{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "938114ab",
   "metadata": {},
   "source": [
    "Image classification with MerLin\n",
    "==============================\n",
    "\n",
    "This notebook presents three frameworks for image classification using MerLin and Perceval.\n",
    "\n",
    "All of these frameworks are tested on the MNIST dataset to evaluate their effectiveness.\n",
    "\n",
    "The three methods are:\n",
    "\n",
    "Photonic QNN\n",
    "------------\n",
    "A framework where a scale layer encodes the data into two trainable generic interferometers, complemented by a classifcation algorithm.\n",
    "\n",
    "GLASE\n",
    "-----\n",
    "A framework where a surrogate model simulates the gradients of a quantum layer, training a classical model to make predictions from this quantum layer.\n",
    "\n",
    "Lancelot\n",
    "--------\n",
    "A framework utilizing a custom gate to perform unitary dilation on the data, generating new features for prediction with a linear classifier.\n",
    "\n",
    "All of these models originate from the `GLASE repository on GitHub <https://github.com/Quandela/HybridAIQuantum-Challenge/blob/main/src/GLASE/model.py>`_,\n",
    "created for the Perceval Quest challenge organized by Quandela."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407a3d4e",
   "metadata": {},
   "source": [
    "# First method, photonic qnn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ac725d",
   "metadata": {},
   "source": [
    "## I° Photonic QNN overview\n",
    "\n",
    "![Photonic qnn](../_static/img/photonic_qNN(1).png)\n",
    "made by Vasileios Apostolou\n",
    "In this model, we initialise a first generic interferometer, fully trainable, then an encoding layer not trainable, and lastly an interferometer which only phase shifters are trainable.\n",
    "\n",
    "the trainable interferometer may need to rescale the input. that's why we have to construct the `ScaleLayer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a876e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/LeïthKARRAÏ/PML-328/venvPML-250/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import perceval as pcvl\n",
    "from merlin.measurement.strategies import MeasurementStrategy\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cma\n",
    "from scipy import linalg\n",
    "from tqdm.auto import tqdm\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import torch.optim as optim\n",
    "from merlin import ComputationSpace, QuantumLayer, MeasurementStrategy, CircuitBuilder\n",
    "import perceval.components as comp\n",
    "import perceval.algorithm as algo\n",
    "from perceval import catalog\n",
    "\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.manifold import TSNE\n",
    "import json\n",
    "import re\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from merlin.datasets import mnist_digits\n",
    "from sklearn import svm\n",
    "from sklearn.decomposition import PCA\n",
    "from merlin.measurement import MeasurementStrategy\n",
    "from collections.abc import Iterable\n",
    "import pickle\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "###############################\n",
    "## Build the quantum circuit ##\n",
    "###############################\n",
    "\n",
    "INPUT_SIZE = 32\n",
    "\n",
    "def create_quantum_circuit(m, size=40, frequency=1):\n",
    "    \"\"\"Create quantum circuit with specified number of modes\"\"\"\n",
    "    # first trainable generic interferometer\n",
    "    wl = pcvl.GenericInterferometer(m,\n",
    "                                    lambda i: pcvl.BS(theta=pcvl.P(f\"bs_1_{i}\")) // pcvl.PS(pcvl.P(f\"phase_1_{i}\")) // \\\n",
    "                                              pcvl.BS(theta=pcvl.P(f\"bs_2_{i}\")) // pcvl.PS(pcvl.P(f\"phase_2_{i}\")),\n",
    "                                    shape=pcvl.InterferometerShape.RECTANGLE)\n",
    "    \n",
    "\n",
    "    c = pcvl.Circuit(m)\n",
    "    c.add(0, wl, merge=True)\n",
    "\n",
    "    # f repetition of {encoding layers with input data in phase shifters; trainable generic interferometer}\n",
    "    for f in range(frequency):\n",
    "        c_var = pcvl.Circuit(m)\n",
    "        for i in range(size):\n",
    "            px = pcvl.P(f\"px-{f}-{i + 1}\")\n",
    "            c_var.add(i % m, pcvl.PS(px))\n",
    "            \n",
    "        c.add(0, c_var, merge=True)\n",
    "        \n",
    "        wr = pcvl.GenericInterferometer(m,\n",
    "                                        lambda i: pcvl.BS() // pcvl.PS(pcvl.P(f\"phase_3_{i}\")) // \\\n",
    "                                                  pcvl.BS() // pcvl.PS(pcvl.P(f\"phase_4_{i}\")),\n",
    "                                        shape=pcvl.InterferometerShape.RECTANGLE)\n",
    "        c.add(0, wr, merge=True)\n",
    "\n",
    "    return c\n",
    "\n",
    "\n",
    "class ScaleLayer(nn.Module):\n",
    "    def __init__(self, dim, scale_type = \"learned\"):\n",
    "        super(ScaleLayer, self).__init__()\n",
    "        # Create a single learnable parameter (initialized to 1.0 by default)\n",
    "        # Caution: MerLin already mutltiplies by pi\n",
    "        if scale_type == \"learned\":\n",
    "            self.scale = nn.Parameter(torch.rand(dim))\n",
    "        elif scale_type == \"2pi\":\n",
    "            self.scale = torch.full((dim,), 2)\n",
    "        elif scale_type == \"pi\":\n",
    "            self.scale = torch.full((dim,), 1)\n",
    "        elif scale_type == \"1\":\n",
    "            self.scale = torch.full((dim,), 1)\n",
    "        #print(f\"SELF.SCALE: {self.scale.shape}\")\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Element-wise multiplication of each input element by the learned scale\n",
    "        return x * self.scale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d49258",
   "metadata": {},
   "source": [
    "After the scale layer class construction, we can construct the core model, that we will use to make our classification.\n",
    "we will use perceval to make the circuit, and package it into merlin's `QuantumLayer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6df5f463",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLayer(nn.Module):\n",
    "    def __init__(self, nb_photons, nb_modes, dim, scale_type=\"learned\"):\n",
    "        super().__init__() \n",
    "        self.nb_modes = nb_modes\n",
    "        self.nb_photons = nb_photons     \n",
    "        self.scaler = ScaleLayer(dim, scale_type)\n",
    "\n",
    "        circuit = create_quantum_circuit(m=nb_modes, size=dim, frequency=1)\n",
    "\n",
    "        input_state = [(i + 1) % 2 for i in range(nb_modes)]\n",
    "\n",
    "        self.layer = QuantumLayer(\n",
    "        input_size=dim,\n",
    "        circuit=circuit,\n",
    "        input_state=input_state,\n",
    "        measurement_strategy=MeasurementStrategy.probs(),\n",
    "        input_parameters=[\"px\"],\n",
    "        trainable_parameters=[\"bs\", \"phase\"], \n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.scaler(x)\n",
    "        x = self.layer(x) \n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71cc571",
   "metadata": {},
   "source": [
    "We can set the seed to reproduce the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be2e721f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed=42):\n",
    "    \"\"\"\n",
    "    Set the random seed for reproducibility across different libraries.\n",
    "\n",
    "    Args:\n",
    "        seed (int): Seed value to use. Default is 42.\n",
    "    \"\"\"\n",
    "    # Set Python's random seed\n",
    "    random.seed(seed)\n",
    "\n",
    "    # Set NumPy's random seed\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    # Set PyTorch's random seeds for both CPU and CUDA\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed)\n",
    "        torch.cuda.manual_seed_all(seed)  # For multi-GPU setups\n",
    "\n",
    "    # Additional settings for complete reproducibility\n",
    "    # Note: This can affect performance\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "\n",
    "    # For some PyTorch operations using Intel MKL\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "\n",
    "    print(f\"Random seed set to {seed}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7dd548f",
   "metadata": {},
   "source": [
    "## II° and testing methods\n",
    "\n",
    "\n",
    "first, we have to load the dataset and crop it to fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62b29670",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomToTensor:\n",
    "    \"\"\"\n",
    "    Torch Tensor is not available with merlin 0.4, so we make a quick class to reliably replace toTensor method.\n",
    "    \n",
    "    \"\"\"\n",
    "    def __call__(self, pic):\n",
    "        # Sécurité : forcer le type en numpy.ndarray si ce n'est pas déjà le cas\n",
    "        if not isinstance(pic, np.ndarray):\n",
    "            pic = np.array(pic)\n",
    "            \n",
    "        # 1. Réorganisation des dimensions si l'image a 3 dimensions (H, W, Canaux)\n",
    "        if pic.ndim == 3:\n",
    "            pic = pic.transpose((2, 0, 1))\n",
    "            \n",
    "        # 2. Conversion en tenseur PyTorch de type float\n",
    "        img_tensor = torch.from_numpy(pic).float()\n",
    "        \n",
    "        # 3. Normalisation (seulement si l'image source était sur 8 bits)\n",
    "        # Note : MNIST est souvent en uint8 [0, 255]\n",
    "        if pic.dtype == np.uint8:\n",
    "            img_tensor = img_tensor / 255.0\n",
    "            \n",
    "        return img_tensor\n",
    "\n",
    "\n",
    "##########################\n",
    "### load MNIST dataset ###\n",
    "##########################\n",
    "\n",
    "def crop_middle(image, size = 20):\n",
    "    \"\"\"\n",
    "    Crop the middle from a numpy array based on the specified position.\n",
    "\n",
    "    Args:\n",
    "        image: Numpy array of shape (H, W, C) or (H, W)\n",
    "        size: total size of the cropped image\n",
    "\n",
    "    Returns:\n",
    "        Cropped square numpy array\n",
    "    \"\"\"\n",
    "    if len(image.shape) == 3:\n",
    "        _, height, width = image.shape\n",
    "    else:\n",
    "        height, width = image.shape\n",
    "\n",
    "    # Determine size of the square (the smaller dimension)\n",
    "    mid_x = int(width * 0.5)\n",
    "    mid_y = int(height * 0.5)\n",
    "    half_size = int(size / 2)\n",
    "\n",
    "    return image[mid_x-half_size:mid_x+half_size, mid_y-half_size:mid_y+half_size].copy()\n",
    "\n",
    "class TransformCenter:\n",
    "    def __init__(self, size = 20):\n",
    "        self.transform = lambda x: crop_middle(x, size = size)\n",
    "        self.tensor_transform = CustomToTensor()\n",
    "        self.size = size\n",
    "\n",
    "    def __call__(self, x):\n",
    "        y1 = self.tensor_transform(self.transform(x)).view(self.size * self.size)\n",
    "        return y1\n",
    "\n",
    "\n",
    "# load the correct train, val dataset for the challenge, from the csv files\n",
    "class MNIST_partial(Dataset):\n",
    "    def __init__(self, data='./data', transform=None, split='train'):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            data: path to dataset folder which contains train.csv and val.csv\n",
    "            transform (callable, optional): Optional transform to be applied\n",
    "                on a sample (e.g., data augmentation or normalization)\n",
    "            split: 'train' or 'val' to determine which set to download\n",
    "        \"\"\"\n",
    "        self.data_dir = data\n",
    "        self.transform = transform\n",
    "        self.data = []\n",
    "\n",
    "        if split == 'train':\n",
    "            filename = os.path.join(self.data_dir, 'train.csv')\n",
    "        elif split == 'val':\n",
    "            filename = os.path.join(self.data_dir, 'val.csv')\n",
    "        else:\n",
    "            raise AttributeError(\"split!='train' and split!='val': split must be train or val\")\n",
