{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1aad8daa8cdd20",
   "metadata": {},
   "source": [
    "# Quantum-enhanced random kitchen sinks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f644861594f155",
   "metadata": {},
   "source": [
    "The goal of this notebook is to present the third algorithm presented in [Fock State-enhanced Expressivity of Quantum Machine Learning Models](https://arxiv.org/abs/2107.05224). It is an implementation of a random kitchen sinks algorithm that uses a photonic quantum circuit as part of its routine.\n",
    "\n",
    "Let's start by explaining what is the random kitchen sinks algorithm. Basically for each datapoint features x, we will use the random Fourier features z(x) defined such as:\n",
    "\n",
    "$$\n",
    "z(x) = \\frac{1}{\\sqrt R} \\begin{pmatrix}\n",
    "z_{w_1}(x) \\\\\n",
    "z_{w_2}(x) \\\\\n",
    "... \\\\\n",
    "z_{w_R}(x)\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "where each $z_{w_r}(x)$ is a randomized cosine function:\n",
    "$$\n",
    "z_{w_r}(x) = \\sqrt2 \\cos (\\gamma [w_r \\cdot x + b_r])\n",
    "$$\n",
    "where x is the D-dimensional input data, $w_r$ are D-dimensional random vectors sampled from a spherical Gaussian and $b_r$ are random scalars sampled from a uniform distribution:\n",
    "$$\n",
    "w_r \\sim N_D(0, I) \\quad b_r \\sim Uniform(0, 2\\pi)\n",
    "$$\n",
    "and $\\gamma$ is a hyperparameter that will control the standard deviation of the Gaussian approximated afterwards. Once we have the random Fourier features for every point, we can approximate the Gaussian kernel for every pair of points by using a salar product:\n",
    "$$\n",
    "z(x) \\cdot z(x') \\approx k(x, x') = e^{-\\frac{\\gamma^2}{2}(x-x')^2}\n",
    "$$\n",
    "That is the random kitchen sinks approach.\n",
    "\n",
    "## How will the photonic quantum circuit be used in this whole process ?\n",
    "There are two different methods to use the quantum circuit. One involves training and the other does not. For both, the randomized input encoding for a data point x, i.e. $x_{r} = \\gamma(w_r \\cdot x + b_r)$ will be encoded in the quantum circuit.\n",
    "\n",
    "- Method 1: The model will undergo optimization for the fitting task on the function $f(x_r) = \\sqrt2 \\cos (x_r)$. That way, the optimized hybrid model approximates $z_{w_r}(x_r)$ for each $x_r$.\n",
    "\n",
    "- Method 2: The model is instantiated and directly used to approximate $z_{w_r}(x_r)$ without training.\n",
    "\n",
    "From there, all that is left is to build $z(x)$ and approximate the Gaussian kernel with $z(x) \\cdot z(x') \\approx k(x, x')$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecce09be4899e558",
   "metadata": {},
   "source": [
    "## 0. Imports and prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "99aeea9036ea7e2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:52.084981500Z",
     "start_time": "2025-11-10T13:45:51.927222300Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import os\n",
    "\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import perceval as pcvl\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from tqdm import tqdm\n",
    "\n",
    "from merlin import QuantumLayer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1174f9283fe7949f",
   "metadata": {},
   "source": [
    "We will need a class to keep track of the hyperparameters for this experiment. There are many of them and they are all explained in the README.md file present in the q_rand_kitchen_sinks folder except for the **decision_boundary_output** which is only needed for this notebook.\n",
    "\n",
