{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e09548a",
   "metadata": {},
   "source": [
    "# QCNNClassifier demonstration on binary MNIST\n",
    "\n",
    "This notebook presents the `QCNNClassifier` class model to use for vision classification tasks. \"QCNN\" stands for Quantum Convolutional Neural Network which is mostly used to process images. The architecture of this model is based upon the architecture proposed by [Monbroussou et al. (2025)](https://arxiv.org/abs/2504.20989).\n",
    "\n",
    "For this demonstration, we will use the `QCNNClassifier` on MNIST of size 8x8 for binary classification between digits 0 and 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b9fa1e",
   "metadata": {},
   "source": [
    "## 0. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4f220b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import merlin\n",
    "import numpy as np\n",
    "import perceval\n",
    "import torch\n",
    "\n",
    "from merlin.measurement import MeasurementStrategy\n",
    "from merlin.models import QCNNClassifier\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e536562",
   "metadata": {},
   "source": [
    "# 1. Dataset\n",
    "\n",
    "Fetch the 8x8 MNIST dataset from sklearn and select labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d5458fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P18q89NJLR31d9qF98MEH+U//M5999ll+ncDFF1+c384uEs7uO//88xf5FVAn+g09x6CzzqHn/tfVV1+d7y9m3xeXXXZZfl92LWq275ldPFw3tQ0Chw4dSp1O54j7VqxYke+4//zzz/kV3DfccEN+es8zzzxzVP2JJ56Y7rvvvvyi4uw0oXvvvTddddVVh4PBww8/nKfR8847L9100015qMhOF/roo4/SI488csw5ZRcXn3POOflpSX/k888/z1Nj9m92qtG/XkM272XLlpV8V/iz6DcWm55Dz9muDro6rnMXX3xxuu6669Kdd96ZzymbZzZu9h/OnH322al2+jW9qCmb+r//2bx5c/749u3b84t6TznllP6GDRv6u3btyh8/ePDgERfqvvLKK/1Vq1b1TzrppP769ev78/PzR4yzd+/e/tq1a/PnWb58eX/NmjX955577g8vFl63bl0+tyJz37dv3wK/SywU/cZi03PouX+yXR1cdV7nvv766/6tt97aX7ZsWf6cmzZt6n/33Xf9OhrK/qo6jAAAAItrIH+PAAAA8J8JAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABDQcf9CsaGhoVSV0dHRwrX/+Mc/Ctfu2bMnlTE+Pp7qaKn8j7JV9lxVpqamStU/8cQThWs3bdpUauxWq1W4Vs/Vt+cmJycL105PT5cau91uF67Vc6my7VuZtSLT7XYL146NjZUaO/uNsnXuuSq3q2XWmjLbtuyXlpXRbDYr6ZeyjrffHBEAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhoqN/v94/rC4eGUh0d58v7U2zatKlwbavVShHfs0HoubGxscK1+/btS1X5r//6r1L1s7OzhWv1XHV6vV6p+pGRkcK1W7duLTX29PR04Vo9l1Kz2azk+33lypWpKtHXuTLb1fHx8VJj7969u3Dt3Nxc4drVq1enMraWWKfKrFFlHW+/OSIAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAENpwE3NzdXuHb16tWlxm40GqXqKa7ZbJZ6+2ZmZgrXjo2NpToaHR0tVT87O7tgc4mozHpR5r0fGRlJVWm325WNTUqTk5OF34aVK1d6C4Mpu23bs2dP4drx8fHK5r1v377KtouLsV11RAAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAIKDhNOC63W7h2tWrV5cae3R0tFQ9xU1PT5d6+zZu3Fi4dm5urnBtp9NJZUxMTFQ2NuW02+3K1qqq6LlyJicnS9VPTU2laPRcdfs0s7OzpeqrGnfPnj2VfY8txnvmiAAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQEDDacA1Go3Kxp6dna1s7OimpqZK1c/MzBSu7XQ6hWvHxsZSGRMTE7X8XhkE09PTperXrVtXuPZvf/tb4dp//OMfqYy5ublS9aRK1pqyPTs6OlrJOlVWmXlH3673er0UUafCbfpicEQAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAIaTjUwOjpauHbdunWpKrOzs5WNHV2v1ytV3+l0Ut16vaxms1nZ2IOg1WqVqm+327Xr14X4XiNV9rmXqS/Tr1Wq8nul7sq+d+Pj44Vrp6enU1XGxsYK13a73bTUOSIAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABDQcKqBZrNZybiHDh0qVd/tdhdsLsRQVa9nGo1GZWMPgqjf7/omJp97PK1Wq1T9li1bCtfOzMwUrh0dHS1cW7Z+amoqLXWOCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQMOpBrrdbuHaubm5wrXNZrNwLdRNme8z6mvr1q2l6sfGxhZsLtRHq9WqbNtqrapG2fe9zFqzZcuWSvYDM5OTk6moTqeTljpHBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhrq9/v9qicBAAAsLkcEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACCgkEGg1WqlRqNR+nmGhoZSu91ekDkx2PQc+o1BZo1Dz9VTLYPA5ORkGh8fT3W0bdu2tHbt2nTqqacuSBhhcdS557755pt02223peXLl+c9t3nz5vT9999XPS0GtN+scfWk59BzMde5WgaBOvvpp5/SzTffnO6+++6qp0IQWQj4+OOP02uvvZZeffXV9NZbb6W77rqr6mkxoKxx6DkG3U8DtC83kEFg+/bt6ZJLLkmnnXZaOvfcc9M999xzzJ+AZqf1XHDBBenkk09OGzZsSF988cURj+/Zsyddeuml+eOrVq1KW7duTb/88kupuWXP8cADD+TzY3As1Z47cOBA2rt3b3r++efTlVdema655pr01FNPpRdffDF9+eWXhZ+Xai3VfstY4waTnkPPDeY6N5BB4IQTTkhPPvlk/lPQnTt3pjfeeCM9+OCDR3zNDz/8kB/a2bVrV3r33XdTr9dLt9xyy+HH33777XTHHXek+++/P33yySfp2Wefzc+BzGr+yNjYWH54lXiWas+99957+WHLyy+//PB969evz+e7f//+0q+baizVfmNw6Tn03IDq19DExER/48aNx/31L7/8cn/FihWHb+/YsaOfvfT333//8H0HDhzI79u/f39++9prr+0/+uijRzzPCy+80D/rrLMO386+fvfu3Ydv33777f2HHnrouOaUzWFkZOS4XwPVqmvPbdu2rX/hhRcedf8ZZ5zRf/rpp4/79bC46tpvv2eNqxc9h56Luc4NpwH0+uuvp8ceeyx9+umn6dtvv80Pdf/444/5T8iyCzsyw8PD6Yorrjhcc9FFF+U/Oc1OpVizZk2am5vLf4r2+5+O/frrr0c9z+9lP3kjJj2HfmOQWePQc4Np4IJAt9tN119/fX4BR7YTf/rpp6d33nkn/59Ssos7jrUDfyzZ+bbZOWA33njjUY9l59NCHXruzDPPTF999dUR92XBOPufhLLHqJ+l3G8MJj2HnhtcAxcEPvzww/Tbb7+lxx9/PD+nMfPSSy8d9XXZztAHH3yQ//Q/89lnn+Xn0F588cX57ewCuuy+888/f5FfAXWzlHvu6quvzsfI5njZZZfl92Xnk2fzzS4epn6Wcr8xmPQcem5w1TYIHDp0KHU6nSPuW7FiRb5R+/nnn/P/GeWGG27IT+955plnjqo/8cQT03333ZdfcJedJnTvvfemq6666vBG8+GHH85/6nbeeeelm266Kd/gZqcLffTRR+mRRx455pyyC+/OOeec/LSkP/L555/nP43N/s1ONfrXa8jmvWzZspLvCn+mOvZcttN33XXXpTvvvDOfUzbPbNzsotGzzz57Qd4X/hx17LeMNa6+9Bx6LsVb5/o1vagpm/q//9m8eXP++Pbt2/ML3k455ZT+hg0b+rt27cofP3jw4BEXd7zyyiv9VatW9U866aT++vXr+/Pz80eMs3fv3v7atWvz51m+fHl/zZo1/eeee+4PL6Rbt25dPrcic9+3b98Cv0sspDr33Ndff92/9dZb+8uWLcufc9OmTf3vvvtOgyxhde43a1w96Tn0XMx1bij7q+owAgAALK6B/D0CAADAfyYIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQEDH/QvFhoaGUlUmJycL105NTRWuHR0dTREtlf9RtsqeazabhWv//ZdALVavZ9rtdqojPVdO9huDq+jXzPj4eCXzLkvPlds+PvHEE4Vrs19cV8b09HQt18il0HNVblfLvP6tW7dW0i91drzvtyMCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABDacaaDabhWtXr15dybiZbrdbqp7qzMzMFK4dGRkpXLt79+5UxqZNmwrXtlqtUmNTzvj4eCU9t27dulTVvPVctSYnJysZt8x2meqU3Seqap2Znp5e0LkMGkcEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACCg4VQD3W63knEbjUYl41Le6OhoZfV/+9vfCteOjY2lMnbs2FG4ttPplBq7bH10dX3/6jrvQVB2G7V69epUR1XtE1CdMr06OTlZauxWq5UGmSMCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABDaca6HQ6hWsPHTpUuHZ0dDRVNW/KGRsbK1XfarUq+dzL9kyj0ShcOz4+Xmps/R5Tt9utegoUND8/X/i9W7lyZSXb5Yy1pp7f61u3bi1cu2XLllruD9SBIwIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAENpxrodDqFa9vtduHasbGxVEar1SpVT3HdbrfU2zc6OlrLt392drayfqecqt7/ubm5UvW9Xm/B5sLiWrlyZS3f8kajUbhWv1Znenq6cO3k5GTh2mazWbg2AkcEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACCg4TTgZmZmCtfOzs4u6FxYPL1er1T92NhYqqO6zpuU2u124bdhx44dhWtXr15d6u1vNpuFa7vdbqmxoyu7zs3PzxeuXblyZeHakZGRVMbo6GjhWtv1eirzuU1MTJQauznga5wjAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQ2nGpiamipcOzo6Wri21+ulMmZnZwvXzszMVDb2ICj7+pvNZiWfXdme27JlS+Hav/71r6XGJlXWc2UcOnSoVH2j0ViwubC4pqenC9fu2LEjVaXMdj36trFK4+PjtVsfy27TO51OKqPdbqc/myMCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABDQcKqBXq9XuHZiYiJVZeXKlYVr161bV2rsN998s1R9dOPj44Vr2+124dpGo5HK2LRpU+HabrdbamzK6XQ6hWsPHTpUuHZkZCSVoW/qq8xnNz8/X8k2fSHqqcbU1FRl+0RlbNy4sZLahdgnOB6OCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABDTU7/f7VU8CAABYXI4IAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAIYNAq9VKjUaj9PMMDQ2ldru9IHNicOk39ByDzjqHnqunWgaBycnJND4+nurom2++Sbfddltavnx5HkY2b96cvv/++6qnxYD227Zt29LatWvTqaeeuiDhl8VR556zxtVTnXvOOldPde65bwZoX66WQaDOssb5+OOP02uvvZZeffXV9NZbb6W77rqr6mkxoH766ad08803p7vvvrvqqRCENY7FZp1jsd02QPtyAxkEtm/fni655JJ02mmnpXPPPTfdc889x0xq2Wk9F1xwQTr55JPThg0b0hdffHHE43v27EmXXnpp/viqVavS1q1b0y+//FJ4XgcOHEh79+5Nzz//fLryyivTNddck5566qn04osvpi+//LLw81Ktpdpvmew5HnjggXx+DI6l2nPWuMG1VHsuY50bTEu15w4M2L7cQAaBE044IT355JN5Wtu5c2d644030oMPPnjE1/zwww/54cRdu3ald999N/V6vXTLLbccfvztt99Od9xxR7r//vvTJ598kp599tn8HMis5o+MjY3lh7r+yHvvvZcfQrr88ssP37d+/fp8vvv37y/9uqnGUu03BtdS7Tlr3OBaqj3H4FqqPffeoO3L9WtoYmKiv3HjxuP++pdffrm/YsWKw7d37NjRz176+++/f/i+AwcO5Pft378/v33ttdf2H3300SOe54UXXuifddZZh29nX7979+7Dt2+//fb+Qw899Ifz2LZtW//CCy886v4zzjij//TTTx/362Fx1bXffi+bw8jIyHG/BqpV156zxtVXXXvu96xz9VLXnts2YPtyw2kAvf766+mxxx5Ln376afr222/zQ0A//vhjnhyziyYzw8PD6Yorrjhcc9FFF+UJLzvks2bNmjQ3N5eny9+nxl9//fWo5/m9LJESj35DzzHorHPoucE0cEGg2+2m66+/Pr84MtuJP/3009M777yTX9GdXVB0rB34Y8nOQ8vOI7vxxhuPeiw7z6yIM888M3311