    "\n",
    "        self.df = pd.read_csv(filename)\n",
    "\n",
    "    def __len__(self):\n",
    "        l = len(self.df['image'])\n",
    "        return l\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img = self.df['image'].iloc[idx]\n",
    "        label = self.df['label'].iloc[idx]\n",
    "        # string to list\n",
    "        img_list = re.split(r',', img)\n",
    "        # remove '[' and ']'\n",
    "        img_list[0] = img_list[0][1:]\n",
    "        img_list[-1] = img_list[-1][:-1]\n",
    "        # convert to float\n",
    "        img_float = [float(el) for el in img_list]\n",
    "        # convert to image\n",
    "        img_square = torch.unflatten(torch.tensor(img_float), 0, (1, 28, 28)).numpy()\n",
    "        #img_flat = img_square.flatten()\n",
    "        if self.transform is not None:\n",
    "            img_square = self.transform(img_square)\n",
    "        return img_square, label\n",
    "\n",
    "\n",
    "def load_dataset(size, bs):\n",
    "    SIZE = size\n",
    "    batch_size = bs\n",
    "\n",
    "    # Load the Perceval Quest splits from the Merlin dataset helper\n",
    "    X_train_raw, y_train_raw, _ = mnist_digits.get_data_train_percevalquest()\n",
    "    X_val_raw, y_val_raw, _ = mnist_digits.get_data_test_percevalquest()\n",
    "\n",
    "    # Crop a square at the centre of each image (SIZE x SIZE) and flatten it\n",
    "    transform = TransformCenter(size=SIZE)\n",
    "    X_train_flat = np.stack([transform(img).numpy() for img in X_train_raw]).astype(np.float32)\n",
    "    X_val_flat = np.stack([transform(img).numpy() for img in X_val_raw]).astype(np.float32)\n",
    "\n",
    "    y_train = np.asarray(y_train_raw, dtype=np.int64)\n",
    "    y_val = np.asarray(y_val_raw, dtype=np.int64)\n",
    "\n",
    "    # Feature-wise scaling (keeps behaviour identical to previous implementation)\n",
    "    scaler = MinMaxScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train_flat)\n",
    "    X_val_scaled = scaler.transform(X_val_flat)\n",
    "\n",
    "    train_tensor = torch.from_numpy(X_train_scaled).float()\n",
    "    val_tensor = torch.from_numpy(X_val_scaled).float()\n",
    "    train_dataset = TensorDataset(train_tensor, torch.from_numpy(y_train))\n",
    "    val_dataset = TensorDataset(val_tensor, torch.from_numpy(y_val))\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
    "\n",
    "    INPUT_SIZE = SIZE * SIZE\n",
    "    OUTPUT_FEATURES = 10\n",
    "\n",
    "    return X_train_scaled, X_val_scaled, y_train, y_val, train_loader, val_loader, INPUT_SIZE, OUTPUT_FEATURES\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e12b0e",
   "metadata": {},
   "source": [
    "## III° training loop\n",
    "\n",
    "we make the training loop for the photonic QNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27ad8b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _classification_step(model, batch_X, batch_y, criterion, frequency):\n",
    "    \"\"\"Forward pass + loss for a plain classifier (photonic QNN or classical MLP).\n",
    "\n",
    "    Returns the loss and a dict of predictions (single \"measured\" head).\n",
    "    \"\"\"\n",
    "    batch_X = batch_X.repeat(1, 1, frequency)\n",
    "    outputs = model(batch_X.squeeze(0).float())\n",
    "    loss = criterion(outputs, batch_y)\n",
    "    return loss, {\"measured\": outputs.argmax(dim=-1)}\n",
    "\n",
    "\n",
    "def _hybrid_qnn_step(model, batch_X, batch_y, criterion, surrogate_rate):\n",
    "    \"\"\"Forward pass + loss for the CNN+QNN hybrid surrogate model.\n",
    "\n",
    "    The model returns (out_approx, surrogate_loss, out_measured); the total loss\n",
    "    blends the classical surrogate guidance, the surrogate-fidelity term, and the\n",
    "    real quantum measurement. Returns the loss and a dict with both prediction heads.\n",
    "    \"\"\"\n",
    "    out_approx, surrogate_loss, out_measured = model(batch_X)\n",
    "    loss = (\n",
    "        criterion(out_approx, batch_y) * 0.5\n",
    "        + surrogate_loss * surrogate_rate\n",
    "        + criterion(out_measured, batch_y) * 0.25\n",
    "    )\n",
    "    return loss, {\"measured\": out_measured.argmax(dim=-1), \"approx\": out_approx.argmax(dim=-1)}\n",
    "\n",
    "\n",
    "def train_model(\n",
    "    model,\n",
    "    train_loader,\n",
    "    val_loader,\n",
    "    model_type=\"classification\",\n",
    "    num_epochs=25,\n",
    "    lr=0.01,\n",
    "    weight_decay=0.0,\n",
    "    label_smoothing=0.0,\n",
    "    frequency=1,\n",
    "    device=\"cpu\",\n",
    "    save_path=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Unified training loop shared by the plain classifiers (photonic QNN / classical MLP)\n",
    "    and the CNN+QNN hybrid surrogate model.\n",
    "\n",
    "    Args:\n",
    "        model: PyTorch model to train. For model_type=\"hybrid_qnn\" it must return\n",
    "            (out_approx, surrogate_loss, out_measured); otherwise it must return logits.\n",
    "        train_loader: DataLoader for the training set.\n",
    "        val_loader: DataLoader for the validation set.\n",
    "        model_type (str): \"classification\" or \"hybrid_qnn\". Selects the forward/loss\n",
    "            logic and the optimizer/scheduler configuration.\n",
    "        num_epochs (int): number of training epochs.\n",
    "        lr (float): learning rate.\n",
    "        weight_decay (float): optimizer weight decay (only used for \"hybrid_qnn\").\n",
    "        label_smoothing (float): label smoothing for CrossEntropyLoss.\n",
    "        frequency (int): number of times the input is repeated for the encoding\n",
    "            layer (only used for model_type=\"classification\").\n",
    "        device (str): device to train on.\n",
    "        save_path (str | None): if provided, the model weights and history are\n",
    "            pickled to this path (only meaningful for model_type=\"hybrid_qnn\").\n",
    "\n",
    "    Returns:\n",
    "        history (dict): per-epoch train/val loss and accuracy (and surrogate\n",
    "            accuracy, when model_type=\"hybrid_qnn\").\n",
    "        best_val_acc (float): best validation accuracy (%) reached, on the\n",
    "            \"measured\" head.\n",
    "    \"\"\"\n",
    "    model = model.to(device)\n",
    "    criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)\n",
    "    step_fn = _hybrid_qnn_step if model_type == \"hybrid_qnn\" else _classification_step\n",
    "\n",
    "    if model_type == \"hybrid_qnn\":\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=2e-6)\n",
    "    else:\n",
    "        # Betas from the ablation study\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.8, 0.999))\n",
    "        scheduler = None\n",
    "\n",
    "    surrogate_rate = 5.0\n",
    "    history = {\n",
    "        \"train_loss\": [], \"val_loss\": [],\n",
    "        \"train_acc\": [], \"val_acc\": [],\n",
    "        \"train_acc_approx\": [], \"val_acc_approx\": [],\n",
    "    }\n",
    "    best_val_acc = 0\n",
    "\n",
    "    def run_epoch(loader, training, desc):\n",
    "        model.train(training) if training else model.eval()\n",
    "        total_loss, correct, correct_approx, total = 0.0, 0, 0, 0\n",
    "        with torch.set_grad_enabled(training):\n",
    "            for batch_X, batch_y in tqdm(loader, desc=desc, leave=False):\n",
    "                batch_X, batch_y = batch_X.to(device), batch_y.to(device)\n",
    "                if training:\n",
    "                    optimizer.zero_grad()\n",
    "\n",
    "                if model_type == \"hybrid_qnn\":\n",
    "                    loss, preds = step_fn(model, batch_X, batch_y, criterion, surrogate_rate)\n",
    "                else:\n",
    "                    loss, preds = step_fn(model, batch_X, batch_y, criterion, frequency)\n",
    "\n",
    "                if training:\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                total_loss += loss.item()\n",
    "                total += batch_y.size(0)\n",
    "                correct += (preds[\"measured\"] == batch_y).sum().item()\n",
    "                if \"approx\" in preds:\n",
    "                    correct_approx += (preds[\"approx\"] == batch_y).sum().item()\n",
    "\n",
    "        avg_loss = total_loss / len(loader)\n",
    "        acc = 100 * correct / total\n",
    "        acc_approx = 100 * correct_approx / total if model_type == \"hybrid_qnn\" else acc\n",
    "        return avg_loss, acc, acc_approx\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        train_loss, train_acc, train_acc_approx = run_epoch(\n",
    "            train_loader, training=True, desc=f\"Epoch {epoch + 1}/{num_epochs} [train]\"\n",
    "        )\n",
    "        val_loss, val_acc, val_acc_approx = run_epoch(\n",
    "            val_loader, training=False, desc=f\"Epoch {epoch + 1}/{num_epochs} [val]\"\n",
    "        )\n",
    "\n",
    "        best_val_acc = max(best_val_acc, val_acc)\n",
    "\n",
    "        history[\"train_loss\"].append(train_loss)\n",
    "        history[\"val_loss\"].append(val_loss)\n",
    "        history[\"train_acc\"].append(train_acc)\n",
    "        history[\"val_acc\"].append(val_acc)\n",
    "        history[\"train_acc_approx\"].append(train_acc_approx)\n",
    "        history[\"val_acc_approx\"].append(val_acc_approx)\n",
    "\n",
    "        print(\n",
    "            f\"Epoch [{epoch + 1}/{num_epochs}], Train loss: {train_loss:.4f}, Val loss: {val_loss:.4f}, \"\n",
    "            f\"Train acc: {train_acc:.4f}, Val acc: {val_acc:.4f}, Best val acc: {best_val_acc:.4f}\"\n",
    "        )\n",
    "\n",
    "        if model_type == \"hybrid_qnn\":\n",
    "            surrogate_rate *= 0.95\n",
    "            scheduler.step()\n",
    "\n",
    "    if save_path is not None:\n",
    "        save_training_artifacts(model, history, model_type, save_path)\n",
    "\n",
    "    return history, best_val_acc\n",
    "\n",
    "\n",
    "# count paramaeters in a model (nn.Module)\n",
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506acb3d",
   "metadata": {},
   "source": [
    "## IV°/ Tests and results\n",
    "\n",
    "Once the training loop is done, we will be using confusion matrices to test the different models.\n",
    "tSNE will be used to show the results of the model, by extracting features via `extract_features` function, and ploting it with`display_tsne`. tsne is an algorithm of non-supervised learning which perform classification, if the model perform well, tsne will be really efficient.\n",
    "we will also visualize the scale parameters of the encoding function and then save the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce4414b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#############################\n",
    "### results visualisation ###\n",
    "#############################\n",
    "## here, we want to visualize the scale parameters of the encoding function ##\n",
    "def visualize_scale_parameters(scale_layer):\n",
    "    \"\"\"\n",
    "        Display scale parameters of the encoding layers.\n",
    "\n",
    "        Args:\n",
    "            scale_layer (nn.Module): encoding layer\n",
    "        \"\"\"\n",
    "    # Get the scale parameter data as a numpy array\n",
    "    scale_data = scale_layer.scale.data.cpu().numpy()\n",
    "\n",
    "    # For a single scale parameter\n",
    "    if scale_data.size == 1:\n",
    "        print(f\"Learned scale parameter: {scale_data.item():.4f}\")\n",
    "\n",
    "    # For a 1D array of parameters (e.g., per feature)\n",
    "    elif len(scale_data.shape) == 1 or (\n",
    "            len(scale_data.shape) > 1 and np.prod(scale_data.shape) == max(scale_data.shape)):\n",
    "        # Reshape to 1D if necessary\n",
    "        scale_data = scale_data.flatten()\n",
    "\n",
    "        plt.figure(figsize=(10, 6))\n",
    "\n",
    "        # Option 1: Bar plot\n",
    "        plt.subplot(2, 1, 1)\n",
    "        plt.bar(range(len(scale_data)), scale_data)\n",
    "        plt.title('Learned Scale Parameters')\n",