    "- **decision_boundary_output** : (str) --> ['show', 'save'] If 'show', the decision boundary is simply shown as cell output. If 'save', then a directory './results/' is created if not already present, and the figure is saved locally in it. This last feature is useful to merge all the different decision boundaries together in one image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e8b5830a1667de7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:52.459176600Z",
     "start_time": "2025-11-10T13:45:52.126633600Z"
    }
   },
   "outputs": [],
   "source": [
    "class Hyperparameters:\n",
    "    def __init__(\n",
    "        self,\n",
    "        random_state=42,\n",
    "        scaling=\"MinMax\",\n",
    "        num_photon=10,\n",
    "        circuit=\"mzi\",\n",
    "        learning_rate=0.001,\n",
    "        c=5,\n",
    "        r=1,\n",
    "        gamma=1,\n",
    "        train_hybrid_model=True,\n",
    "        pre_encoding_scaling=1.0,\n",
    "        z_q_matrix_scaling=10,\n",
    "        hybrid_model_data=\"Default\",\n",
    "        visu_losses=True,\n",
    "        decision_boundary_output=\"show\",\n",
    "    ):\n",
    "        self.random_state = random_state\n",
    "        self.scaling = scaling\n",
    "        self.num_photon = num_photon\n",
    "        self.circuit = circuit\n",
    "        self.learning_rate = learning_rate\n",
    "        self.C = c\n",
    "        self.r = r\n",
    "        self.gamma = gamma\n",
    "        self.train_hybrid_model = train_hybrid_model\n",
    "        self.pre_encoding_scaling = pre_encoding_scaling\n",
    "        self.z_q_matrix_scaling = z_q_matrix_scaling\n",
    "        self.set_z_q_matrix_scaling_value()\n",
    "        self.hybrid_model_data = hybrid_model_data\n",
    "        self.visu_losses = visu_losses\n",
    "        self.decision_boundary_output = decision_boundary_output\n",
    "\n",
    "        self.w = None\n",
    "        self.b = None\n",
    "\n",
    "    def set_z_q_matrix_scaling_value(self):\n",
    "        if isinstance(self.z_q_matrix_scaling, str):\n",
    "            if self.z_q_matrix_scaling == \"1/sqrt(R)\":\n",
    "                self.z_q_matrix_scaling_value = torch.tensor(1.0 / np.sqrt(self.r))\n",
    "            elif self.z_q_matrix_scaling == \"sqrt(R)\":\n",
    "                self.z_q_matrix_scaling_value = torch.tensor(np.sqrt(self.r))\n",
    "            else:\n",
    "                raise ValueError('z_q_matrix_scaling must be \"1/sqrt(R)\" or \"sqrt(R)\"')\n",
    "        else:\n",
    "            self.z_q_matrix_scaling_value = torch.tensor(self.z_q_matrix_scaling)\n",
    "\n",
    "    def set_random(self, w, b):\n",
    "        \"\"\"\n",
    "        Set values for random weights and biases. That is to keep the same values for the quantum and classical\n",
    "        methods in order to fairly compare the two.\n",
    "        \"\"\"\n",
    "        self.w = w\n",
    "        self.b = b\n",
    "        return\n",
    "\n",
    "    def set_gamma(self, gamma):\n",
    "        self.gamma = gamma\n",
    "        return\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.r = r\n",
    "        self.set_z_q_matrix_scaling_value()\n",
    "        return\n",
    "\n",
    "\n",
    "base_args = Hyperparameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f7fa599036c74f",
   "metadata": {},
   "source": [
    "## 1. Get the data and define the target function for the hybrid model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce729d260bebf5e6",
   "metadata": {},
   "source": [
    "We will consider the moon dataset, by sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f7025aef462f8b54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:52.459176600Z",