VdH3JeFlOzq8+wx6mcp9xuDaSn3nDVuMC3lnmMwLeWeO3PA9uUGLgh8+OGH6bfffkuPP/54fr5W5qWXXjrq67IP7YMPPsh/+p/57LPP8nPLLr744vx2dmFJdt/555+/YHO7+uqr8zGyOV522WX5fdk5b9l8swtOqJ+l3G8MpqXcc9a4wbSUe47BtJR77uoB25erbRA4dOhQ6nQ6R9y3YsWK/MP++eef8yu4b7jhhvz0nmeeeeao+hNPPDHdd999+YUo2WlC9957b7rqqqsON9PDDz+cp9Hzzjsv3XTTTXkjZqcLffTRR+mRRx455pyyC1LOOeec/LSkY8ka87rrrkt33nlnPqdsntm42YUtZ5999oK8L/w56thvmc8//zz/KUX2b3Zq279eQzbvZcuWlXxX+DPVseescfVWx57LWOfqq449d/Gg7cv1a3qBSTb1f/+zefPm/PHt27fnF4Kccsop/Q0bNvR37dqVP37w4MEjLih65ZVX+qtWreqfdNJJ/fXr1/fn5+ePGGfv3r39tWvX5s+zfPny/po1a/rPPffcH15gsm7dunxu/8nXX3/dv/XWW/vLli3Ln3PTpk397777boHfIRZSnfvtj+a+b98+TbKE1bnnrHH1VOees87VU5177usB2pcbyv6qOowAAACLayB/jwAAAPCfCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQ0HH/QrGhoaFUlbGxscK1+/btK/WLLsoYHR0t9eu1q7JU/kfZKnuuKlNTU5XVz8zMlBq7TL2eK6fValWyTmUmJycL1/77LxJaTHquOu12u1R9mZ4dHx8vNXaZnl0KPVfldrXZbBaunZ2dLVyb/RbgqvZBeyXHLuN4+80RAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgIZTDUxNTVUybrfbLVXfbrcL146OjpYam+qU+eyeeOKJVJVms1nZ2KQ0PT1d+G2YmJgoXDs/P1/q7Z+ZmSlcOzY2VmpsUi3Xi40bN6aq9Hq9ysaObnZ2tnDtypUrK/vMW61W4drx8fG01DkiAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABDS/GII1Go1T9xo0bC9du3bq1cO3MzEwq4+DBg4Vrm81mqbG73W6p+ugmJycr65uqdDqdqqdQa6Ojo6Xqt2zZUrh2bm6usnmXWWvKbht6vV6pelIt17n5+fnCtbaNxY2NjZWoTmnlypWFa/fs2VPJ9jwzOzubqlpfF2O77IgAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAw4sxyOTkZKpKt9stXNvr9UqNvWfPnsres+np6RTZ+Ph4qfodO3akaGZnZ6ueQq01m83Kxp6ZmUl1NDY2Vqq+3W4v2FwiGh0dLVy7cePGVJVGo1HZ2JFV+b6X2acpuy/X6XQq25ebmppKfzZHBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhpOA67b7VY9BSrQ6/VK1W/dujVVYcuWLakqvlfKaTQaqSqtVquWr7vs92l0ZXuuyr4pw1pVz36bm5srXNvpdFJVeiXWqWazmZY6RwQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAIKDhxRik0+mUqj906FAlYzcajVTG2NhY4drZ2dlSY0dX9v0rUz86Olq4dsuWLakqZea9EN/n0Xtubm6ukrWqzDqVGRkZKVVPcePj46XevtWrVxeu/e///u/CtZOTk4Vrqe8aNzMzk6pQdl9uskS/VvWa/y8cEQAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACGio3+/3j+sLh4ZSVTqdTuHaXq9XuLbZbKYyGo1GZWOXed3H2RJ/uip7rqrPfXZ2ttTYq1evruX7redSarVahd+/iYmJVJX5+fnCtaOjo6XGts7VU9l1bt26dYVr//KXv4TuuSrX+Xa7Xcl2tew6U0Yd9uUcEQAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgIZTDUxOThaubbfbhWtXrlyZyti0aVPh2l6vV2psqlPmsyvTr5lms1mqnupMT08Xrm00GpX1zMzMTOFa61xMnU6nVL11rp6mpqYq2TaOjIykMv7+978P9BrniAAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQEBD/X6/X/UkAACAxeWIAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEJAgAAEBAggAAAAQkCAAAQECCAAAABCQIAABAQIIAAAAEFDIItFqt1Gg0Sj/P0NBQarfbCzInBpd+Q88x6Kxz6Ll6qmUQmJycTOPj46mOvvnmm3Tbbbel5cuX52Fk8+bN6fvvv696Wgxov23bti2tXbs2nXrqqQsSflkceo7FpufQczH35WoZBOosa5yPP/44vfbaa+nVV19Nb731VrrrrruqnhYD6qeffko333xzuvvuu6ueCkHoOfQcg+62AdqXG8ggsH379nTJJZek0047LZ177rnpnnvuOWZSy07rueCCC9LJJ5+cNmzYkL744osjHt+zZ0+69NJL88dXrVqVtm7dmn755ZfC8zpw4EDau3dvev7559OVV16ZrrnmmvTUU0+lF198MX355ZeFn5dqLdV+y2TP8cADD+TzY3DoOfTc/7LODaalus4dGLB9uYEMAieccEJ68skn87S2c+fO9MYbb6QHH3zwiK/54Ycf8tMmdu3ald59993U6/XSLbfccvjxt99+O91xxx3p/vvvT5988kl69tln83Mgs5o/MjY2lh9e/SPvvfdefgjp8ssvP3zf+vXr8/nu37+/9OumGku13xhceg49x6Bbquvce4O2L9evoYmJif7GjRuP++tffvnl/ooVKw7f3rFjRz976e+///7h+w4cOJDft3///vz2tdde23/00UePeJ4XXnihf9ZZZx2+nX397t27D9++/fbb+w899NAfzmPbtm39Cy+88Kj7zzjjjP7TTz993K+HxVXXfvu9bA4jIyPH/Rqolp5Dz/2TdW5w1XWd2zZg+3LDaQC9/vrr6bHHHkuffvpp+vbbb/NDQD/++GOeHLOLJjPDw8PpiiuuOFxz0UUX5QkvO+SzZs2aNDc3l6fL36fGX3/99ajn+b0skRKPfkPPMeisc+i5wTRwQaDb7abrr78+vzgy24k//fTT0zvvvJNf0Z1dxHasHfhjyc5Dy84ju/HGG496LDvPrIgzzzwzffXVV0fcl4WU7Orz7DHqZyn3G4NJz6HnGHRLeZ07c8D25QYuCHz44Yfpt99+S48//nh+vlbmpZdeOurrsg/tgw8+yH/6n/nss8/yc8suvvji/HZ2YUl23/nnn79gc7v66qvzMbI5XnbZZfl92Tlv2XyzC06on6XcbwwmPYeeY9At5XXu6gHbl6ttEDh06FDqdDpH3LdixYr8w/7555/zK7hvuOGG/PSeZ5555qj6E088Md133335hSjZaUL33ntvuuqqqw4308MPP5yn0fPOOy/ddNNNeSNmpwt99NFH6ZFHHjnmnLILUs4555z8tKRjyRrzuuuuS3feeWc+p2ye2bjZhS1nn332grwv/Dnq2G+Zzz//PP8pRfZvdmrbv15DNu9ly5aVfFf4M+k5FpueQ8+lePty/ZpeYJJN/d//bN68OX98+/bt+YUgp5xySn/Dhg39Xbt25Y8fPHjwiAsnX3nllf6qVav6J510Un/9+vX9+fn5I8bZu3dvf+3atfnzLF++vL9mzZr+c88994cXmKxbty6f23/y9ddf92+99db+smXL8ufctGlT/7vvvlvgd4iFVOd++6O579u3T5MsYXoOPfdP1rnBVed17usB2pcbyv6qOowAAACLayB/jwAAAPCfCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQ0HH/QrGhoaFUlVarVbh2fHy8cO3U1FSqat5VWir/o2yVPddutwvXNhqNwrVjY2MpIj1XTrPZrKTXM7Ozs5WtsWXouXLrzfT0dCVrZGZycrJw7b//YshoPVfldrXMOlVmnamy39ol19fF6DdHBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhpejEGazWap+omJicK18/PzhWt37NiRymg0GoVrZ2ZmSo1NdT27evVqbz+Lanx8vLJ+LfO9MjU1VWpsyhkdHa3kc1+5cmUqY3JysnCtnqvO9PR0ZT1TRqvVqmz/t9frpT+bIwIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAENDwYgzSbDZTVVqtVuHaTqdTauzdu3dXMu9Mr9dLkY2OjtayZ8fGxkrVz87OLthcqI+yfVPG9PR0ZWNTzszMTOHa8fHxwrUrV65MZUTfvtV1uzoxMVG4dufOnakqEyXmXfY9W4xtuiMCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABCQIAABCQIAAAAAEJAgAAEJAgAAAAAQkCAAAQkCAAAAABDS/GII1Go1T9m2++Wbh2eno6VaXMvKempkqNXeXrXgparVaqo2azWfUUqKGxsbHKxm6325WNTT3X2HXr1pUau9vtlqqnftunycnJWq6vzRps0x0RAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAIaHgxBhkdHS1V3+l0FmwuxDA5OVnZ2LOzs4Vrx8bGSo3darVK1VOdqampwrUjIyOFa+fn51MZ3W63VD31VHa7Dou1TS+7XeyWWOMajUZa6hwRAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICAhhdjkE6nU6p+amoqVaHRaJSqHx0drd1rHhRle66M2dnZwrUTExOlxp6cnCxVT3V8dtRJme1bndf3yMps2zKHDh2qZJ+o2+2mqvYFe71eWuocEQAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACEgQAACAgAQBAAAISBAAAICABAEAAAhIEAAAgIAEAQAACGh4MQbp9Xql6huNRuHasbGxwrXT09OpjG63W7i20+mUGptyms1mZf1exuTkZGU9p2fLabfblfTrypUrC9cSV5ntMvVUdts2OztbuHbjxo2Fa/ft25fKOHToUOHaVquVljpHBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgoKF+v98/ri8cGkpVmZmZKVx7//33F66dm5tLZUxNTRWunZ2dTVU5zpb401XZc2U+uyeeeCJF9Pe//71w7e7du1P0nitjdHS0cG2r1So1drvdLlw7PT2dqmKdK6fX6xWuHRkZKTX2nj17CteOj4+nyD1X5RrXbDYr2Q8sM26EfTlHBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhrq9/v9qicBAAAsLkcEAAAgIEEAAAACEgQAACAgQQAAAAISBAAAICBBAAAAAhIEAAAgIEEAAAACEgQAACDF8/8ALeyhHD/RAFIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_mnist(random_state, class_list=(0, 1)):\n",
    "    \"\"\"\n",
    "    Get MNIST dataset reduced to certain labels.\n",
    "\n",
    "    :param random_state\n",
    "    :param class_list: List of labels to keep\n",
    "\n",
    "    :return: x_train, x_test, y_train, y_test\n",
    "    \"\"\"\n",
    "    mnist_x, mnist_y = load_digits(return_X_y=True)\n",
    "\n",
    "    # Keep only selected classes\n",
    "    mask = np.isin(mnist_y, class_list)\n",
    "    mnist_x = mnist_x[mask]\n",
    "    mnist_y = mnist_y[mask]\n",
    "\n",
    "    # Train/test split\n",
    "    mnist_x_train, mnist_x_test, mnist_y_train, mnist_y_test = train_test_split(\n",
    "        mnist_x, mnist_y, test_size=200, random_state=random_state\n",
    "    )\n",
    "    # Since there are only 360 data points in this specific dataset with labels = 0 or 1, that implies that we will have 160 training points.\n",
    "\n",
    "    # Reshape to 8×8 images\n",
    "    mnist_x_train = mnist_x_train.reshape(-1, 8, 8)\n",
    "    mnist_x_test = mnist_x_test.reshape(-1, 8, 8)\n",
    "\n",
    "    return mnist_x_train, mnist_x_test, mnist_y_train, mnist_y_test\n",
    "\n",
    "\n",
    "# Visualize images from our training data\n",
    "x_train, x_test, y_train, y_test = get_mnist(42)\n",
    "\n",
    "fig, axes = plt.subplots(5, 5, figsize=(8, 8))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_train[i], cmap=\"gray\")\n",
    "    ax.set_title(f\"Label: {y_train[i]}\", fontsize=10)\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7898d99",
   "metadata": {},
   "source": [
    "Convert the training data to `torch.Tensor` (of shape \\[batch_size, 1, input_shape[0], input_shape[1]\\]) and to `torch.utils.data.DataLoader`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b1bfbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_dataset_to_tensor(x_train, x_test, y_train, y_test):\n",