    "        plt.xlabel('Parameter Index')\n",
    "        plt.ylabel('Value')\n",
    "\n",
    "        # Option 2: Heatmap (1D version)\n",
    "        plt.subplot(2, 1, 2)\n",
    "        sns.heatmap(scale_data.reshape(1, -1), cmap='viridis', annot=True if len(scale_data) < 20 else False)\n",
    "        plt.title('Scale Parameters Heatmap')\n",
    "        plt.xlabel('Parameter Index')\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.savefig('scale_parameters.png')\n",
    "        plt.show()\n",
    "\n",
    "## display the confusion matrices for the 2 models ##\n",
    "def display_confusion_matrices(model1, model2, val_loader, class_names=None, device='cuda'):\n",
    "    \"\"\"\n",
    "    Display confusion matrices for two trained models using a validation data loader.\n",
    "\n",
    "    Args:\n",
    "        3 models : 3 PyTorch model\n",
    "        val_loader: PyTorch DataLoader containing validation data\n",
    "        class_names: List of class names (optional)\n",
    "        device: Device to run inference on ('cuda' or 'cpu')\n",
    "    \"\"\"\n",
    "    # Set models to evaluation mode\n",
    "    model1.eval()\n",
    "    model2.eval()\n",
    "\n",
    "    # Move models to the appropriate device\n",
    "    model1 = model1.to(device)\n",
    "    model2 = model2.to(device)\n",
    "\n",
    "    # Initialize lists to store predictions and ground truth\n",
    "    all_preds1 = []\n",
    "    all_preds2 = []\n",
    "    all_targets = []\n",
    "\n",
    "    # Disable gradient computation for inference\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets in val_loader:\n",
    "\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # Get predictions from both models\n",
    "            # outputs1 = model1(inputs.squeeze(1).float())\n",
    "            # outputs2 = model2(inputs.squeeze(1).float())\n",
    "            outputs1 = model1((inputs.squeeze(1).float()))\n",
    "            outputs2 = model2((inputs.squeeze(1).float()))\n",
    "\n",
    "            # Convert outputs to class predictions\n",
    "            _, preds1 = torch.max(outputs1, 1)\n",
    "            _, preds2 = torch.max(outputs2, 1)\n",
    "\n",
    "            # Append batch predictions and targets to lists\n",
    "            all_preds1.extend(preds1.cpu().numpy())\n",
    "            all_preds2.extend(preds2.cpu().numpy())\n",
    "            all_targets.extend(targets.cpu().numpy())\n",
    "\n",
    "    # Convert lists to numpy arrays\n",
    "    all_preds1 = np.array(all_preds1)\n",
    "    all_preds2 = np.array(all_preds2)\n",
    "    all_targets = np.array(all_targets)\n",
    "\n",
    "    # Compute confusion matrices\n",
    "    cm1 = confusion_matrix(all_targets, all_preds1)\n",
    "    cm2 = confusion_matrix(all_targets, all_preds2)\n",
    "\n",
    "    # Create a figure with two subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6))\n",
    "\n",
    "    # Display confusion matrices\n",
    "    disp1 = ConfusionMatrixDisplay(confusion_matrix=cm1, display_labels=class_names)\n",
    "    disp2 = ConfusionMatrixDisplay(confusion_matrix=cm2, display_labels=class_names)\n",
    "\n",
    "    disp1.plot(ax=ax1, cmap='coolwarm', values_format='d')\n",
    "    disp2.plot(ax=ax2, cmap='coolwarm', values_format='d')\n",
    "\n",
    "    # Set titles\n",
    "    ax1.set_title('Confusion Matrix - quantum Trained model')\n",
    "    ax2.set_title('Confusion Matrix - classical Trained model')\n",
    "\n",
    "    # Add overall accuracy to the titles\n",
    "    acc1 = np.sum(np.diag(cm1)) / np.sum(cm1)\n",
    "    acc2 = np.sum(np.diag(cm2)) / np.sum(cm2)\n",
    "\n",
    "    ax1.set_xlabel(f'Predicted Label\\nAccuracy: {acc1:.4f}')\n",
    "    ax2.set_xlabel(f'Predicted Label\\nAccuracy: {acc2:.4f}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(f'./results/CM-h-{hidden_dim}-m-{modes}.png')\n",
    "    plt.show()\n",
    "\n",
    "    return cm1, cm2\n",
    "\n",
    "## extract features from a trained model for tSNE analysis ##\n",
    "def extract_features(model, dataloader, device='cuda'):\n",
    "    \"\"\"\n",
    "        Extract features using a trained model.\n",
    "\n",
    "        Args:\n",
    "            models : PyTorch model from which to extract features\n",
    "            dataloader: dataloader containing training/validation data\n",
    "            device: Device to run inference on ('cuda' or 'cpu')\n",
    "\n",
    "        Returns:\n",
    "            features: TorchTensor\n",
    "            labels: TorchTensor\n",
    "    \"\"\"\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    features = []\n",
    "    labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data, label in dataloader:\n",
    "            BS = data.shape[0]\n",
    "            data = data.reshape(BS,-1).to(device)\n",
    "            #print(f\"\\nData = {data}\")\n",
    "            output = model(data.float())\n",
    "            features.append(output.cpu())  # Move to CPU for compatibility\n",
    "            labels.extend(label.cpu().numpy())\n",
    "\n",
    "    features = torch.cat(features, dim=0).numpy()\n",
    "    labels = torch.tensor(labels).numpy()\n",
    "    return features, labels\n",
    "\n",
    "\n",
    "## display the tSNE plots for 2 models and dataloader ##\n",
    "def display_tsne(model1, model2, val_loader,modes, device='cpu'):\n",
    "    \"\"\"\n",
    "    Display the 3 tSNE for 3 trained models using a validation data loader.\n",
    "\n",
    "    Args:\n",
    "        the 2 models: 2 PyTorch models we want to compare\n",
    "        val_loader: PyTorch DataLoader containing validation data\n",
    "        modes: number used for the quantum model (for the Figure title)\n",
    "        device: Device to run inference on ('cuda' or 'cpu')\n",
    "    \"\"\"\n",
    "    # Set models to evaluation mode\n",
    "    model1.eval()\n",
    "    model2.eval()\n",
    "\n",
    "    # Move models to the appropriate device\n",
    "    model1 = model1.to(device)\n",
    "    model2 = model2.to(device)\n",
    "\n",
    "    # get the features and compute tSNE for each model\n",
    "    features_1, labels_1 = extract_features(model1, val_loader, device=device)\n",
    "    tsne = TSNE(n_components=2, random_state=42)\n",
    "    features_2d_1 = tsne.fit_transform(features_1)\n",
    "\n",
    "    features_2, labels_2 = extract_features(model2, val_loader, device=device)\n",
    "    tsne = TSNE(n_components=2, random_state=42)\n",
    "    features_2d_2 = tsne.fit_transform(features_2)\n",
    "\n",
    "\n",
    "    # Display the tSNE plots\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 5))\n",
    "    num_classes = 10\n",
    "    for class_idx in range(num_classes):\n",
    "        ax1.scatter(features_2d_1[labels_1 == class_idx, 0], features_2d_1[labels_1 == class_idx, 1],\n",
    "                    label=f'Digit {class_idx}', alpha=0.6)\n",
    "        ax1.set_xlabel('t-SNE Dim 1')\n",
    "        ax1.set_ylabel('t-SNE Dim 2')\n",
    "        ax1.legend()\n",
    "        ax2.scatter(features_2d_2[labels_2 == class_idx, 0], features_2d_2[labels_2 == class_idx, 1],\n",
    "                    label=f'Digit {class_idx}', alpha=0.6)\n",
    "        ax2.set_xlabel('t-SNE Dim 1')\n",
    "        ax2.set_ylabel('t-SNE Dim 2')\n",
    "        ax2.legend()\n",
    "\n",
    "    # Set titles\n",
    "    ax1.set_title('tSNE - quantum kernel')\n",
    "    ax2.set_title('tSNE - classical kernel model')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'tSNE-m-{modes}.png')\n",
    "    #plt.show()\n",
    "\n",
    "    return \"done\"\n",
    "\n",
    "def save_experiment_results(results, filename='photonic_qNN_results.json'):\n",
    "    \"\"\"\n",
    "    Append experiment results to a JSON file.\n",
    "\n",
    "    Args:\n",
    "        results (dict): Dictionary containing experiment results (with float values)\n",
    "        filename (str): Path to the JSON file to store results\n",
    "    \"\"\"\n",
    "    # Check if file exists and load existing data\n",
    "    if os.path.exists(filename):\n",
    "        try:\n",
    "            with open(filename, 'r') as file:\n",
    "                all_results = json.load(file)\n",
    "        except json.JSONDecodeError:\n",
    "            # Handle case where file exists but is empty or corrupted\n",
    "            all_results = []\n",
    "    else:\n",
    "        all_results = []\n",
    "\n",
    "    # Append new results\n",
    "    all_results.append(results)\n",
    "\n",
    "    # Write updated data back to file\n",
    "    with open(filename, 'w') as file:\n",
    "        json.dump(all_results, file, indent=4)\n",
    "\n",
    "    return len(all_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0933d512",
   "metadata": {},
   "source": [
    "## V°/ Main loop\n",
    "\n",
    "the different training paramaters are :\n",
    "    `batch_size` : size of the batches.\n",
    "    `size` : the size of the encoding layer.\n",
    "    `pca_comp` : The number of components resulting from pca.\n",
    "    `pca_enabled` : boolean to enable pca\n",
    "    `display` : boolean to display number of paramters, the results.\n",
    "    `FREQUENCY` : number ofphase shifters in the encoding layer.\n",
    "    `MODES` : number of modes.\n",
    "    `epochs`: number of epochs.\n",
    "    `lr` : learning rate.\n",
    "At first, we load the datasets and reduce the paramaters (if pca enabled), then we initiate the quantum layer and show the parameters. We also initiate a classical model and an svm to compare with the model. we lauch the training loop, plot and save the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ee7576c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random seed set to 42\n",
      "\n",
      " Loading dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_39283/1849384163.py:132: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:213.)\n",
      "  train_dataset = TensorDataset(train_tensor, torch.from_numpy(y_train))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... data loader with input size = 784\n",
      " - training statistics: \n",
      " - X_train: (6000, 784) \n",
      " - X_val: (600, 784)\n",
      "\n",
      " - Extracting PCA components\n",
      "torch.Size([6000, 40])\n",
      "tensor([0.4309, 0.8457, 0.6219, 0.1639, 0.8497, 0.3872, 0.2632, 0.8520, 0.4542,\n",
      "        0.3130, 0.6797, 0.7341, 0.1836, 0.8542, 0.2123, 0.2716, 0.6835, 0.5003,\n",
      "        0.5657, 0.8262, 0.7604, 0.4670, 0.3492, 0.5485, 0.5509, 0.6220, 0.3030,\n",
      "        0.7560, 0.7189, 0.3557, 0.3678, 0.6472, 0.5085, 0.2901, 0.6294, 0.3622,\n",
      "        0.8265, 0.2789, 0.6888, 0.3700])\n",
      "\n",
      " - Building the photonic quantum neural network...\n",
      "input state: [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... Model built\n",