     "start_time": "2025-11-10T13:45:52.411666Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_moon_dataset(random_state):\n",
    "    \"\"\"\n",
    "    Return moon dataset x and y.\n",
    "    x : [n_samples, 2]\n",
    "    y : [n_samples, ] of value 0 or 1\n",
    "    \"\"\"\n",
    "    x, y = make_moons(n_samples=200, noise=0.2, random_state=random_state)\n",
    "    return np.array(x), np.array(y)\n",
    "\n",
    "\n",
    "def scale_dataset(x_train, x_test, scaling=\"MinMax\"):\n",
    "    if scaling == \"Standard\":\n",
    "        scaler = StandardScaler()\n",
    "        x_train = scaler.fit_transform(x_train)\n",
    "        x_test = scaler.transform(x_test)\n",
    "    elif scaling == \"MinMax\":\n",
    "        scaler = MinMaxScaler()\n",
    "        x_train = scaler.fit_transform(x_train)\n",
    "        x_test = scaler.transform(x_test)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown scaling method: {scaling}\")\n",
    "    return x_train, x_test\n",
    "\n",
    "\n",
    "def split_train_test(x, y, random_state):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(\n",
    "        x, y, test_size=0.4, random_state=random_state\n",
    "    )\n",
    "    return x_train, x_test, y_train, y_test\n",
    "\n",
    "\n",
    "x, y = get_moon_dataset(base_args.random_state)\n",
    "x_train, x_test, y_train, y_test = split_train_test(x, y, base_args.random_state)\n",
    "x_train, x_test = scale_dataset(x_train, x_test, scaling=base_args.scaling)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d064c02f39d35d56",
   "metadata": {},
   "source": [
    "Let's visualize the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eca0bc1032d7c1ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:52.745119900Z",
     "start_time": "2025-11-10T13:45:52.411666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_dataset(x_train, x_test, y_train, y_test):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "\n",
    "    # Plot training data (circle marker 'o')\n",
    "    plt.scatter(\n",
    "        x_train[y_train == 0][:, 0],\n",
    "        x_train[y_train == 0][:, 1],\n",
    "        color=\"red\",\n",
    "        marker=\"o\",\n",
    "        label=\"Class 0 - Train\",\n",
    "        s=80,\n",
    "    )\n",
    "\n",
    "    # Plot test data (cross marker 'x')\n",
    "    plt.scatter(\n",
    "        x_test[y_test == 0][:, 0],\n",
    "        x_test[y_test == 0][:, 1],\n",
    "        color=\"red\",\n",
    "        marker=\"x\",\n",
    "        label=\"Class 0 - Test\",\n",
    "        s=80,\n",
    "    )\n",
    "\n",
    "    plt.scatter(\n",
    "        x_train[y_train == 1][:, 0],\n",
    "        x_train[y_train == 1][:, 1],\n",
    "        color=\"blue\",\n",
    "        marker=\"o\",\n",
    "        label=\"Class 1 - Train\",\n",
    "        s=80,\n",
    "    )\n",
    "\n",
    "    plt.scatter(\n",
    "        x_test[y_test == 1][:, 0],\n",
    "        x_test[y_test == 1][:, 1],\n",
    "        color=\"blue\",\n",
    "        marker=\"x\",\n",
    "        label=\"Class 1 - Test\",\n",
    "        s=80,\n",
    "    )\n",
    "\n",
    "    plt.xlabel(\"x1\")\n",
    "    plt.ylabel(\"x2\")\n",
    "    plt.title(\"Moons Dataset: Train and Test Split\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    return\n",
    "\n",
    "\n",
    "visualize_dataset(x_train, x_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7326c63d78217e",
   "metadata": {},
   "source": [
    "Let's define the target function for the hybrid model and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3f27964b6712204d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.041310600Z",
     "start_time": "2025-11-10T13:45:52.748766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_target_function(x_r_i_s):\n",