    "    x_train = torch.tensor(x_train, dtype=torch.float32)\n",
    "    x_train = x_train.unsqueeze(1)\n",
    "    x_test = torch.tensor(x_test, dtype=torch.float32)\n",
    "    x_test = x_test.unsqueeze(1)\n",
    "    y_train = torch.tensor(y_train, dtype=torch.long)\n",
    "    y_test = torch.tensor(y_test, dtype=torch.long)\n",
    "    return x_train, x_test, y_train, y_test\n",
    "\n",
    "\n",
    "def convert_tensor_to_loader(x_train, y_train, batch_size=30):\n",
    "    train_dataset = torch.utils.data.TensorDataset(x_train, y_train)\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True\n",
    "    )\n",
    "    return train_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50027ce",
   "metadata": {},
   "source": [
    "## 2. Model definition\n",
    "\n",
    "The `QCNNClassifier` class takes three initialization parameters: `input_shape`, `num_classes` and `stages`. In this context, we have that `input_shape` = (8, 8) and `num_classes` = 2. We will not specify the `stages` parameter, so the default architecture will be used: a `QConv(2, 2)` layer followed by a `QPool(2)` layer, a `QDense()` layer and a Readout layer. Note that if you specify the `stages` directly, the `QDense` layer has to only appear as the last stage and that the Readout layer is always automatically added to the architecture to map from the output size of the `QDense` layer to the number of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb88c58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "qcnn = QCNNClassifier(input_shape=(8, 8), num_classes=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447e64f9",
   "metadata": {},
   "source": [
    "Before going straight to the classification task, let's analyze the structure of the default `QCNNClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36e5c284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QConv(kernel_size=2, stride=2)\n",
      "QPool(kernel_size=2)\n",
      "QDense()\n"
     ]
    }
   ],
   "source": [
    "stages = qcnn.resolved_stages\n",
    "\n",
    "for stage in stages:\n",
    "    print(stage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9373e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 0: QConv_1 of type <class 'merlin.algorithms.layer.QuantumLayer'>\n",
      "Layer 1: QPool_1 of type <class 'merlin.algorithms.layer.QuantumLayer'>\n",
      "Layer 2: QDense of type <class 'merlin.algorithms.layer.QuantumLayer'>\n",
      "Layer 3: Readout of type <class 'torch.nn.modules.linear.Linear'>\n"
     ]
    }
   ],
   "source": [
    "layers = qcnn.layers\n",
    "\n",
    "for index, (name, layer) in enumerate(layers.named_children()):\n",
    "    print(f\"Layer {index}: {name} of type {type(layer)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5aaf37a",
   "metadata": {},
   "source": [
    "We can access to QuantumLayer for each stage to visualize their photonic circuit using `perceval`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d869696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       "     width=\"275.0\" height=\"1031.25\" viewBox=\"-35.0 0 220.0 825.0\">\n",
       "<defs>\n",
       "</defs>\n",
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       "<text x=\"75\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_first_register</text>\n",
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       "<text x=\"98\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
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       "<text x=\"75\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_first_register</text>\n",
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       "<path d=\"M78,244 L97,225 L125,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"280\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"226\" font-size=\"7\" text-anchor=\"middle\">Θ=px_first_register</text>\n",
       "<path d=\"M50,243 L100,243 L100,247 L50,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,250 L103,250 L103,260 L93,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,325 L53,325 L72,344\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,344 L97,325 L125,325\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"380\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"326\" font-size=\"7\" text-anchor=\"middle\">Θ=px_first_register</text>\n",
       "<path d=\"M50,343 L100,343 L100,347 L50,347 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,350 L103,350 L103,360 L93,360 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"357\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,425 L53,425 L72,444\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,444 L97,425 L125,425\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"480\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"426\" font-size=\"7\" text-anchor=\"middle\">Θ=px_second_register</text>\n",
       "<path d=\"M50,443 L100,443 L100,447 L50,447 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,450 L103,450 L103,460 L93,460 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"457\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,525 L53,525 L72,544\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,544 L97,525 L125,525\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"580\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"526\" font-size=\"7\" text-anchor=\"middle\">Θ=px_second_register</text>\n",
       "<path d=\"M50,543 L100,543 L100,547 L50,547 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,550 L103,550 L103,560 L93,560 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"557\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,625 L53,625 L72,644\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,644 L97,625 L125,625\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M25,675 L53,675 L72,656\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,656 L97,675 L125,675\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M50,643 L100,643 L100,657 L50,657 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"75\" y=\"680\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"626\" font-size=\"7\" text-anchor=\"middle\">Θ=px_second_register</text>\n",
       "<path d=\"M50,643 L100,643 L100,647 L50,647 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,650 L103,650 L103,660 L93,660 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"657\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,725 L53,725 L72,744\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M78,744 L97,725 L125,725\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"726\" font-size=\"7\" text-anchor=\"middle\">Θ=px_second_register</text>\n",
       "<path d=\"M50,743 L100,743 L100,747 L50,747 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M93,750 L103,750 L103,760 L93,760 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"757\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M125,25.0 L140,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M125,75.0 L140,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M125,125.0 L140,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M125,175.0 L140,175.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
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       "<text x=\"150\" y=\"28.0\" font-size=\"10\" text-anchor=\"end\">0</text>\n",
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       "<text x=\"150\" y=\"178.0\" font-size=\"10\" text-anchor=\"end\">3</text>\n",
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       "<text x=\"0\" y=\"328.0\" font-size=\"10\" text-anchor=\"start\">6</text>\n",
       "<text x=\"0\" y=\"378.0\" font-size=\"10\" text-anchor=\"start\">7</text>\n",
       "<text x=\"0\" y=\"428.0\" font-size=\"10\" text-anchor=\"start\">8</text>\n",