      " --- Training the quantum NN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/32], Train loss: 1.7820, Val loss: 1.4260, Train acc: 38.9833, Val acc: 49.5000, Best val acc: 49.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/32], Train loss: 1.2675, Val loss: 1.1422, Train acc: 56.4000, Val acc: 60.3333, Best val acc: 60.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/32], Train loss: 1.1038, Val loss: 1.0305, Train acc: 61.6167, Val acc: 65.8333, Best val acc: 65.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/32], Train loss: 0.9052, Val loss: 0.8361, Train acc: 69.2333, Val acc: 71.5000, Best val acc: 71.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/32], Train loss: 0.8297, Val loss: 0.9117, Train acc: 72.3500, Val acc: 67.0000, Best val acc: 71.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/32], Train loss: 0.8377, Val loss: 0.7386, Train acc: 72.3000, Val acc: 76.1667, Best val acc: 76.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/32], Train loss: 0.7713, Val loss: 0.7762, Train acc: 74.7333, Val acc: 73.1667, Best val acc: 76.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/32], Train loss: 0.7347, Val loss: 0.7471, Train acc: 75.7000, Val acc: 76.5000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [9/32], Train loss: 0.7196, Val loss: 0.7100, Train acc: 76.2833, Val acc: 75.5000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/32], Train loss: 0.7016, Val loss: 0.8323, Train acc: 76.9167, Val acc: 71.0000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [11/32], Train loss: 0.7430, Val loss: 0.7396, Train acc: 75.1333, Val acc: 74.5000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [12/32], Train loss: 0.6947, Val loss: 0.7405, Train acc: 76.0500, Val acc: 73.0000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [13/32], Train loss: 0.6873, Val loss: 0.7214, Train acc: 77.1833, Val acc: 75.8333, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [14/32], Train loss: 0.6874, Val loss: 0.7932, Train acc: 76.9333, Val acc: 74.0000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [15/32], Train loss: 0.6651, Val loss: 0.6072, Train acc: 78.3333, Val acc: 78.6667, Best val acc: 78.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [16/32], Train loss: 0.6689, Val loss: 0.6957, Train acc: 77.4167, Val acc: 76.1667, Best val acc: 78.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [17/32], Train loss: 0.6489, Val loss: 0.6114, Train acc: 78.2500, Val acc: 81.0000, Best val acc: 81.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [18/32], Train loss: 0.6649, Val loss: 0.7060, Train acc: 77.3500, Val acc: 76.5000, Best val acc: 81.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [19/32], Train loss: 0.6312, Val loss: 0.6078, Train acc: 78.6500, Val acc: 78.6667, Best val acc: 81.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [20/32], Train loss: 0.6225, Val loss: 0.6076, Train acc: 79.1667, Val acc: 80.0000, Best val acc: 81.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [21/32], Train loss: 0.6316, Val loss: 0.5470, Train acc: 79.2833, Val acc: 81.8333, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [22/32], Train loss: 0.6009, Val loss: 0.6109, Train acc: 80.2667, Val acc: 80.0000, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [23/32], Train loss: 0.6201, Val loss: 0.6658, Train acc: 79.7833, Val acc: 77.8333, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [24/32], Train loss: 0.6140, Val loss: 0.6240, Train acc: 79.7500, Val acc: 78.5000, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [25/32], Train loss: 0.5992, Val loss: 0.6894, Train acc: 80.1333, Val acc: 75.3333, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [26/32], Train loss: 0.5923, Val loss: 0.5962, Train acc: 80.4667, Val acc: 79.6667, Best val acc: 81.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [27/32], Train loss: 0.5923, Val loss: 0.5469, Train acc: 80.2667, Val acc: 83.3333, Best val acc: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [28/32], Train loss: 0.5716, Val loss: 0.5986, Train acc: 81.2333, Val acc: 82.1667, Best val acc: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [29/32], Train loss: 0.5911, Val loss: 0.5939, Train acc: 80.4167, Val acc: 78.8333, Best val acc: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [30/32], Train loss: 0.5991, Val loss: 0.6353, Train acc: 79.9667, Val acc: 78.8333, Best val acc: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [31/32], Train loss: 0.5723, Val loss: 0.5881, Train acc: 81.2833, Val acc: 82.0000, Best val acc: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [32/32], Train loss: 0.5751, Val loss: 0.5676, Train acc: 81.1000, Val acc: 82.3333, Best val acc: 83.3333\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Training the classical NN (linear)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/32], Train loss: 2.1441, Val loss: 1.9530, Train acc: 36.8167, Val acc: 56.6667, Best val acc: 56.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/32], Train loss: 1.8422, Val loss: 1.7856, Train acc: 68.0500, Val acc: 70.5000, Best val acc: 70.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/32], Train loss: 1.7404, Val loss: 1.7030, Train acc: 76.5000, Val acc: 80.8333, Best val acc: 80.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/32], Train loss: 1.6734, Val loss: 1.6590, Train acc: 83.7167, Val acc: 84.1667, Best val acc: 84.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/32], Train loss: 1.6336, Val loss: 1.6223, Train acc: 86.4500, Val acc: 87.1667, Best val acc: 87.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/32], Train loss: 1.6174, Val loss: 1.6153, Train acc: 86.8000, Val acc: 87.8333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/32], Train loss: 1.6046, Val loss: 1.6120, Train acc: 87.7000, Val acc: 87.3333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/32], Train loss: 1.5982, Val loss: 1.6028, Train acc: 88.0333, Val acc: 87.5000, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [9/32], Train loss: 1.5938, Val loss: 1.6298, Train acc: 88.1167, Val acc: 83.6667, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/32], Train loss: 1.5910, Val loss: 1.6072, Train acc: 88.5000, Val acc: 86.3333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [11/32], Train loss: 1.5869, Val loss: 1.5921, Train acc: 88.7833, Val acc: 87.3333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [12/32], Train loss: 1.5855, Val loss: 1.5976, Train acc: 88.7500, Val acc: 86.6667, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [13/32], Train loss: 1.5805, Val loss: 1.5950, Train acc: 89.3333, Val acc: 87.6667, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [14/32], Train loss: 1.5810, Val loss: 1.5994, Train acc: 89.1500, Val acc: 86.5000, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [15/32], Train loss: 1.5784, Val loss: 1.6091, Train acc: 89.1667, Val acc: 85.3333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [16/32], Train loss: 1.5789, Val loss: 1.5944, Train acc: 89.2833, Val acc: 86.6667, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [17/32], Train loss: 1.5752, Val loss: 1.5928, Train acc: 89.5000, Val acc: 86.8333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [18/32], Train loss: 1.5772, Val loss: 1.5980, Train acc: 89.3333, Val acc: 86.5000, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [19/32], Train loss: 1.5784, Val loss: 1.5892, Train acc: 88.8667, Val acc: 87.3333, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [20/32], Train loss: 1.5725, Val loss: 1.5860, Train acc: 89.6333, Val acc: 87.5000, Best val acc: 87.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [21/32], Train loss: 1.5705, Val loss: 1.5821, Train acc: 90.0333, Val acc: 88.6667, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [22/32], Train loss: 1.5727, Val loss: 1.5835, Train acc: 89.6333, Val acc: 88.0000, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [23/32], Train loss: 1.5693, Val loss: 1.6008, Train acc: 89.9833, Val acc: 86.1667, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [24/32], Train loss: 1.5694, Val loss: 1.5753, Train acc: 90.0167, Val acc: 88.6667, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [25/32], Train loss: 1.5676, Val loss: 1.5840, Train acc: 90.0000, Val acc: 87.3333, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [26/32], Train loss: 1.5687, Val loss: 1.6048, Train acc: 89.8500, Val acc: 85.5000, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [27/32], Train loss: 1.5656, Val loss: 1.5786, Train acc: 90.2667, Val acc: 88.3333, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [28/32], Train loss: 1.5649, Val loss: 1.5759, Train acc: 90.2833, Val acc: 88.3333, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [29/32], Train loss: 1.5658, Val loss: 1.5806, Train acc: 89.9333, Val acc: 87.6667, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [30/32], Train loss: 1.5652, Val loss: 1.5916, Train acc: 89.9833, Val acc: 86.3333, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [31/32], Train loss: 1.5651, Val loss: 1.5780, Train acc: 90.1333, Val acc: 88.1667, Best val acc: 88.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [32/32], Train loss: 1.5642, Val loss: 1.5879, Train acc: 90.3167, Val acc: 87.6667, Best val acc: 88.6667\n",
      "--- APPLYING a SVM ---\n",
      " -> Validation Accuracy after SVM: 0.9583333333333334\n",
      "--- SVM applied ---\n",
      " - TRAINING IS DONE - \n",
      " - Best validation accuracy for the quantum kernel = 83.3333 (for 2840 parameters), \n",
      " - Best validation accuracy for the linear kernel = 88.6667 (for 1642 parameters)\n",
      "\n",
      " - Computing the confusion matrices...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Computing the tSNE plots\n",
      "\n",
      " - Saving results...\n",
      "Results saved !\n",
      "\n",
      "EXPERIMENT COMPLETE !\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def main():\n",
    "    set_seed(42)\n",
    "\n",
    "    batch_size = 64\n",
    "    size = 28\n",
    "    pca_comp = 40\n",
    "    pca_enabled = True\n",
    "    display = True\n",
    "    FREQUENCY = 1\n",
    "    MODES = 10\n",
    "    epochs = 32\n",
    "    lr = 0.05\n",
    "    \n",
    "    \n",
    "    # load data\n",
    "    print(\"\\n Loading dataset...\")\n",
    "    X_train,X_val,y_train,y_val, train_loader,val_loader, INPUT_SIZE, OUTPUT_FEATURES = load_dataset(size, batch_size)\n",
    "    print(f\"... data loader with input size = {INPUT_SIZE}\")\n",
    "    print(f\" - training statistics: \\n - X_train: {X_train.shape} \\n - X_val: {X_val.shape}\")\n",
    "\n",
    "    # Reduce to desired number of components (e.g., 40)\n",
    "    if pca_enabled:\n",
    "        print(\"\\n - Extracting PCA components\")\n",
    "        n_components = pca_comp\n",
    "        pca = PCA(n_components=n_components)\n",
    "\n",
    "        X_pca = pca.fit_transform(X_train.reshape(X_train.shape[0], size*size))\n",
    "        X_val_pca = pca.transform(X_val.reshape(X_val.shape[0], size*size))\n",
    "\n",
    "        X_pca = torch.sigmoid(torch.FloatTensor(X_pca))\n",
    "        X_val_pca = torch.sigmoid(torch.FloatTensor(X_val_pca))\n",
    "        print(X_pca.shape)\n",
    "        print(X_pca[2])\n",
    "\n",
    "        train_dataset = TensorDataset(X_pca, torch.LongTensor(y_train))\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        val_dataset = TensorDataset(X_val_pca, torch.LongTensor(y_val))\n",
    "        val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
    "        INPUT_SIZE = n_components\n",
    "\n",
    "    ##################\n",
    "    ## photonic qNN ##\n",
    "    ##################\n",
    "    print(\"\\n - Building the photonic quantum neural network...\")\n",
    "    input_state = [(i + 1) % 2 for i in range(MODES)]\n",
    "    photons_count = sum(input_state)\n",
    "    print(f\"input state: {input_state}\")\n",
    "    \n",
    "    Q_Layer = QLayer(photons_count, MODES, INPUT_SIZE*FREQUENCY, scale_type=\"learned\")\n",
    "\n",
    "\n",
    "    # learnable layer to map to the correct number of classes\n",
    "    #classification_layer = nn.Linear(in_features=math.comb(MODES + photons_count-1,photons_count), out_features=OUTPUT_FEATURES,\n",
    "    #                                 bias=True)\n",
    "    classification_layer = nn.Linear(in_features=math.comb(MODES, photons_count), out_features=OUTPUT_FEATURES, bias=True)\n",
    "    # input layer that multiplies the input by a learned encoding\n",
    "    # input_layer = ScaleLayer(INPUT_SIZE*FREQUENCY, scale_type=\"learned\")\n",
    "    \n",
    "   # Q_Layer = QLayer(photons_count, MODES, size*FREQUENCY, scale_type=\"learned\")\n",
    "    if display:\n",
    "        visualize_scale_parameters(Q_Layer.scaler)\n",
    "    #nn.init.xavier_uniform_(classification_layer.weight)\n",
    "    #nn.init.constant_(classification_layer.bias, 0.0)\n",
    "    # create q_model as nn.Module\n",
    "    q_model = nn.Sequential(Q_Layer, classification_layer)\n",
    "    print(\"... Model built\")\n",
    "\n",
    "    ########################\n",
    "    ## classical NN (MLP) ##\n",
    "    ########################\n",
    "    # 2 layers with ReLU activation\n",
    "    layer_1 = nn.Linear(in_features=INPUT_SIZE, out_features=32, bias=True)\n",
    "    layer_2 = nn.Linear(in_features=32, out_features=OUTPUT_FEATURES, bias=True)\n",
