, range(1, total_q_32_rgb))\n",
    "qc_h_32_rgb.h(0)\n",
    "qc_h_32_rgb.unitary(Amp_permutation_unitary_32_rgb, range(total_q_32_rgb))\n",
    "qc_h_32_rgb.h(0)\n",
    "\n",
    "display(qc_h_32_rgb.draw('mpl', fold=-1))\n",
    "\n",
    "\n",
    "# Create the circuit for vertical scan\n",
    "qc_v_32_rgb = QuantumCircuit(total_q_32_rgb)\n",
    "qc_v_32_rgb.initialize(v_norm_image_32_rgb, range(1, total_q_32_rgb))\n",
    "qc_v_32_rgb.h(0)\n",
    "qc_v_32_rgb.unitary(Amp_permutation_unitary_32_rgb, range(total_q_32_rgb))\n",
    "qc_v_32_rgb.h(0)\n",
    "\n",
    "display(qc_v_32_rgb.draw('mpl', fold=-1))\n",
    "\n",
    "# Combine both circuits into a single list\n",
    "circ_list_32_rgb = [qc_h_32_rgb, qc_v_32_rgb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1391bd58-a471-47c5-acf5-1c595d0b4245",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/865111609.py: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",
      "  print(\"size is \",state_vector_h_32_rgb.size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size is  2048\n",
      "Horizontal scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0203111116 & 0.0203111116 & 0.0203111116 & -0.0203111116 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & \\cdots & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0203111116 & 0.0203111116 & 0.0203111116 & -0.0203111116 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Vertical scan statevector:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & \\cdots & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0 & 0.0406222232 & 0  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulating cirucits\n",
    "\n",
    "back = Aer.get_backend('statevector_simulator')\n",
    "results = execute(circ_list_32_rgb, backend=back).result()\n",
    "state_vector_h_32_rgb = results.get_statevector(qc_h_32_rgb)\n",
    "state_vector_v_32_rgb = results.get_statevector(qc_v_32_rgb)\n",
    "print(\"size is \",state_vector_h_32_rgb.size)\n",
    "from qiskit.visualization import array_to_latex\n",
    "print('Horizontal scan statevector:')\n",
    "display(array_to_latex(state_vector_h_32_rgb, max_size=128))\n",
    "print()\n",
    "print('Vertical scan statevector:')\n",
    "display(array_to_latex(state_vector_v_32_rgb, max_size=128))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b91236e5-c8b6-470d-af7a-33758b4f1a93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  postprocessing for plotting the output (Classical)\n",
    "\n",
    "# Defining a lambda function for thresholding to binary values\n",
    "# returns true for specified Amplitude values else false\n",
    "threshold = lambda amp: (amp > 1e-15 or amp < -1e-15)\n",
    "\n",
    "# Selecting odd states from the raw statevector and\n",
    "# reshaping column vector of size 64 to an 8x8 matrix\n",
    "h_edge_scan_img_32_rgb = np.abs(np.array([1 if threshold(state_vector_h_32_rgb[2*(i)+1].real) else 0 for i in range(2**data_q_32_rgb)])).reshape(32, 32)\n",
    "v_edge_scan_img_32_rgb= np.abs(np.array([1 if threshold(state_vector_v_32_rgb[2*(i)+1].real) else 0 for i in range(2**data_q_32_rgb)])).reshape(32, 32).T\n",
    "\n",
    "# Plotting the Horizontal and vertical scans\n",
    "plot_image(h_edge_scan_img_32_rgb, 'Horizontal scan output')\n",
    "plot_image(v_edge_scan_img_32_rgb, 'Vertical scan output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00c7356f-d1a7-4e66-9925-02bdccbd074c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combining the horizontal and vertical component of the result by or operator\n",
    "edge_scan_image_32_rgb = h_edge_scan_img_32_rgb | v_edge_scan_img_32_rgb\n",
    "\n",
    "# Plotting the original and edge-detected images\n",
    "plot_image(imggg_, 'Original image')\n",
    "plot_image(edge_scan_image_32_rgb, 'Edge-Detected image')"
   ]
  }
 ],
 "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": "3.10.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}