       "<text x=\"0\" y=\"478.0\" font-size=\"10\" text-anchor=\"start\">9</text>\n",
       "<text x=\"0\" y=\"528.0\" font-size=\"10\" text-anchor=\"start\">10</text>\n",
       "<text x=\"0\" y=\"578.0\" font-size=\"10\" text-anchor=\"start\">11</text>\n",
       "<text x=\"0\" y=\"628.0\" font-size=\"10\" text-anchor=\"start\">12</text>\n",
       "<text x=\"0\" y=\"678.0\" font-size=\"10\" text-anchor=\"start\">13</text>\n",
       "<text x=\"0\" y=\"728.0\" font-size=\"10\" text-anchor=\"start\">14</text>\n",
       "<text x=\"0\" y=\"778.0\" font-size=\"10\" text-anchor=\"start\">15</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<drawsvg.drawing.Drawing at 0x13e853250>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_layer = layers[0]\n",
    "pool_layer = layers[1]\n",
    "dense_layer = layers[2]\n",
    "linear_layer = layers[3]\n",
    "\n",
    "conv_circuit = conv_layer.circuit\n",
    "perceval.pdisplay(conv_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77dc801b",
   "metadata": {},
   "source": [
    "This circuit is expected because the number of modes, for this architecture, equals the sum of the input dimensions (8 + 8 = 16) and the convolutions of `kernel_size` = 2 and `stride` = 2 are expected to be non-overlapping beam splitters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9387d863",
   "metadata": {},
   "source": [
    "The pooling layer does not utilize circuit components so its circuit visualization would be empty. Instead, we can verify that its `measurement_stategy` is that of partial measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5caf8931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pooling layer has measurement type: MeasurementKind.PARTIAL\n",
      "Measured modes: (0, 2, 4, 6, 8, 10, 12, 14)\n",
      "Computation space: ComputationSpace.FOCK\n"
     ]
    }
   ],
   "source": [
    "pool_m_strat = pool_layer.measurement_strategy\n",
    "assert isinstance(pool_m_strat, MeasurementStrategy)\n",
    "print(f\"Pooling layer has measurement type: {pool_m_strat.type}\")\n",
    "print(f\"Measured modes: {pool_m_strat.measured_modes}\")\n",
    "print(f\"Computation space: {pool_m_strat.computation_space}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03fb98ee",
   "metadata": {},
   "source": [
    "The measured modes we see there are what we expect with a pooling layer of `stride` = 2, since the pooling window measures the upper mode present before moving downward by `stride` modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9d6b707",
   "metadata": {},
   "outputs": [
    {
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       "<defs>\n",
       "</defs>\n",
       "<path d=\"M10,25.0 L25,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M10,75.0 L25,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M10,125.0 L25,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
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       "<text x=\"75\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"75\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_0</text>\n",
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       "<path d=\"M93,50 L103,50 L103,60 L93,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"98\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,125.0 L125,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M125,75 L153,75 L172,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<text x=\"175\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_1</text>\n",
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       "<text x=\"198\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,175.0 L225,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,125 L253,125 L272,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,144 L297,125 L325,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,175 L253,175 L272,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,156 L297,175 L325,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M250,143 L300,143 L300,157 L250,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"275\" y=\"180\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"275\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_2</text>\n",
       "<path d=\"M250,143 L300,143 L300,147 L250,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M293,150 L303,150 L303,160 L293,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"298\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M125,25.0 L225,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,25 L253,25 L272,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,44 L297,25 L325,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,75 L253,75 L272,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,56 L297,75 L325,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M250,43 L300,43 L300,57 L250,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"275\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"275\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_2_0</text>\n",
       "<path d=\"M250,43 L300,43 L300,47 L250,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M293,50 L303,50 L303,60 L293,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"298\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,225.0 L325,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M325,175 L353,175 L372,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M378,194 L397,175 L425,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M325,225 L353,225 L372,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M378,206 L397,225 L425,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M350,193 L400,193 L400,207 L350,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"375\" y=\"230\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"375\" y=\"176\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_3</text>\n",
       "<path d=\"M350,193 L400,193 L400,197 L350,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M393,200 L403,200 L403,210 L393,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"398\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M325,75 L353,75 L372,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M378,94 L397,75 L425,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M325,125 L353,125 L372,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M378,106 L397,125 L425,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M350,93 L400,93 L400,107 L350,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"375\" y=\"130\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"375\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_3_1</text>\n",
       "<path d=\"M350,93 L400,93 L400,97 L350,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M393,100 L403,100 L403,110 L393,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"398\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,275.0 L425,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M425,225 L453,225 L472,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,244 L497,225 L525,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M425,275 L453,275 L472,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,256 L497,275 L525,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M450,243 L500,243 L500,257 L450,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"475\" y=\"280\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"475\" y=\"226\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_4</text>\n",