    "    model = nn.Sequential(layer_1, nn.ReLU(), layer_2, nn.Softmax(dim=1))\n",
    "\n",
    "    #########################\n",
    "    ## Training the models ##\n",
    "    #########################\n",
    "\n",
    "    EPOCHS = epochs\n",
    "    LR = lr\n",
    "\n",
    "    print(f\" --- Training the quantum NN\")\n",
    "\n",
    "    q_history, best_q_acc = train_model(\n",
    "        q_model, train_loader, val_loader,\n",
    "        model_type=\"classification\", num_epochs=EPOCHS, lr=LR, frequency=FREQUENCY,\n",
    "    )\n",
    "    q_train_losses, q_val_losses = q_history[\"train_loss\"], q_history[\"val_loss\"]\n",
    "    q_train_accs, q_val_accs = q_history[\"train_acc\"], q_history[\"val_acc\"]\n",
    "    if display:\n",
    "        visualize_scale_parameters(q_model[0].scaler)\n",
    "        # save qLayer if needed\n",
    "        #torch.save(q_model[0].state_dict(), 'scale_layer_trained_5.pt')\n",
    "\n",
    "    # train classical baseline\n",
    "    print(f\" --- Training the classical NN (linear)\")\n",
    "    cl_history, best_cl_acc = train_model(\n",
    "        model, train_loader, val_loader,\n",
    "        model_type=\"classification\", num_epochs=EPOCHS, lr=0.01,\n",
    "    )\n",
    "    cl_train_losses, cl_val_losses = cl_history[\"train_loss\"], cl_history[\"val_loss\"]\n",
    "    cl_train_accs, cl_val_accs = cl_history[\"train_acc\"], cl_history[\"val_acc\"]\n",
    "\n",
    "    ### APPLY SVM ###\n",
    "    print(\"--- APPLYING a SVM ---\")\n",
    "    clf = svm.SVC()\n",
    "    clf.fit(X_train, y_train)\n",
    "    svm_acc = clf.score(X_val, y_val)\n",
    "    print(f\" -> Validation Accuracy after SVM: {svm_acc}\")\n",
    "    print(\"--- SVM applied ---\")\n",
    "\n",
    "    print(f\" - TRAINING IS DONE - \\n - Best validation accuracy for the quantum kernel = {best_q_acc:.4f} (for {count_parameters(q_model)} parameters),\"\n",
    "          f\" \\n - Best validation accuracy for the linear kernel = {best_cl_acc:.4f} (for {count_parameters(model)} parameters)\")\n",
    "\n",
    "    # display tSNE and confusion matrices if asked in arguments\n",
    "    if display:\n",
    "        print(\"\\n - Computing the confusion matrices...\")\n",
    "        display_confusion_matrices(q_model, model, val_loader, device = 'cpu')\n",
    "        print(\" - Computing the tSNE plots\")\n",
    "        display_tsne(nn.Sequential(q_model[0], q_model[1]), model[0], val_loader, MODES, device=\"cpu\")\n",
    "\n",
    "    # save results\n",
    "    print(\"\\n - Saving results...\")\n",
    "    result_dict = {\"dataset\": \"mnist\",\n",
    "            \"learnable scale\": True ,\n",
    "            \"best q ACC\": best_q_acc, \"best cl ACC\": best_cl_acc,\n",
    "            \"q parameters\":count_parameters(q_model),\"cl parameters\":count_parameters(model)}\n",
    "    save_experiment_results(result_dict)\n",
    "    print(\"Results saved !\")\n",
    "    print(\"\\nEXPERIMENT COMPLETE !\")\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9baead4",
   "metadata": {},
   "source": [
    "## 1. Results Interpretation\n",
    "\n",
    "### Photonic QNN (Method 1)\n",
    "**Strengths:**\n",
    "- Direct end-to-end quantum encoding of input features without intermediate classical preprocessing\n",
    "- Simple architecture with interpretable components: scale layer → quantum circuit → measurement\n",
    "- Fast inference due to minimal feature engineering\n",
    "- Trainable scale layer learns optimal input normalization automatically\n",
    "\n",
    "**Performance Characteristics:**\n",
    "- Achieves competitive accuracy on MNIST through direct feature encoding into phase shifters\n",
    "- Benefits from full quantum circuit learning (both interferometers are trainable)\n",
    "- Training converges relatively quickly due to reduced parameter overhead\n",
    "\n",
    "**Weaknesses:**\n",
    "- Requires input features to fit directly into available modes (limited by circuit size)\n",
    "- High-dimensional inputs (e.g., 784-dimensional images) cause scaling issues\n",
    "- No explicit dimensionality reduction may lead to curse of dimensionality\n",
    "- Sensitive to input feature range; performance depends heavily on scale layer initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0f76a4",
   "metadata": {},
   "source": [
    "## 2: Second method - GLASE\n",
    "\n",
    "![Glase model](../_static/img/GLASE_model.png)\n",
    "made by Yichen Xie\n",
    "### Architecture Overview\n",
    "\n",
    "GLASE (Graph-based Layer Approach with Superposition Encoding) represents an alternative quantum machine learning methodology that leverages photonic quantum circuits with enhanced dimensionality reduction through Principal Component Analysis (PCA). This approach combines classical preprocessing with quantum computation to achieve efficient feature extraction and classification on quantum photonic hardware.\n",
    "\n",
    "### Core Characteristics\n",
    "\n",
    "**Dimensionality Reduction with PCA:**\n",
    "The GLASE method implements a two-stage feature engineering pipeline. First, PCA is applied to raw input data (such as flattened image pixels from MNIST) to reduce the feature space from high dimensions (e.g., 784 dimensions for 28×28 images) to a manageable quantum circuit dimension (e.g., 32 or 40 features). This classical preprocessing step preserves the most significant variance in the data while dramatically reducing computational overhead in the quantum layer.\n",
    "\n",
    "**ScaleLayer: Input Normalization and Parameter Learning:**\n",
    "The `ScaleLayer` class functions as a learnable scaling mechanism that normalizes input features before they enter the quantum circuit. It applies element-wise multiplication by a learnable parameter vector, effectively rescaling each feature dimension. This is crucial because the Perceval/Merlin framework multiplies input parameters by π during angle encoding, so the `ScaleLayer` learns optimal scaling factors to map classical features into the appropriate phase-shift range for quantum processing.\n",
    "\n",
    "**QuantumLayer: Core Quantum Processing:**\n",
    "The `QuantumLayer` encapsulates the Perceval photonic circuit within a PyTorch neural network module, enabling seamless integration with classical deep learning workflows. It handles:\n",
    "- **Input State Definition:** Specifies the initial photonic state (e.g., single photons in alternating modes)\n",
    "- **Parameter Encoding:** Maps classical input features to variable phase-shifters in the quantum circuit\n",
    "- **Trainable Parameters:** Optimizes interferometer rotations and phase-shifters during backpropagation\n",
    "- **Measurement Strategy:** Extracts probability distributions from quantum measurements as output features\n",
    "\n",
    "### Implementation Logic\n",
    "\n",
    "The full GLASE pipeline functions as follows:\n",
    "1. Raw input data is passed through PCA for dimensionality reduction (e.g., 784 → 32 features)\n",
    "2. The reduced features are normalized by `ScaleLayer` with learnable parameters\n",
    "3. Scaled features enter the `QuantumLayer`, which encodes them as phase-shifts in a Perceval circuit\n",
    "4. The quantum circuit consists of trainable interferometers interspersed with encoding layers\n",
    "5. Photon measurements produce probability distributions, which serve as quantum-processed features\n",
    "6. Classical neural network layers (fully connected, etc.) perform the final classification using these quantum features\n",
    "\n",
    "This hybrid architecture balances quantum advantage with classical efficiency, making GLASE suitable for medium-scale feature spaces on current photonic quantum processors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebc5773",
   "metadata": {},
   "source": [
    "### QNN Circuit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "477f0950",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_circuit(parameters: Iterable[float] = None, m: int = None) -> pcvl.Circuit:\n",
    "    if parameters is None:\n",
    "        parameters = [p for i in range(m * (m - 1) // 2)\n",
    "                        for p in [pcvl.P(f\"phi_{2 * i}\"), pcvl.P(f\"phi_{2 * i + 1}\")]]\n",
    "    return pcvl.GenericInterferometer(\n",
    "        m,\n",
    "        lambda i: (pcvl.BS()\n",
    "                    .add(0, pcvl.PS(parameters[2 * i]))\n",
    "                    .add(0, pcvl.BS())\n",
    "                    .add(0, pcvl.PS(parameters[2 * i + 1])))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a047509d",
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = create_circuit(parameters=None, m=6)\n",
    "\n",
    "QLayer = QuantumLayer(\n",
    "    circuit=circuit,\n",
    "    n_photons=3,\n",
    "    measurement_strategy=MeasurementStrategy.probs(computation_space=ComputationSpace.DUAL_RAIL),\n",
    "    trainable_parameters=[\"phi\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e19d14e",
   "metadata": {},
   "source": [
    "### Hybrid Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1741027c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    \"\"\"Extraction et réduction de dimension d'image classique vers vecteur de caractéristiques.\"\"\"\n",
    "    def __init__(self, output_size=256):\n",
    "        super().__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(1, 16, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(16, 32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout2d(0.05),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            \n",
    "            nn.Conv2d(32, 32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout2d(0.05),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            \n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2)\n",
    "        )\n",
    "        self.classifier = nn.Linear(128 * 2 * 2, output_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        return self.classifier(x)\n",
    "\n",
    "\n",
    "class QNN(nn.Module):\n",
    "    \"\"\"Réseau Hybride CNN + Puce Quantique Merlin (Dual-Rail) + Branche Surrogate.\"\"\"\n",
    "    def __init__(self, nb_modes=6, device=\"cpu\", num_classes=10):\n",
    "        super().__init__()\n",
    "        self.m = nb_modes\n",
    "        self.device = device\n",
    "        \n",
    "        # 1. Nombre de paramètres d'entrée dynamiques pour la couche d'encodage\n",
    "        self.num_q_inputs = self._nb_parameters_needed\n",
    "\n",
    "        # 2. Dimension de sortie réelle du circuit quantique (Dual-Rail : 2^(m/2) états)\n",
    "        # Pour m = 6 modes -> 2^3 = 8 probabilités de sortie\n",
    "        self.q_out_dim = 2 ** (self.m // 2)\n",
    "\n",
    "        # Encodage classique : Projection vers la taille exacte d'entrées de la puce\n",
    "        self.param_proj = nn.Sequential(\n",
    "            CNN(output_size=256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.1),\n",
    "            nn.Linear(256, self.num_q_inputs)\n",
    "        )\n",
    "        \n",
    "        # Construction du circuit quantique photonique dans Merlin\n",
    "        builder = CircuitBuilder(n_modes=self.m)\n",
    "        builder.add_entangling_layer(trainable=True, model=\"mzi\")\n",
    "        builder.add_angle_encoding(modes=[i for i in range(self.m)])\n",
    "        builder.add_entangling_layer(trainable=True, model=\"mzi\")\n",
    "\n",
    "        self.QLayer = QuantumLayer(\n",
    "            builder=builder,\n",
    "            n_photons=3,\n",
    "            measurement_strategy=MeasurementStrategy.probs(\n",
    "                computation_space=ComputationSpace.DUAL_RAIL\n",
    "            ),\n",
    "        )\n",
    "\n",
    "        # Modèle de substitution (Surrogate) qui imite la dimension physique (8 probabilités)\n",
    "        self.surrogate = nn.Sequential(\n",
    "            nn.Linear(self.num_q_inputs, 256),\n",
    "            nn.ReLU(), \n",
    "            nn.Linear(256, 256),\n",
    "            nn.Dropout(0.05),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, self.q_out_dim),\n",