    "    return np.sqrt(2) * np.cos(x_r_i_s)\n",
    "\n",
    "\n",
    "x = np.linspace(-1, 2 * np.pi + 1, 500)\n",
    "plt.plot(x, get_target_function(x))\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Target function: f(x) = sqrt(2) * cos(x)\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb78e24366119bb",
   "metadata": {},
   "source": [
    "## 2. Approximations and model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d141b4d4717e128",
   "metadata": {},
   "source": [
    "First off, let's define some functions useful for future approximations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f65611c13781e51c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.041310600Z",
     "start_time": "2025-11-10T13:45:52.833398800Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_random_w_b(r, random_state):\n",
    "    np.random.seed(random_state)\n",
    "    w = np.random.normal(size=(r, 2))\n",
    "    b = np.random.uniform(low=0.0, high=2.0 * np.pi, size=(r,))\n",
    "\n",
    "    return w, b\n",
    "\n",
    "\n",
    "def get_x_r_i_s(x_s, w, b, r, gamma):\n",
    "    \"\"\"\n",
    "    Given input data points x_s, of size [num_points, num_features],\n",
    "    Return the x_{r, i}_s of size [num_points, r] such that\n",
    "    x_{r, i} = gamma * (w_r * x_i + b_r)\n",
    "    \"\"\"\n",
    "    num_points, num_features = x_s.shape\n",
    "\n",
    "    x_r_i_s = gamma * (np.matmul(x_s, w.T) + np.tile(b, (num_points, 1)))\n",
    "    assert x_r_i_s.shape == (num_points, r), f\"Wrong shape for x_r_i_s: {x_r_i_s.shape}\"\n",
    "\n",
    "    return x_r_i_s\n",
    "\n",
    "\n",
    "def get_z_s_classically(x_r_i_s):\n",
    "    n, r = x_r_i_s.shape\n",
    "    z_s = np.sqrt(2) * np.cos(x_r_i_s)\n",
    "    z_s = z_s / np.sqrt(r)\n",
    "    return z_s\n",
    "\n",
    "\n",
    "def get_approx_kernel_train(z_s):\n",
    "    result_matrix = np.matmul(z_s, z_s.T)\n",
    "    assert result_matrix.shape == (z_s.shape[0], z_s.shape[0]), (\n",
    "        f\"Wrong shape for result_matrix: {result_matrix.shape}\"\n",
    "    )\n",
    "    return result_matrix\n",
    "\n",
    "\n",
    "def get_approx_kernel_predict(z_s_test, z_s_train):\n",
    "    result_matrix = np.matmul(z_s_test, z_s_train.T)\n",
    "    assert result_matrix.shape == (z_s_test.shape[0], z_s_train.shape[0]), (\n",
    "        f\"Wrong shape for result_matrix: {result_matrix.shape}\"\n",
    "    )\n",
    "    return result_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b038907651a441",
   "metadata": {},
   "source": [
    "Next we define everything that is related to the hybrid model. That includes MerLin's QuantumLayer which allows backpropagation for optimization with gradient descent. It was also designed to be used with PyTorch so this facilitates its usage immensely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e26489afd2c62593",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.041310600Z",
     "start_time": "2025-11-10T13:45:52.846048300Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_mzi():\n",
    "    circuit = pcvl.Circuit(2)\n",
    "    circuit.add(0, pcvl.BS())\n",
    "    circuit.add(0, pcvl.PS(pcvl.P(\"data\")))\n",
    "    circuit.add(0, pcvl.BS())\n",
    "\n",
    "    return circuit\n",
    "\n",
    "\n",
    "def get_general():\n",
    "    left_side = pcvl.GenericInterferometer(\n",
    "        2,\n",
    "        lambda i: pcvl.BS()\n",
    "        // pcvl.PS(phi=pcvl.P(f\"theta_psl1{i}\"))\n",
    "        // pcvl.BS()\n",
    "        // pcvl.PS(phi=pcvl.P(f\"theta_{i}\")),\n",
    "        shape=pcvl.InterferometerShape.RECTANGLE,\n",
    "    )\n",
    "    right_side = pcvl.GenericInterferometer(\n",