       "<path d=\"M450,243 L500,243 L500,247 L450,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M493,250 L503,250 L503,260 L493,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"498\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M425,125 L453,125 L472,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,144 L497,125 L525,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M425,175 L453,175 L472,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,156 L497,175 L525,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M450,143 L500,143 L500,157 L450,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"475\" y=\"180\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"475\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_4_2</text>\n",
       "<path d=\"M450,143 L500,143 L500,147 L450,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M493,150 L503,150 L503,160 L493,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"498\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M325,25.0 L425,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M425,25 L453,25 L472,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,44 L497,25 L525,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M425,75 L453,75 L472,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M478,56 L497,75 L525,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M450,43 L500,43 L500,57 L450,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"475\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"475\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_4_0</text>\n",
       "<path d=\"M450,43 L500,43 L500,47 L450,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M493,50 L503,50 L503,60 L493,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"498\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,325.0 L525,325.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,275 L553,275 L572,294\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,294 L597,275 L625,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,325 L553,325 L572,306\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,306 L597,325 L625,325\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M550,293 L600,293 L600,307 L550,307 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"575\" y=\"330\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"575\" y=\"276\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_5</text>\n",
       "<path d=\"M550,293 L600,293 L600,297 L550,297 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M593,300 L603,300 L603,310 L593,310 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"598\" y=\"307\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M525,175 L553,175 L572,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,194 L597,175 L625,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,225 L553,225 L572,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,206 L597,225 L625,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M550,193 L600,193 L600,207 L550,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"575\" y=\"230\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"575\" y=\"176\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_5_3</text>\n",
       "<path d=\"M550,193 L600,193 L600,197 L550,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M593,200 L603,200 L603,210 L593,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"598\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M525,75 L553,75 L572,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,94 L597,75 L625,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,125 L553,125 L572,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,106 L597,125 L625,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M550,93 L600,93 L600,107 L550,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"575\" y=\"130\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"575\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_5_1</text>\n",
       "<path d=\"M550,93 L600,93 L600,97 L550,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M593,100 L603,100 L603,110 L593,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"598\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M25,375.0 L625,375.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,325 L653,325 L672,344\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,344 L697,325 L725,325\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,375 L653,375 L672,356\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,356 L697,375 L725,375\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M650,343 L700,343 L700,357 L650,357 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"675\" y=\"380\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"675\" y=\"326\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_6</text>\n",
       "<path d=\"M650,343 L700,343 L700,347 L650,347 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M693,350 L703,350 L703,360 L693,360 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"698\" y=\"357\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M625,225 L653,225 L672,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,244 L697,225 L725,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,275 L653,275 L672,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,256 L697,275 L725,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M650,243 L700,243 L700,257 L650,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"675\" y=\"280\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"675\" y=\"226\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_6_4</text>\n",
       "<path d=\"M650,243 L700,243 L700,247 L650,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M693,250 L703,250 L703,260 L693,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"698\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M625,125 L653,125 L672,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,144 L697,125 L725,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,175 L653,175 L672,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,156 L697,175 L725,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M650,143 L700,143 L700,157 L650,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"675\" y=\"180\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"675\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_6_2</text>\n",
       "<path d=\"M650,143 L700,143 L700,147 L650,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M693,150 L703,150 L703,160 L693,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"698\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M525,25.0 L625,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,25 L653,25 L672,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,44 L697,25 L725,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M625,75 L653,75 L672,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M678,56 L697,75 L725,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M650,43 L700,43 L700,57 L650,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"675\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"675\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_down_6_0</text>\n",
       "<path d=\"M650,43 L700,43 L700,47 L650,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M693,50 L703,50 L703,60 L693,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"698\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M725,275 L753,275 L772,294\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,294 L797,275 L825,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M725,325 L753,325 L772,306\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,306 L797,325 L825,325\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M750,293 L800,293 L800,307 L750,307 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"775\" y=\"330\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"775\" y=\"276\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_5</text>\n",