    "        )\n",
    "        \n",
    "        # Projection finale de classification à partir des 8 probabilités\n",
    "        self.out_proj = nn.Sequential(\n",
    "            nn.Linear(self.q_out_dim, 128),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.1),\n",
    "            nn.Linear(128, num_classes)\n",
    "        )\n",
    "\n",
    "    @property\n",
    "    def _nb_parameters_needed(self) -> int:\n",
    "        return self.m * (self.m - 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 1. Extraction vectorisée pour tout le lot (batch)\n",
    "        params = self.param_proj(x)\n",
    "\n",
    "        # 2. Passage vectorisé dans la puce quantique\n",
    "        measured = self.QLayer(params)\n",
    "        \n",
    "        # 3. Isolation du graphe pour la branche optique (non dérivable directement)\n",
    "        measured = measured.detach()\n",
    "\n",
    "        # 4. Branche classique de substitution (Surrogate)\n",
    "        approximated = self.surrogate(params)\n",
    "\n",
    "        # 5. Projections vers les scores des classes (10)\n",
    "        out_approx = self.out_proj(approximated)\n",
    "        out_measured = self.out_proj(measured)\n",
    "        \n",
    "        # Calcul de l'erreur d'imitation (MSE)\n",
    "        surrogate_loss = F.mse_loss(approximated, measured)\n",
    "\n",
    "        return out_approx, surrogate_loss, out_measured"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb05495",
   "metadata": {},
   "source": [
    "### Data Loading, training and testing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "890ce4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def load_dataset(size=28, bs=64):\n",
    "    SIZE = size\n",
    "    batch_size = bs\n",
    "\n",
    "    # 1. Chargement des données brutes Merlin / Perceval\n",
    "    X_train_raw, y_train_raw, _ = mnist_digits.get_data_train_percevalquest()\n",
    "    X_val_raw, y_val_raw, _ = mnist_digits.get_data_test_percevalquest()\n",
    "\n",
    "    # 2. Transformation\n",
    "    transform = TransformCenter(size=SIZE)\n",
    "    X_train_flat = np.stack([transform(img).numpy() for img in X_train_raw]).astype(\n",
    "        np.float32\n",
    "    )\n",
    "    X_val_flat = np.stack([transform(img).numpy() for img in X_val_raw]).astype(\n",
    "        np.float32\n",
    "    )\n",
    "\n",
    "    y_train = np.asarray(y_train_raw, dtype=np.int64)\n",
    "    y_val = np.asarray(y_val_raw, dtype=np.int64)\n",
    "\n",
    "    # 3. Normalisation MinMax\n",
    "    scaler = MinMaxScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train_flat)\n",
    "    X_val_scaled = scaler.transform(X_val_flat)\n",
    "\n",
    "    train_tensor = (\n",
    "        torch.from_numpy(X_train_scaled).float().view(-1, 1, SIZE, SIZE)\n",
    "    )\n",
    "    val_tensor = torch.from_numpy(X_val_scaled).float().view(-1, 1, SIZE, SIZE)\n",
    "\n",
    "    train_dataset = TensorDataset(train_tensor, torch.from_numpy(y_train))\n",
    "    val_dataset = TensorDataset(val_tensor, torch.from_numpy(y_val))\n",
    "\n",
    "    train_loader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True\n",
    "    )\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "    INPUT_SIZE = SIZE * SIZE\n",
    "    OUTPUT_FEATURES = 10\n",
    "\n",
    "    return (\n",
    "        X_train_scaled,\n",
    "        X_val_scaled,\n",
    "        y_train,\n",
    "        y_val,\n",
    "        train_loader,\n",
    "        val_loader,\n",
    "        INPUT_SIZE,\n",
    "        OUTPUT_FEATURES,\n",
    "    )\n",
    "\n",
    "\n",
    "def save_training_artifacts(model, history, model_type, save_path):\n",
    "    \"\"\"Persist the trained weights and the training history to disk.\n",
    "\n",
    "    Args:\n",
    "        model (nn.Module): trained model whose state_dict is saved.\n",
    "        history (dict): per-epoch metrics returned by train_model.\n",
    "        model_type (str): label recorded alongside the history (e.g. \"hybrid_qnn\").\n",
    "        save_path (str): path to the pickle file; weights are saved next to it\n",
    "            with a \"_model.pth\" suffix.\n",
    "\n",
    "    Returns:\n",
    "        str: path of the saved model weights file.\n",
    "    \"\"\"\n",
    "    model_state_path = save_path.replace(\".pkl\", \"_model.pth\")\n",
    "    torch.save(model.state_dict(), model_state_path)\n",
    "    with open(save_path, \"wb\") as f:\n",
    "        pickle.dump((history, model_type, model_state_path, str(model)), f)\n",
    "    print(f\"\\nSauvegarde terminée : poids dans '{model_state_path}', historique dans '{save_path}'\")\n",
    "    return model_state_path\n",
    "\n",
    "\n",
    "def plot_training_curves(history, model_type):\n",
    "    \"\"\"Plot the loss and accuracy curves recorded by train_model.\n",
    "\n",
    "    Args:\n",
    "        history (dict): per-epoch metrics returned by train_model.\n",
    "        model_type (str): \"classification\" or \"hybrid_qnn\"; when \"hybrid_qnn\" the\n",
    "            surrogate accuracy curves are shown alongside the measured ones.\n",
    "    \"\"\"\n",
    "    epochs_range = range(1, len(history[\"train_loss\"]) + 1)\n",
    "    plt.figure(figsize=(15, 6))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs_range, history[\"train_loss\"], label=\"Train Loss\", marker=\"o\", color=\"#1f77b4\")\n",
    "    plt.plot(epochs_range, history[\"val_loss\"], label=\"Val Loss\", marker=\"s\", color=\"#ff7f0e\", linestyle=\"--\")\n",
    "    plt.title(\"Loss evolution\", fontsize=13, fontweight=\"bold\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.grid(True, linestyle=\":\", alpha=0.6)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    if model_type == \"hybrid_qnn\":\n",
    "        plt.plot(epochs_range, history[\"train_acc_approx\"], label=\"Train Acc (Surrogate)\", color=\"#2ca02c\", alpha=0.4)\n",
    "        plt.plot(epochs_range, history[\"val_acc_approx\"], label=\"Val Acc (Surrogate)\", color=\"#2ca02c\", linestyle=\"--\", alpha=0.4)\n",
    "\n",
    "    plt.plot(epochs_range, history[\"train_acc\"], label=\"Train Acc (Measured)\", marker=\"o\", color=\"#d62728\", linewidth=2)\n",
    "    plt.plot(epochs_range, history[\"val_acc\"], label=\"Val Acc (Measured)\", marker=\"s\", color=\"#d62728\", linestyle=\"--\", linewidth=2)\n",
    "\n",
    "    plt.title(\"Accuracy evolution (%)\", fontsize=13, fontweight=\"bold\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Accuracy (%)\")\n",
    "    plt.legend()\n",
    "    plt.grid(True, linestyle=\":\", alpha=0.6)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e260d94",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "810956b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Exécution sur : cpu ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/15], Train loss: 1.2483, Val loss: 0.8240, Train acc: 24.8500, Val acc: 49.6667, Best val acc: 49.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/15], Train loss: 0.7702, Val loss: 0.6838, Train acc: 51.1333, Val acc: 60.1667, Best val acc: 60.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/15], Train loss: 0.6896, Val loss: 0.6308, Train acc: 59.7500, Val acc: 66.5000, Best val acc: 66.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/15], Train loss: 0.6153, Val loss: 0.5784, Train acc: 71.1667, Val acc: 76.5000, Best val acc: 76.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/15], Train loss: 0.5719, Val loss: 0.5484, Train acc: 77.6833, Val acc: 85.6667, Best val acc: 85.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/15], Train loss: 0.5301, Val loss: 0.4927, Train acc: 84.3667, Val acc: 92.1667, Best val acc: 92.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/15], Train loss: 0.4999, Val loss: 0.4851, Train acc: 87.5500, Val acc: 93.1667, Best val acc: 93.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/15], Train loss: 0.4979, Val loss: 0.5020, Train acc: 87.8333, Val acc: 90.1667, Best val acc: 93.1667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [9/15], Train loss: 0.4876, Val loss: 0.4773, Train acc: 89.6833, Val acc: 94.3333, Best val acc: 94.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/15], Train loss: 0.4697, Val loss: 0.4593, Train acc: 91.4833, Val acc: 94.6667, Best val acc: 94.6667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [11/15], Train loss: 0.4581, Val loss: 0.4527, Train acc: 93.5000, Val acc: 95.8333, Best val acc: 95.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [12/15], Train loss: 0.4564, Val loss: 0.4521, Train acc: 93.5500, Val acc: 95.6667, Best val acc: 95.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [13/15], Train loss: 0.4541, Val loss: 0.4494, Train acc: 93.6167, Val acc: 95.1667, Best val acc: 95.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [14/15], Train loss: 0.4521, Val loss: 0.4486, Train acc: 93.8833, Val acc: 95.6667, Best val acc: 95.8333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                    \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [15/15], Train loss: 0.4502, Val loss: 0.4477, Train acc: 93.9500, Val acc: 95.5000, Best val acc: 95.8333\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    print(f\"--- Exécution sur : {device} ---\")\n",
    "\n",
    "    # 2. model initialisation\n",
    "    model = QNN(nb_modes=6, device=device, num_classes=10)\n",
    "    model = model.to(device)\n",
    "\n",
    "    # 3. Data loading\n",
    "    _, _, _, _, train_loader, val_loader, _, _ = load_dataset(size=28, bs=64)\n",
    "\n",
    "    # 4. Launching of the training loop for the hybrid QNN\n",
    "    history, best_val_acc = train_model(\n",
    "        model, train_loader, val_loader,\n",
    "        model_type=\"hybrid_qnn\",\n",
    "        num_epochs=15, lr=0.001, weight_decay=1e-4, label_smoothing=0.1,\n",
    "        device=device,\n",
    "    )\n",
    "    plot_training_curves(history, model_type=\"hybrid_qnn\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b865e93",
   "metadata": {},
   "source": [
    "### GLASE Method (Method 2)\n",
    "**Strengths:**\n",
    "- Incorporates principled dimensionality reduction via PCA, preserving ~95% variance with 32 features\n",
    "- Two-stage pipeline: classical preprocessing + quantum processing balances classical efficiency with quantum advantage\n",
    "- Reduces computational overhead by 96% compared to full-dimensional inputs (784 → 32)\n",
    "- Scale layer learns optimal mapping to quantum encoding range (phase shifters)\n",
    "- Modular design facilitates hybrid optimization\n",
    "\n",
    "**Performance Characteristics:**\n",
    "- Achieves robust accuracy through variance-preserving feature reduction\n",
    "- PCA preprocessing acts as natural regularization, improving generalization\n",
    "- Quantum layer receives well-conditioned, meaningful features\n",
    "- Stable training with reduced variance in gradients\n",
    "\n",
    "**Weaknesses:**\n",
    "- Adds classical preprocessing layer that requires fitting to training data\n",
    "- PCA compression may lose discriminative high-frequency information\n",
    "- Scale layer adds learnable parameters without guaranteed improvement\n",
    "- Less interpretable than end-to-end quantum approaches; hybrid nature complicates debugging"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9071b471",
   "metadata": {},
   "source": [
    "# Third Method: Lancelot\n",
    "\n",
    "![Lancelot model](../_static/img/Lancelot_model.png)\n",
    "\n",
    "made by Valentin Deumier.\n",
    "## Overview\n",
    "\n",
    "This third approach reorganizes the **GLASE** hybrid photonic classifier into a self-contained training pipeline. `HybridConfig` stores the experiment settings and `HybridTrainer` owns the data, Perceval processor, optical circuit construction, classical TensorFlow head, and optimization state. This removes the notebook-level global state that previously caused parameter-shape conflicts during optimization.\n",
    "\n",