    "        2,\n",
    "        lambda i: pcvl.BS()\n",
    "        // pcvl.PS(phi=pcvl.P(f\"theta_psr1{i}\"))\n",
    "        // pcvl.BS()\n",
    "        // pcvl.PS(phi=pcvl.P(f\"theta_psr2{i}\")),\n",
    "        shape=pcvl.InterferometerShape.RECTANGLE,\n",
    "    )\n",
    "\n",
    "    circuit = pcvl.Circuit(2)\n",
    "    circuit.add(0, left_side)\n",
    "    circuit.add(0, pcvl.PS(pcvl.P(\"data\")))\n",
    "    circuit.add(0, right_side)\n",
    "    return circuit\n",
    "\n",
    "\n",
    "def get_circuit(args):\n",
    "    if args.circuit == \"mzi\":\n",
    "        return get_mzi(), []\n",
    "    elif args.circuit == \"general\":\n",
    "        return get_general(), [\"theta\"]\n",
    "    else:\n",
    "        raise ValueError(f\"Wrong circuit type: {args.circuit}\")\n",
    "\n",
    "\n",
    "def save_circuit_locally(circuit, path):\n",
    "    pcvl.pdisplay_to_file(circuit, path)\n",
    "    return\n",
    "\n",
    "\n",
    "def get_input_fock_state(num_photons):\n",
    "    if num_photons % 2 == 0:\n",
    "        return [int(num_photons / 2), int(num_photons / 2)]\n",
    "    else:\n",
    "        return [int(1 + (num_photons // 2)), int(num_photons // 2)]\n",
    "\n",
    "\n",
    "def get_q_model(args):\n",
    "    torch.manual_seed(args.random_state)\n",
    "\n",
    "    input_fock_state = get_input_fock_state(int(args.num_photon))\n",
    "    circuit, trainable_params = get_circuit(args)\n",
    "\n",
    "    quantum_core = QuantumLayer(\n",
    "        input_size=1,\n",
    "        circuit=circuit,\n",
    "        trainable_parameters=trainable_params,\n",
    "        input_parameters=[\"data\"],\n",
    "        input_state=input_fock_state,\n",
    "        no_bunching=False,  # Forced to use no_bunching = False for their experiment (2 modes, 10 photons)\n",
    "    )\n",
    "\n",
    "    return nn.Sequential(quantum_core, nn.Linear(quantum_core.output_size, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9382c7aeed4bd962",
   "metadata": {},
   "source": [
    "## 3. Training function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb919ae9cce3cb7",
   "metadata": {},
   "source": [
    "The training here is separated in two blocks: first, we must train our hybrid model to approximate $f(x) = \\sqrt 2 \\cos (x)$ (or we can skip that part), then we must train a classical model that utilizes our approximated kernels. For that last part, we will use sklearn's SVC which allows us to use our precomputed kernel matrices.\n",
    "\n",
    "### 3.1 Hybrid model\n",
    "The optimization for the quantum model is as easy as for a classical PyTorch model thanks to MerLin. The structure of the training loop remains the same ! Note that the loss function used for this first training block is the Mean Squared Error (MSE) loss which is useful for regression tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9b1f50b2cfc29486",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.050054Z",
     "start_time": "2025-11-10T13:45:52.892007300Z"
    }
   },
   "outputs": [],
   "source": [
    "def training_q_model(x_train, x_test, args):\n",
    "    # Transform data\n",
    "    x_r_i_s_train_origin = get_x_r_i_s(x_train, args.w, args.b, args.r, args.gamma)\n",
    "    x_r_i_s_test_origin = get_x_r_i_s(x_test, args.w, args.b, args.r, args.gamma)\n",
    "\n",
    "    target_fit_train_origin = get_target_function(x_r_i_s_train_origin)\n",
    "    target_fit_test_origin = get_target_function(x_r_i_s_test_origin)\n",
    "\n",
    "    if args.hybrid_model_data == \"Default\":\n",
    "        # 'Default' means we train the hybrid model on data from the moon dataset\n",
    "        x_r_i_s_train = x_r_i_s_train_origin\n",