       "<path d=\"M750,293 L800,293 L800,297 L750,297 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M793,300 L803,300 L803,310 L793,310 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"798\" y=\"307\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M725,175 L753,175 L772,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,194 L797,175 L825,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M725,225 L753,225 L772,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,206 L797,225 L825,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M750,193 L800,193 L800,207 L750,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"775\" y=\"230\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"775\" y=\"176\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_5_3</text>\n",
       "<path d=\"M750,193 L800,193 L800,197 L750,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M793,200 L803,200 L803,210 L793,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"798\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M725,75 L753,75 L772,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,94 L797,75 L825,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M725,125 L753,125 L772,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M778,106 L797,125 L825,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M750,93 L800,93 L800,107 L750,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"775\" y=\"130\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"775\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_5_1</text>\n",
       "<path d=\"M750,93 L800,93 L800,97 L750,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M793,100 L803,100 L803,110 L793,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"798\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M825,225 L853,225 L872,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,244 L897,225 L925,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,275 L853,275 L872,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,256 L897,275 L925,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,243 L900,243 L900,257 L850,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"280\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"226\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_4</text>\n",
       "<path d=\"M850,243 L900,243 L900,247 L850,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,250 L903,250 L903,260 L893,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M825,125 L853,125 L872,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,144 L897,125 L925,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,175 L853,175 L872,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,156 L897,175 L925,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,143 L900,143 L900,157 L850,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"180\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_4_2</text>\n",
       "<path d=\"M850,143 L900,143 L900,147 L850,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,150 L903,150 L903,160 L893,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M725,25.0 L825,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,25 L853,25 L872,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,44 L897,25 L925,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,75 L853,75 L872,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,56 L897,75 L925,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,43 L900,43 L900,57 L850,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_4_0</text>\n",
       "<path d=\"M850,43 L900,43 L900,47 L850,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,50 L903,50 L903,60 L893,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M925,175 L953,175 L972,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M978,194 L997,175 L1025,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M925,225 L953,225 L972,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M978,206 L997,225 L1025,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M950,193 L1000,193 L1000,207 L950,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"975\" y=\"230\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"975\" y=\"176\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_3</text>\n",
       "<path d=\"M950,193 L1000,193 L1000,197 L950,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M993,200 L1003,200 L1003,210 L993,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"998\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M925,75 L953,75 L972,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M978,94 L997,75 L1025,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M925,125 L953,125 L972,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M978,106 L997,125 L1025,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M950,93 L1000,93 L1000,107 L950,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"975\" y=\"130\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"975\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_3_1</text>\n",
       "<path d=\"M950,93 L1000,93 L1000,97 L950,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M993,100 L1003,100 L1003,110 L993,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"998\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1025,125 L1053,125 L1072,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1078,144 L1097,125 L1125,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1025,175 L1053,175 L1072,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1078,156 L1097,175 L1125,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1050,143 L1100,143 L1100,157 L1050,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1075\" y=\"180\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1075\" y=\"126\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_2</text>\n",
       "<path d=\"M1050,143 L1100,143 L1100,147 L1050,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1093,150 L1103,150 L1103,160 L1093,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1098\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M925,25.0 L1025,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1025,25 L1053,25 L1072,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1078,44 L1097,25 L1125,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1025,75 L1053,75 L1072,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1078,56 L1097,75 L1125,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1050,43 L1100,43 L1100,57 L1050,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1075\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1075\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_2_0</text>\n",
       "<path d=\"M1050,43 L1100,43 L1100,47 L1050,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1093,50 L1103,50 L1103,60 L1093,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1098\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1125,75 L1153,75 L1172,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,94 L1197,75 L1225,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,125 L1153,125 L1172,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,106 L1197,125 L1225,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1150,93 L1200,93 L1200,107 L1150,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1175\" y=\"130\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1175\" y=\"76\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_1</text>\n",