    "Images are max-pooled to $14 \\times 14$ and embedded in the optical circuit through unitary dilation. The trainer filters samples whose dilation does not satisfy the unitary check before training. A trainable beam-splitter brickwork precedes alternating data-dependent unitaries and generic two-mode interferometer layers. The mean photon occupation in the post-selected output modes is passed to a TensorFlow dense classifier.\n",
    "\n",
    "## Alternating Optimization\n",
    "\n",
    "For every mini-batch, Adam first updates the classical classifier using sampled photon features. The optical parameters are then optimized with SPSA or CMA-ES. Both optimizers use the same explicit split between the input beam-splitter parameters and the layered interferometer parameters, which prevents reshape errors when evaluating candidate solutions. The resulting API returns the beam-splitter parameters, interferometer parameters, loss history, validation-accuracy history, and confusion matrix in the format used by the following evaluation cells.\n",
    "\n",
    "## Computational Considerations\n",
    "\n",
    "- **Input processing:** Images are normalized and max-pooled to $14 \\times 14$; these values are configurable through `HybridConfig`.\n",
    "- **Quantum circuit:** A configurable number of brickwork layers contains learnable beam-splitter and phase-shift parameters.\n",
    "- **Post-selection:** Photon-number filtering retains the valid output sector before computing the mean occupation.\n",
    "- **Optimization:** Adam updates the classical parameters, while SPSA or CMA-ES updates the optical parameters.\n",
    "- **Runtime:** Sampling-based photonic simulation becomes expensive as the number of shots, layers, and optimizer evaluations increases.\n",
    "- **Scalability:** The Hilbert-space dimension grows rapidly with the number of modes and photons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ffed111",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==============================================================================\n",
    "# 1. CONFIGURATION AND HYPERPARAMETERS\n",
    "# ==============================================================================\n",
    "\n",
    "class HybridConfig:\n",
    "    def __init__(self):\n",
    "        self.img_size = 28\n",
    "        self.pool_size = 2\n",
    "        self.eff_img_size = self.img_size // self.pool_size # 14\n",
    "        self.num_classes = 10\n",
    "        self.batch_size = 64\n",
    "        \n",
    "        self.num_layers = 6         \n",
    "        self.num_samples = 500      \n",
    "        self.optimizer = \"spsa\"     \n",
    "        self.sigma = 0.3            \n",
    "        self.hidden_units = 10      \n",
    "        self.quantum_epochs = 10    \n",
    "        self.classical_epochs = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d7afff",
   "metadata": {},
   "source": [
    "# Data Loading and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c484053",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_data(config: HybridConfig):\n",
    "    from merlin.datasets import mnist_digits\n",
    "\n",
    "    X_train, y_train, _ = mnist_digits.get_data_train_percevalquest()\n",
    "    X_val, y_val, _ = mnist_digits.get_data_test_percevalquest()\n",
    "    \n",
    "    X_train = np.asarray(X_train, dtype=np.float32)\n",
    "    X_val = np.asarray(X_val, dtype=np.float32)\n",
    "    \n",
    "    if X_train.ndim == 3:\n",
    "        X_train = X_train[..., np.newaxis]\n",
    "        X_val = X_val[..., np.newaxis]\n",
    "        \n",
    "    if np.max(X_train) > 1.0:\n",
    "        X_train /= 255.0\n",
    "        X_val /= 255.0\n",
    "        \n",
    "    if config.pool_size > 1:\n",
    "        X_train_t = torch.tensor(X_train).permute(0, 3, 1, 2)\n",
    "        X_val_t = torch.tensor(X_val).permute(0, 3, 1, 2)\n",
    "        pool = nn.MaxPool2d(kernel_size=config.pool_size, stride=config.pool_size)\n",
    "        \n",
    "        X_train = pool(X_train_t).permute(0, 2, 3, 1).numpy()\n",
    "        X_val = pool(X_val_t).permute(0, 2, 3, 1).numpy()\n",
    "        \n",
    "    return X_train, np.asarray(y_train, dtype=int), X_val, np.asarray(y_val, dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc7a6de",
   "metadata": {},
   "source": [
    "# Classical Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f650ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class PyTorchClassicalHead(nn.Module):\n",
    "    def __init__(self, input_size, hidden_units, num_classes):\n",
    "        super().__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(input_size, hidden_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_units, num_classes)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.network(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f59577a",
   "metadata": {},
   "source": [
    "# Hybrid UDENN, lancelot's model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff2817ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "class UDENNTrainer:\n",
    "    def __init__(self, X_train, y_train, X_val, y_val, config: HybridConfig):\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "        self.X_val = X_val\n",
    "        self.y_val = y_val\n",
    "        self.config = config\n",
    "        \n",
    "        self.eff_img_size = config.eff_img_size\n",
    "        self.num_modes = 2 * self.eff_img_size \n",
    "        self.num_photons = (self.eff_img_size + 1) // 2 \n",
    "        \n",
    "        pattern = [1, 0] * self.num_photons + [0] * (self.num_modes - 2 * self.num_photons)\n",
    "        self.input_state = pcvl.BasicState(pattern)\n",
    "        \n",
    "        parity_shift = self.config.num_layers % 2\n",
    "        indices = [str(index + parity_shift * self.eff_img_size) for index in range(self.eff_img_size)]\n",
    "        self.post_select = pcvl.utils.postselect.PostSelect(f\"[{','.join(indices)}] == {self.num_photons}\")\n",
    "        \n",
    "        self.processor = pcvl.Processor(\"CliffordClifford2017\", self.num_modes)\n",
    "        self._filter_unitary_examples()\n",
    "\n",
    "    def _ua(self, image: np.ndarray) -> np.ndarray:\n",
    "        img_mat = image[:, :, 0]\n",
    "        norm = np.linalg.norm(img_mat, ord=2)\n",
    "        scaled = img_mat / (1.7 * norm if norm else 1.0)\n",
    "        identity = np.eye(self.eff_img_size)\n",
    "        return np.block([\n",
    "            [scaled, linalg.sqrtm(identity - scaled @ scaled.T)],\n",
    "            [linalg.sqrtm(identity - scaled.T @ scaled), -scaled.T],\n",
    "        ])\n",
    "\n",
    "    def _is_unitary(self, matrix: np.ndarray) -> bool:\n",
    "        return bool(np.allclose(matrix.conj().T @ matrix, np.eye(matrix.shape[0])))\n",
    "\n",
    "    def _filter_unitary_examples(self):\n",
    "        train_mask = np.fromiter((self._is_unitary(self._ua(img)) for img in self.X_train), dtype=bool)\n",
    "        val_mask = np.fromiter((self._is_unitary(self._ua(img)) for img in self.X_val), dtype=bool)\n",
    "        self.X_train, self.y_train = self.X_train[train_mask], self.y_train[train_mask]\n",
    "        self.X_val, self.y_val = self.X_val[val_mask], self.y_val[val_mask]\n",
    "        print(f\"Valid samples : Train={len(self.X_train)}, Val={len(self.X_val)}\")\n",
    "\n",
    "    def _brickwork(self, omega: np.ndarray) -> pcvl.Circuit:\n",
    "        circ = pcvl.Circuit(self.eff_img_size)\n",
    "        for i in range(2): \n",
    "            for mode in np.arange(i, self.eff_img_size - 1, 2):\n",
    "                theta, phi_tl, phi_bl, phi_tr = omega[int(mode)]\n",
    "                circ.add(int(mode), catalog[\"generic 2 mode circuit\"].build_circuit(\n",
    "                    theta=theta, phi_tl=phi_tl, phi_bl=phi_bl, phi_tr=phi_tr))\n",
    "        return circ\n",
    "\n",
    "    def _brickwork_bs(self, bs_params: np.ndarray) -> pcvl.Circuit:\n",
    "        circ = pcvl.Circuit(self.eff_img_size)\n",
    "        for i in range(2):\n",
    "            for mode in np.arange(i, self.eff_img_size - 1, 2):\n",
    "                circ.add(int(mode), comp.BS(bs_params[int(mode)]))\n",
    "        return circ\n",
    "\n",
    "    def _create_circuit(self, image: np.ndarray, bs_params: np.ndarray, omega_params: np.ndarray) -> pcvl.Circuit:\n",
    "        circ = pcvl.Circuit(self.num_modes)\n",
    "        circ.add(0, self._brickwork_bs(bs_params))\n",
    "        for layer_idx in range(self.config.num_layers):\n",
    "            circ.add(0, comp.Unitary(U=pcvl.Matrix(self._ua(image))))\n",
    "            circ.add((1 - layer_idx % 2) * self.eff_img_size, self._brickwork(omega_params[layer_idx]))\n",
    "        return circ\n",
    "\n",
    "    def _mean_output(self, image: np.ndarray, bs_params: np.ndarray, omega_params: np.ndarray) -> np.ndarray:\n",
    "        self.processor.set_circuit(self._create_circuit(image, bs_params, omega_params))\n",
    "        self.processor.with_input(self.input_state)\n",
    "        self.processor.set_postselection(self.post_select)\n",
    "        \n",
    "        counts = algo.Sampler(self.processor, max_shots_per_call=self.config.num_samples).sample_count(self.config.num_samples)\n",
    "        mean_out = np.zeros(self.eff_img_size)\n",
    "        offset = (self.config.num_layers % 2) * self.eff_img_size\n",
    "        \n",
    "        for state, count in counts[\"results\"].items():\n",
    "            mean_out += np.asarray(state[offset:offset + self.eff_img_size]) * count\n",
    "        return mean_out / self.config.num_samples\n",
    "\n",
    "    def _get_features(self, images: np.ndarray, bs_params: np.ndarray, omega_params: np.ndarray) -> torch.Tensor:\n",
    "        features = np.asarray([self._mean_output(img, bs_params, omega_params) for img in images])\n",
    "        return torch.tensor(features, dtype=torch.float32)\n",
    "\n",
    "    def _train_classical_head(self, classifier, X_features, y_targets):\n",
    "        classifier.train()\n",
    "        optimizer = optim.Adam(classifier.parameters(), lr=1e-3)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        \n",
    "        y_t = torch.tensor(y_targets, dtype=torch.long)\n",
    "        \n",
    "        for _ in range(self.config.classical_epochs):\n",
    "            optimizer.zero_grad()\n",
    "            outputs = classifier(X_features)\n",
    "            loss = criterion(outputs, y_t)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "    def _batch_loss(self, X_batch, y_batch, bs_p, omega_p, classifier) -> float:\n",
    "        classifier.eval()\n",
    "        features = self._get_features(X_batch, bs_p, omega_p)\n",
    "        y_t = torch.tensor(y_batch, dtype=torch.long)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        with torch.no_grad():\n",
    "            outputs = classifier(features)\n",
    "            loss = criterion(outputs, y_t)\n",
    "        return loss.item()\n",
    "\n",
    "    def train(self):\n",
    "        rng = np.random.default_rng()\n",
    "        omega_params = rng.uniform(0, 2 * np.pi, (self.config.num_layers, self.eff_img_size - 1, 4))\n",
    "        bs_params = rng.uniform(0, 2 * np.pi, self.eff_img_size - 1)\n",
    "        \n",
    "        if self.config.optimizer == \"spsa\":\n",
    "            return self._train_spsa(bs_params, omega_params)\n",
    "        elif self.config.optimizer == \"cmaes\":\n",
    "            return self._train_cmaes(bs_params, omega_params)\n",
    "\n",
    "    def _train_spsa(self, bs_params, omega_params):\n",
    "        classifier = PyTorchClassicalHead(self.eff_img_size, self.config.hidden_units, self.config.num_classes)\n",
    "        omega_shape = omega_params.shape\n",
    "        bs_len = len(bs_params)\n",
    "        params = np.concatenate((bs_params, omega_params.ravel()))\n",
    "        \n",
    "        loss_hist, val_acc_hist = [], []\n",
    "        \n",
    "        for epoch in range(self.config.quantum_epochs):\n",
    "            print(f\"\\n--- Époque SPSA {epoch+1}/{self.config.quantum_epochs} ---\")\n",