    "        x_r_i_s_test = x_r_i_s_test_origin\n",
    "    elif args.hybrid_model_data == \"Generated\":\n",
    "        # 'Generated' means we train the hybrid model on more generated data from the same interval [min, max] as the original data from the moon dataset\n",
    "        train_mins = x_r_i_s_train_origin.min(axis=0)  # shape (r,)\n",
    "        train_maxs = x_r_i_s_train_origin.max(axis=0)  # shape (r,)\n",
    "        test_mins = x_r_i_s_test_origin.min(axis=0)  # shape (r,)\n",
    "        test_maxs = x_r_i_s_test_origin.max(axis=0)  # shape (r,)\n",
    "\n",
    "        x_r_i_s_train = np.linspace(train_mins, train_maxs, 540, axis=0)\n",
    "        x_r_i_s_test = np.linspace(test_mins, test_maxs, 100, axis=0)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown hybrid_model_data: {args.hybrid_model_data}\")\n",
    "\n",
    "    target_fit_train = get_target_function(x_r_i_s_train)\n",
    "    target_fit_test = get_target_function(x_r_i_s_test)\n",
    "\n",
    "    assert x_r_i_s_train.shape == target_fit_train.shape, (\n",
    "        f\"Target fit shape is wrong for x_r_i_s_train: {target_fit_train.shape}\"\n",
    "    )\n",
    "    assert x_r_i_s_train.shape == target_fit_train.shape, (\n",
    "        f\"Target fit shape is wrong for x_r_i_s_test: {target_fit_test.shape}\"\n",
    "    )\n",
    "\n",
    "    q_model = get_q_model(args)\n",
    "    print(q_model)\n",
    "    # Count only trainable parameters\n",
    "    trainable_params = sum(p.numel() for p in q_model.parameters() if p.requires_grad)\n",
    "    print(f\"Trainable parameters: {trainable_params}\")\n",
    "\n",
    "    dataset = TensorDataset(torch.Tensor(x_r_i_s_train), torch.Tensor(target_fit_train))\n",
    "    dataloader = DataLoader(dataset, batch_size=30, shuffle=True)\n",
    "\n",
    "    loss_f = torch.nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(\n",
    "        q_model.parameters(),\n",
    "        lr=args.learning_rate,\n",
    "        betas=(0.99, 0.9999),\n",
    "        weight_decay=0.0002,\n",
    "    )\n",
    "    epoch_bar = tqdm(range(200), desc=\"Training Epochs\")\n",
    "    best_previous_model = None\n",
    "    best_previous_test_mse = np.inf\n",
    "    losses = {\"Train\": [], \"Test\": []}\n",
    "\n",
    "    for _ in epoch_bar:\n",
    "        q_model.train()\n",
    "        total_loss = 0\n",
    "\n",
    "        for x_batch, y_batch in dataloader:\n",
    "            optimizer.zero_grad()\n",
    "            # Reformat input\n",
    "            x_batch = x_batch.view(30 * args.r, -1) * torch.tensor(\n",
    "                args.pre_encoding_scaling\n",
    "            )\n",
    "            logits = q_model(x_batch)\n",
    "            # Reformat output\n",
    "            logits = logits.view(30, args.r)\n",
    "            loss = loss_f(logits, y_batch)\n",
    "            loss.backward()\n",
    "\n",
    "            optimizer.step()\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        avg_loss = total_loss / len(dataloader)\n",
    "        losses[\"Train\"].append(avg_loss)\n",
    "        epoch_bar.set_postfix({\"Train Loss\": avg_loss})\n",
    "\n",
    "        # Eval\n",
    "        q_model.eval()\n",
    "        eval_input = torch.Tensor(x_r_i_s_test).view(\n",
    "            len(x_r_i_s_test) * args.r, -1\n",
    "        ) * torch.tensor(args.pre_encoding_scaling)\n",
    "        test_logits = q_model(eval_input)\n",
    "        # Reformat\n",
    "        test_logits = test_logits.view(len(x_r_i_s_test), args.r)\n",
    "        test_loss = loss_f(test_logits, torch.Tensor(target_fit_test))\n",
    "        epoch_bar.set_postfix({\"Test Loss\": test_loss})\n",