       "<path d=\"M1150,93 L1200,93 L1200,97 L1150,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1193,100 L1203,100 L1203,110 L1193,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1198\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1125,25.0 L1225,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1225,25 L1253,25 L1272,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1278,44 L1297,25 L1325,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1225,75 L1253,75 L1272,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1278,56 L1297,75 L1325,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1250,43 L1300,43 L1300,57 L1250,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1275\" y=\"80\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1275\" y=\"26\" font-size=\"7\" text-anchor=\"middle\">Θ=px_dense_bs_up_0</text>\n",
       "<path d=\"M1250,43 L1300,43 L1300,47 L1250,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1293,50 L1303,50 L1303,60 L1293,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1298\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1225,125.0 L1325,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,175.0 L1325,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1025,225.0 L1325,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M925,275.0 L1325,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,325.0 L1325,325.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M725,375.0 L1325,375.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1325,25.0 L1340,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,75.0 L1340,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,125.0 L1340,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,175.0 L1340,175.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,225.0 L1340,225.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,275.0 L1340,275.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,325.0 L1340,325.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1325,375.0 L1340,375.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<text x=\"1350\" y=\"28.0\" font-size=\"9\" text-anchor=\"end\">0</text>\n",
       "<text x=\"1350\" y=\"78.0\" font-size=\"9\" text-anchor=\"end\">1</text>\n",
       "<text x=\"1350\" y=\"128.0\" font-size=\"9\" text-anchor=\"end\">2</text>\n",
       "<text x=\"1350\" y=\"178.0\" font-size=\"9\" text-anchor=\"end\">3</text>\n",
       "<text x=\"1350\" y=\"228.0\" font-size=\"9\" text-anchor=\"end\">4</text>\n",
       "<text x=\"1350\" y=\"278.0\" font-size=\"9\" text-anchor=\"end\">5</text>\n",
       "<text x=\"1350\" y=\"328.0\" font-size=\"9\" text-anchor=\"end\">6</text>\n",
       "<text x=\"1350\" y=\"378.0\" font-size=\"9\" text-anchor=\"end\">7</text>\n",
       "<text x=\"0\" y=\"28.0\" font-size=\"9\" text-anchor=\"start\">0</text>\n",
       "<text x=\"0\" y=\"78.0\" font-size=\"9\" text-anchor=\"start\">1</text>\n",
       "<text x=\"0\" y=\"128.0\" font-size=\"9\" text-anchor=\"start\">2</text>\n",
       "<text x=\"0\" y=\"178.0\" font-size=\"9\" text-anchor=\"start\">3</text>\n",
       "<text x=\"0\" y=\"228.0\" font-size=\"9\" text-anchor=\"start\">4</text>\n",
       "<text x=\"0\" y=\"278.0\" font-size=\"9\" text-anchor=\"start\">5</text>\n",
       "<text x=\"0\" y=\"328.0\" font-size=\"9\" text-anchor=\"start\">6</text>\n",
       "<text x=\"0\" y=\"378.0\" font-size=\"9\" text-anchor=\"start\">7</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<drawsvg.drawing.Drawing at 0x13ea2bce0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_circuit = dense_layer.circuit\n",
    "perceval.pdisplay(dense_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faafbb8",
   "metadata": {},
   "source": [
    "This circuit is expected because the architecture started off with 16 modes, but 8 were measured. Thus, 8 modes are left for the dense layer. Then, this triangular beam splitter mesh pattern is the one used for the `QDense` stage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c112b3a9",
   "metadata": {},
   "source": [
    "We can finally look at the Readout layer; the Linear layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbfdb398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output size from the QDense stage: 36\n",
      "Into the Linear layer with input size: 36\n",
      "Linear layer maps to output size: 2\n",
      "Which corresponds to the number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "in_linear_layer = linear_layer.in_features\n",
    "out_linear_layer = linear_layer.out_features\n",
    "\n",
    "out_dense = dense_layer.output_size\n",
    "\n",
    "print(f\"Output size from the QDense stage: {out_dense}\")\n",
    "print(f\"Into the Linear layer with input size: {in_linear_layer}\")\n",
    "print(f\"Linear layer maps to output size: {out_linear_layer}\")\n",
    "print(f\"Which corresponds to the number of classes: {qcnn.num_classes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f9e13d",
   "metadata": {},
   "source": [
    "The output size of 36 is expected from the `QDense` stage since there are 2 photons present in 8 modes which implies $\\binom{8+2-1}{2} = 36$ different combinations. Moreover, the linear layer maps from this dimension to the number of classes to obtain logits (raw score for each class)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20aecb14",
   "metadata": {},
   "source": [
    "Lastly, let's look at the distribution of trainable parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "300e5af6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QCNNClassifier has 104 trainable parameters\n",
      "Layer 0: QConv_1 has 2 trainable parameters\n",
      "Layer 1: QPool_1 has 0 trainable parameters\n",
      "Layer 2: QDense has 28 trainable parameters\n",
      "Layer 3: Readout has 74 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "num_params = sum(p.numel() for p in qcnn.parameters() if p.requires_grad)\n",
    "print(f\"QCNNClassifier has {num_params} trainable parameters\")\n",
    "\n",
    "for index, (name, layer) in enumerate(layers.named_children()):\n",
    "    num_layer_params = sum(p.numel() for p in layer.parameters() if p.requires_grad)\n",
    "    print(f\"Layer {index}: {name} has {num_layer_params} trainable parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3781d3",
   "metadata": {},
   "source": [
    "# 3. Training of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73ab1756",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, train_loader, x_train, x_test, y_train, y_test):\n",
    "    \"\"\"Train a single model and return training history\"\"\"\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.1, weight_decay=0.001)\n",
    "    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n",
    "    loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "    loss_history = []\n",
    "    train_acc_history = []\n",
    "    test_acc_history = []\n",
    "\n",
    "    # Initial accuracy\n",
    "    with torch.no_grad():\n",
    "        logits_train = model(x_train)\n",
    "        pred_train = torch.argmax(logits_train, dim=1)\n",
    "        train_acc = (pred_train == y_train).float().mean().item()\n",
    "\n",
    "        logits_test = model(x_test)\n",
    "        pred_test = torch.argmax(logits_test, dim=1)\n",
    "        test_acc = (pred_test == y_test).float().mean().item()\n",
    "\n",
    "        train_acc_history.append(train_acc)\n",
    "        test_acc_history.append(test_acc)\n",
    "\n",
    "    # Training loop\n",
    "    for _ in tqdm(range(20), desc=\"Training epochs\"):\n",
    "        for images, labels in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            output = model(images)\n",
    "            loss = loss_fn(output, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_history.append(loss.item())\n",
    "\n",
    "        # Evaluate accuracy\n",
    "        with torch.no_grad():\n",
    "            logits_train = model(x_train)\n",