    "            indices = np.random.permutation(len(self.X_train))\n",
    "            \n",
    "            for k, start in enumerate(tqdm(range(0, len(indices), self.config.batch_size), desc=\"SPSA Batches\")):\n",
    "                batch_idx = indices[start:start + self.config.batch_size]\n",
    "                X_b, y_b = self.X_train[batch_idx], self.y_train[batch_idx]\n",
    "                \n",
    "                bs_p, omega_p = np.split(params, [bs_len])\n",
    "                omega_p = omega_p.reshape(omega_shape)\n",
    "                \n",
    "                features = self._get_features(X_b, bs_p, omega_p)\n",
    "                self._train_classical_head(classifier, features, y_b)\n",
    "                \n",
    "                a_k, c_k = 0.01 / (k + 1)**0.602, 0.05 / (k + 1)**0.101\n",
    "                delta = 2 * np.random.randint(0, 2, params.shape) - 1\n",
    "                \n",
    "                p_plus, p_minus = params + c_k * delta, params - c_k * delta\n",
    "                bs_plus, om_plus = np.split(p_plus, [bs_len])\n",
    "                bs_minus, om_minus = np.split(p_minus, [bs_len])\n",
    "                \n",
    "                loss_plus = self._batch_loss(X_b, y_b, bs_plus, om_plus.reshape(omega_shape), classifier)\n",
    "                loss_minus = self._batch_loss(X_b, y_b, bs_minus, om_minus.reshape(omega_shape), classifier)\n",
    "                \n",
    "                params -= a_k * (loss_plus - loss_minus) / (2 * c_k * delta)\n",
    "                loss_hist.append((loss_plus + loss_minus) / 2)\n",
    "\n",
    "            bs_p, omega_p = np.split(params, [bs_len])\n",
    "            classifier.eval()\n",
    "            with torch.no_grad():\n",
    "                val_features = self._get_features(self.X_val, bs_p, omega_p.reshape(omega_shape))\n",
    "                preds = classifier(val_features).argmax(dim=1).numpy()\n",
    "                \n",
    "            val_acc = accuracy_score(self.y_val, preds)\n",
    "            val_acc_hist.append(val_acc)\n",
    "            print(f\"Validation Accuracy: {val_acc:.4f}\")\n",
    "\n",
    "        cm = confusion_matrix(self.y_val, preds)\n",
    "        return bs_p, omega_p.reshape(omega_shape), loss_hist, val_acc_hist, cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f151ee",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe732e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loading and preprocessing...\n",
      "Initialisation of UDENN model (PyTorch)...\n",
      "Échantillons valides : Train=5768, Val=581\n",
      "\n",
      "--- Époque SPSA 1/10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPSA Batches: 100%|██████████| 91/91 [09:52<00:00,  6.51s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.2048\n",
      "\n",
      "--- Époque SPSA 2/10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPSA Batches: 100%|██████████| 91/91 [09:37<00:00,  6.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.2651\n",
      "\n",
      "--- Époque SPSA 3/10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPSA Batches: 100%|██████████| 91/91 [10:26<00:00,  6.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.2788\n",
      "\n",
      "--- Époque SPSA 4/10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPSA Batches: 100%|██████████| 91/91 [11:00<00:00,  7.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.2857\n",
      "\n",
      "--- Époque SPSA 5/10 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPSA Batches:  77%|███████▋  | 70/91 [34:30<1:23:24, 238.30s/it]"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    config = HybridConfig()\n",
    "    config.optimizer = \"spsa\" \n",
    "    \n",
    "    print(\"Data loading and preprocessing...\")\n",
    "    X_train, y_train, X_val, y_val = load_and_preprocess_data(config)\n",
    "    \n",
    "    print(\"Initialisation of UDENN model (PyTorch)...\")\n",
    "    trainer = UDENNTrainer(X_train, y_train, X_val, y_val, config)\n",
    "    \n",
    "    BS_params, omega_params, loss_history, val_acc_history, confusion_mtx = trainer.train()\n",
    "    \n",
    "    plt.plot(loss_history, label='Batch Loss', color='blue')\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Number of batches\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.title(f\"Loss history ({config.optimizer.upper()})\")\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc80886",
   "metadata": {},
   "source": [
    "## 3: Third method - Lancelot/UDENN\n",
    "\n",
    "**Strengths:**\n",
    "- Leverages unitary dilation for theoretically grounded feature expansion from image content\n",
    "- Explicit post-selection ensures quantum validity (outputs lie in correct photonic sector)\n",
    "- Alternating optimization allows independent tuning of classical and quantum components\n",
    "- SPSA optimizer reduces computational cost of photonic simulation gradients\n",
    "- Samples down from full Hilbert space, improving numerical stability\n",
    "\n",
    "**Performance Characteristics:**\n",
    "- Achieves accuracy through learned unitary transformations of image structure\n",
    "- Adaptive photon number based on image resolution ((14+1)//2 = 7 photons)\n",
    "- Mean photon occupation provides dense, low-dimensional feature representation\n",
    "- Validation accuracy improves consistently across quantum epochs\n",
    "\n",
    "**Weaknesses:**\n",
    "- Unitary dilations reject many samples; only samples with valid unitary representations are trainable\n",
    "- Significant data loss: filtering may discard 20-30% of training data\n",
    "- Computational cost is extremely high: photonic simulation for each sample and gradient estimate\n",
    "- SPSA gradient estimation introduces noise; convergence is slower than gradient-based methods\n",
    "- Limited scalability: Hilbert-space dimension grows exponentially with modes and photons\n",
    "- Requires careful tuning of optimizer hyperparameters (step sizes a_k, c_k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c303437e",
   "metadata": {},
   "source": [
    "# Conclusion and Comparative Analysis of the Three Models\n",
    "\n",
    "## Comparative Performance\n",
    "\n",
    "| Aspect | Photonic QNN | GLASE | Lancelot (UDENN) |\n",
    "|--------|-------------|-------|------------------|\n",
    "| **Input Dimension** | Full (784 or reduced) | 32 (PCA-reduced) | 196 (14×14 pooled) |\n",
    "| **Architecture Complexity** | Low | Medium | High |\n",
    "| **Quantum Circuit Depth** | 1 encoding layer + interferometer | 1 encoding + readout interferometer | Multiple unitary + interferometer layers |\n",
    "| **Training Stability** | Moderate | High | Variable (depends on post-selection) |\n",
    "| **Inference Speed** | Fast | Fast | Very Slow (sampling-based) |\n",
    "| **Interpretability** | High | Medium | Low |\n",
    "| **Data Efficiency** | Low | High (PCA regularization) | Very Low (unitary filtering) |\n",
    "| **Scalability** | Limited by modes | Moderate (PCA bottleneck) | Poor (exponential Hilbert space) |\n",
    "\n",
    "## Key Findings\n",
    "\n",
    "1. **Dimensionality Reduction is Critical**: GLASE's PCA preprocessing provides substantial benefits in convergence stability and generalization, despite discarding high-frequency features. This suggests that for photonic quantum classifiers, working in reduced feature spaces is more efficient than attempting to encode all raw pixel information.\n",
    "\n",
    "2. **Scale Layer Importance**: All methods benefit from learnable scaling layers, indicating that mapping classical features to appropriate quantum encoding ranges is essential for training success.\n",
    "\n",
    "3. **Quantum Advantage vs. Computational Cost Trade-off**: Lancelot's sophisticated unitary dilation achieves theoretical elegance but at extreme computational cost due to:\n",
    "   - Sample-by-sample post-selection filtering\n",
    "   - Photon simulation overhead for each gradient estimate\n",
    "   - Limited effective training set size\n",
    "\n",
    "4. **Hybrid Approaches Outperform Pure Quantum**: Methods incorporating classical preprocessing (GLASE) show better generalization than pure quantum encodings, suggesting hybrid approaches are practical for current hardware constraints.\n",
    "\n",
    "## Limitations and Challenges\n",
    "\n",
    "### All Models\n",
    "- **MNIST Limitation**: MNIST is too simple for quantum advantage; all classical baselines (CNNs, SVMs) significantly outperform quantum methods\n",
    "- **Hardware Limitations**: Simulated photonic circuits cannot demonstrate true quantum advantage; real Perceval hardware would introduce noise and imperfections\n",
    "- **Scalability**: None scale well to ImageNet-scale datasets; the quantum circuits have fixed mode counts\n",
    "- **Gradient Estimation**: For GLASE and Photonic QNN, gradient computation through quantum circuits remains expensive even with simulation\n",
    "\n",
    "### Photonic QNN\n",
    "- **Curse of Dimensionality**: Direct encoding of high-dimensional inputs is impractical without preprocessing\n",
    "- **Scale Layer Overfitting**: Learnable scales may overfit on small training sets\n",
    "- **Limited Interpretability of Quantum Features**: Difficult to understand what quantum transformations learn\n",
    "\n",
    "### GLASE\n",
    "- **PCA Information Loss**: While preserving variance, PCA may discard class-discriminative information in tail components\n",
    "- **Double Preprocessing**: Both PCA and scale layer add hyperparameters requiring tuning\n",
    "- **Theoretical Justification**: No clear quantum advantage over classical PCA + linear classifier baselines\n",
    "- **Generalization**: PCA fit on training data may not transfer well to new domains\n",
    "\n",
    "### Lancelot/UDENN\n",
    "- **Unitary Filtering Bias**: Rejecting non-unitary samples introduces distribution shift between training and potential deployment data\n",
    "- **Sample Efficiency**: Data loss from unitary rejection makes this method impractical for small datasets\n",
    "- **Computational Infeasibility**: Photonic simulation scales as $2^{O(m)}$ where $m$ is number of modes; current implementation is toy-sized (14×14 images)\n",
    "- **Optimizer Sensitivity**: SPSA requires careful tuning of decay schedules; poor choices lead to divergence\n",
    "- **No Convergence Guarantees**: Noisy gradient estimates from post-selected sampling violate standard optimization assumptions\n",
    "\n",
    "## Recommendations\n",
    "\n",
    "1. **For Research**: GLASE offers the best balance of practicality and hybrid quantum-classical principles; use this as baseline\n",
    "2. **For Production**: Train classical CNN baselines; quantum approaches currently show no advantage for this scale\n",
    "3. **For Future Work**:\n",
    "   - Investigate kernel methods (quantum kernel SVM) which may offer theoretical advantages\n",
    "   - Reduce unitary filtering overhead in Lancelot by preprocessing images to satisfy unitary constraints\n",
    "   - Use variational quantum algorithms (VQA) with fewer samples but better gradient estimates\n",
    "   - Deploy on real Perceval hardware to understand noise effects realistically\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "These three methods represent different design philosophies:\n",
    "- **Photonic QNN** pursues simplicity but lacks preprocessing sophistication\n",
    "- **GLASE** balances classical efficiency with quantum processing—most practical current approach\n",
    "- **Lancelot** pursues theoretical rigor (unitarity) at the cost of extreme computational overhead\n",
    "\n",
    "For MNIST classification, **classical methods remain superior**, confirming that quantum advantage for image classification is still aspirational on current hardware. However, GLASE provides a template for hybrid quantum-classical machine learning that could transfer to domains where quantum sampling naturally provides advantage (e.g., molecular simulation, optimization problems)."
   ]
  }
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