    "        losses[\"Test\"].append(test_loss.detach().numpy())\n",
    "\n",
    "        if best_previous_model is None:\n",
    "            best_previous_model = q_model\n",
    "            best_previous_test_mse = test_loss\n",
    "        elif test_loss < best_previous_test_mse:\n",
    "            best_previous_model = q_model\n",
    "            best_previous_test_mse = test_loss\n",
    "\n",
    "    best_test_mse = np.min(losses[\"Test\"])\n",
    "    best_test_mse_epoch = np.argmin(losses[\"Test\"])\n",
    "    print(f\"Best test MSE: {best_test_mse:.3f} at epoch {best_test_mse_epoch}\")\n",
    "\n",
    "    # We will keep and use the version of the q_model with the best test MSE\n",
    "    q_model = best_previous_model\n",
    "\n",
    "    return (\n",
    "        q_model,\n",
    "        losses,\n",
    "        x_r_i_s_train_origin,\n",
    "        x_r_i_s_test_origin,\n",
    "        target_fit_train_origin,\n",
    "        target_fit_test_origin,\n",
    "    )\n",
    "\n",
    "\n",
    "def visualize_losses(losses):\n",
    "    \"\"\"Plot training and test losses\"\"\"\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    epochs = range(1, len(losses[\"Train\"]) + 1)\n",
    "\n",
    "    plt.plot(epochs, losses[\"Train\"], label=\"Train Loss\", color=\"blue\")\n",
    "    plt.plot(epochs, losses[\"Test\"], label=\"Test Loss\", color=\"red\")\n",
    "\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.title(\"Training and Test Loss Over Epochs\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig('./results/loss_curve.png')  # To save locally\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84f0a6e9ddf130c",
   "metadata": {},
   "source": [
    "However, we also have the option to not train the hybrid model with the following function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "80de7d482a70bec9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.050054Z",
     "start_time": "2025-11-10T13:45:52.908124400Z"
    }
   },
   "outputs": [],
   "source": [
    "def no_train_q_model(x_train, x_test, args):\n",
    "    # Transform data\n",
    "    x_r_i_s_train = get_x_r_i_s(x_train, args.w, args.b, args.r, args.gamma)\n",
    "    x_r_i_s_test = get_x_r_i_s(x_test, args.w, args.b, args.r, args.gamma)\n",
    "\n",
    "    target_fit_train = get_target_function(x_r_i_s_train)\n",
    "    target_fit_test = get_target_function(x_r_i_s_test)\n",
    "\n",
    "    assert x_r_i_s_train.shape == target_fit_train.shape, (\n",
    "        f\"Target fit shape is wrong for x_r_i_s_train: {target_fit_train.shape}\"\n",
    "    )\n",
    "    assert x_r_i_s_train.shape == target_fit_train.shape, (\n",
    "        f\"Target fit shape is wrong for x_r_i_s_test: {target_fit_test.shape}\"\n",
    "    )\n",
    "\n",
    "    q_model = get_q_model(args)\n",
    "\n",
    "    return q_model, x_r_i_s_train, x_r_i_s_test, target_fit_train, target_fit_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525a87dab8eb44a2",
   "metadata": {},
   "source": [
    "### 3.2 Random kitchen sinks\n",
    "Next, we have the functions for the quantum-enhanced and classical random kitchen sinks algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "19ff8efa1e902ba4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.050054Z",
     "start_time": "2025-11-10T13:45:52.914766400Z"
    }
   },
   "outputs": [],
   "source": [
    "def q_rand_kitchen_sinks(x_train, x_test, args):\n",
    "    if args.train_hybrid_model:\n",
    "        (\n",
    "            q_model_opti,\n",
    "            losses,\n",
    "            x_r_i_s_train,\n",
    "            x_r_i_s_test,\n",
    "            target_fit_train,\n",
    "            target_fit_test,\n",
    "        ) = training_q_model(x_train, x_test, args)\n",