    "            pred_train = torch.argmax(logits_train, dim=1)\n",
    "            train_acc = (pred_train == y_train).float().mean().item()\n",
    "\n",
    "            logits_test = model(x_test)\n",
    "            pred_test = torch.argmax(logits_test, dim=1)\n",
    "            test_acc = (pred_test == y_test).float().mean().item()\n",
    "\n",
    "            train_acc_history.append(train_acc)\n",
    "            test_acc_history.append(test_acc)\n",
    "        scheduler.step()\n",
    "        \n",
    "    return {\n",
    "        \"loss_history\": loss_history,\n",
    "        \"train_acc_history\": train_acc_history,\n",
    "        \"test_acc_history\": test_acc_history,\n",
    "        \"final_train_acc\": train_acc,\n",
    "        \"final_test_acc\": test_acc,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11fb30d",
   "metadata": {},
   "source": [
    "Conduct training of the `QCNNClassifier` across multiple random seeds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b5ced3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experiment 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training epochs: 100%|██████████| 20/20 [00:31<00:00,  1.59s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST - Final train: 1.0000, test: 0.9950\n",
      "Experiment 1/5 completed\n",
      "Starting experiment 2/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training epochs: 100%|██████████| 20/20 [00:33<00:00,  1.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST - Final train: 1.0000, test: 0.9900\n",
      "Experiment 2/5 completed\n",
      "Starting experiment 3/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training epochs: 100%|██████████| 20/20 [00:32<00:00,  1.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST - Final train: 1.0000, test: 0.9850\n",
      "Experiment 3/5 completed\n",
      "Starting experiment 4/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training epochs: 100%|██████████| 20/20 [00:32<00:00,  1.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST - Final train: 0.9875, test: 1.0000\n",
      "Experiment 4/5 completed\n",
      "Starting experiment 5/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training epochs: 100%|██████████| 20/20 [00:33<00:00,  1.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST - Final train: 1.0000, test: 1.0000\n",
      "Experiment 5/5 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "random_states = [42, 123, 456, 789, 999]\n",
    "all_results = {}\n",
    "\n",
    "for i, random_state in enumerate(random_states):\n",
    "    print(f\"Starting experiment {i + 1}/5\")\n",
    "    x_train, x_test, y_train, y_test = get_mnist(random_state=random_state)\n",
    "    x_train, x_test, y_train, y_test = convert_dataset_to_tensor(\n",
    "        x_train, x_test, y_train, y_test\n",
    "    )\n",
    "    # Ensure good shape of data\n",
    "    assert x_train.shape == (x_train.shape[0], 1, 8, 8), print(f\"{x_train.shape}\")\n",
    "    assert x_test.shape == (x_test.shape[0], 1, 8, 8), print(f\"{x_test.shape}\")\n",
    "    \n",
    "    train_loader = convert_tensor_to_loader(x_train, y_train)\n",
    "    dims = (8, 8)\n",
    "\n",
    "    qcnn = QCNNClassifier((8, 8), 2)\n",
    "\n",
    "    results = train_model(qcnn, train_loader, x_train, x_test, y_train, y_test)\n",
    "    print(\n",
    "        f\"MNIST - Final train: {results['final_train_acc']:.4f}, test: {results['final_test_acc']:.4f}\"\n",
    "    )\n",
    "    print(f\"Experiment {i + 1}/5 completed\")\n",
    "    all_results[f\"run_{i}\"] = results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a4de13",
   "metadata": {},
   "source": [
    "## 4. Results\n",
    "\n",
    "Visualize training metrics and recap final results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b8a5c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Results:\n",
      "==================================================\n",
      "Binary MNIST 0 vs 1:\n",
      "  Train Accuracy: 0.998 ± 0.005\n",
      "  Test Accuracy:  0.994 ± 0.006\n"
     ]
    }
   ],
   "source": [
    "# Save summary statistics\n",
    "summary = {}\n",
    "num_runs = len(all_results)\n",
    "train_accs = [all_results[f\"run_{i}\"][\"final_train_acc\"] for i in range(num_runs)]\n",
    "test_accs = [all_results[f\"run_{i}\"][\"final_test_acc\"] for i in range(num_runs)]\n",
    "\n",
    "summary = {\n",
    "    \"train_acc_mean\": np.mean(train_accs),\n",
    "    \"train_acc_std\": np.std(train_accs),\n",
    "    \"test_acc_mean\": np.mean(test_accs),\n",
    "    \"test_acc_std\": np.std(test_accs),\n",
    "    \"train_accs\": train_accs,\n",
    "    \"test_accs\": test_accs,\n",
    "}\n",
    "\n",
    "# Create training plots for each dataset\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "colors = [\"blue\", \"red\", \"green\", \"orange\", \"purple\"]\n",
    "\n",
    "# Plot loss history for this dataset\n",
    "ax_loss = axes[0]\n",
    "for run_idx in range(num_runs):\n",
    "    loss_history = all_results[f\"run_{run_idx}\"][\"loss_history\"]\n",
    "    ax_loss.plot(\n",
    "        loss_history,\n",
    "        color=colors[run_idx],\n",
    "        alpha=1,\n",
    "        linewidth=2,\n",
    "        label=f\"Run {run_idx + 1}\",\n",
    "    )\n",
    "ax_loss.set_title(\"MNIST - Training Loss\")\n",
    "ax_loss.set_xlabel(\"Training Steps\")\n",
    "ax_loss.set_ylabel(\"Loss\")\n",
    "ax_loss.legend()\n",
    "ax_loss.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot train accuracy for this dataset\n",
    "ax_train = axes[1]\n",
    "for run_idx in range(num_runs):\n",
    "    train_acc_history = all_results[f\"run_{run_idx}\"][\"train_acc_history\"]\n",
    "    epochs = range(len(train_acc_history))\n",
    "    ax_train.plot(\n",
    "        epochs,\n",
    "        train_acc_history,\n",
    "        color=colors[run_idx],\n",
    "        alpha=1,\n",
    "        linewidth=2,\n",
    "        label=f\"Run {run_idx + 1}\",\n",
    "    )\n",
    "ax_train.set_title(\"MNIST - Training Accuracy\")\n",
    "ax_train.set_xlabel(\"Epochs\")\n",
    "ax_train.set_ylabel(\"Accuracy\")\n",
    "ax_train.legend()\n",
    "ax_train.grid(True, alpha=0.3)\n",
    "ax_train.set_ylim(0, 1)\n",
    "\n",
    "# Plot test accuracy for this dataset\n",
    "ax_test = axes[2]\n",
    "for run_idx in range(num_runs):\n",
    "    test_acc_history = all_results[f\"run_{run_idx}\"][\"test_acc_history\"]\n",
    "    epochs = range(len(test_acc_history))\n",
    "    ax_test.plot(\n",
    "        epochs,\n",
    "        test_acc_history,\n",
    "        color=colors[run_idx],\n",
    "        alpha=1,\n",
    "        linewidth=2,\n",
    "        label=f\"Run {run_idx + 1}\",\n",
    "    )\n",
    "ax_test.set_title(\"MNIST - Test Accuracy\")\n",
    "ax_test.set_xlabel(\"Epochs\")\n",
    "ax_test.set_ylabel(\"Accuracy\")\n",
    "ax_test.legend()\n",
    "ax_test.grid(True, alpha=0.3)\n",
    "ax_test.set_ylim(0, 1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print summary\n",
    "print(\"\\nSummary Results:\")\n",
    "print(\"=\" * 50)\n",
    "print(\"Binary MNIST 0 vs 1:\")\n",
    "print(\n",
    "    f\"  Train Accuracy: {summary['train_acc_mean']:.3f} ± {summary['train_acc_std']:.3f}\"\n",
    ")\n",
    "print(\n",
    "    f\"  Test Accuracy:  {summary['test_acc_mean']:.3f} ± {summary['test_acc_std']:.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e298aa",
   "metadata": {},
   "source": [
    "These results are what we expect from this architecture on this specific dataset, as they are what we have previously reached using a density matrix approach for computation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}