    "        if args.visu_losses:\n",
    "            visualize_losses(losses)\n",
    "    else:\n",
    "        q_model_opti, x_r_i_s_train, x_r_i_s_test, target_fit_train, target_fit_test = (\n",
    "            no_train_q_model(x_train, x_test, args)\n",
    "        )\n",
    "\n",
    "    q_model_opti.eval()\n",
    "    train_input = torch.Tensor(x_r_i_s_train).view(\n",
    "        len(x_r_i_s_train) * args.r, -1\n",
    "    ) * torch.tensor(args.pre_encoding_scaling)\n",
    "    test_input = torch.Tensor(x_r_i_s_test).view(\n",
    "        len(x_r_i_s_test) * args.r, -1\n",
    "    ) * torch.tensor(args.pre_encoding_scaling)\n",
    "    z_s_train = q_model_opti(train_input)\n",
    "    z_s_test = q_model_opti(test_input)\n",
    "\n",
    "    z_s_train = z_s_train.view(len(x_r_i_s_train), args.r)\n",
    "    z_s_test = z_s_test.view(len(x_r_i_s_test), args.r)\n",
    "\n",
    "    # In the paper, they multiply by 1/sqrt(R) but changing this value seems to give better results\n",
    "    z_s_train = z_s_train * args.z_q_matrix_scaling_value\n",
    "    z_s_test = z_s_test * args.z_q_matrix_scaling_value\n",
    "\n",
    "    kernel_matrix_training = get_approx_kernel_train(z_s_train.detach().numpy())\n",
    "    kernel_matrix_test = get_approx_kernel_predict(\n",
    "        z_s_test.detach().numpy(), z_s_train.detach().numpy()\n",
    "    )\n",
    "\n",
    "    return q_model_opti, kernel_matrix_training, kernel_matrix_test\n",
    "\n",
    "\n",
    "def classical_rand_kitchen_sinks(x_train, x_test, args):\n",
    "    # Transform data\n",
    "    x_r_i_s_train = get_x_r_i_s(x_train, args.w, args.b, args.r, args.gamma)\n",
    "    x_r_i_s_test = get_x_r_i_s(x_test, args.w, args.b, args.r, args.gamma)\n",
    "\n",
    "    z_s_train = get_z_s_classically(x_r_i_s_train)\n",
    "    z_s_test = get_z_s_classically(x_r_i_s_test)\n",
    "\n",
    "    kernel_matrix_training = get_approx_kernel_train(z_s_train)\n",
    "    kernel_matrix_test = get_approx_kernel_predict(z_s_test, z_s_train)\n",
    "\n",
    "    return kernel_matrix_training, kernel_matrix_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ce0e2001540649",
   "metadata": {},
   "source": [
    "Finally, we need to train the actual SVM and to visualize its decision boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3635d5af31a88083",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.050054Z",
     "start_time": "2025-11-10T13:45:52.956038400Z"
    }
   },
   "outputs": [],
   "source": [
    "def visu_decision_boundary(\n",
    "    svc, q_model_opti, x_train, x_test, y_train, y_test, acc, incorrect, args\n",
    "):\n",
    "    # Combine train and test for full visualization\n",
    "    x_all = np.vstack((x_train, x_test))\n",
    "\n",
    "    # Build a meshgrid over the 2D input space\n",
    "    h = 0.02  # mesh step size\n",
    "    x_min, x_max = x_all[:, 0].min() - 0.2, x_all[:, 0].max() + 0.2\n",
    "    y_min, y_max = x_all[:, 1].min() - 0.2, x_all[:, 1].max() + 0.2\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "    # Flatten grid to get (n_points, 2) shape\n",
    "    grid_points = np.c_[xx.ravel(), yy.ravel()]\n",
    "\n",
    "    if q_model_opti is None:  # Classically compute the random kitchen sinks\n",
    "        grid_r_i_s = get_x_r_i_s(grid_points, args.w, args.b, args.r, args.gamma)\n",
    "        x_r_i_s_train = get_x_r_i_s(x_train, args.w, args.b, args.r, args.gamma)\n",
    "\n",
    "        grid_z_s = get_z_s_classically(grid_r_i_s)\n",
    "        z_s_train = get_z_s_classically(x_r_i_s_train)\n",
    "\n",
    "        k_grid = get_approx_kernel_predict(grid_z_s, z_s_train)\n",
    "\n",
    "        figure_name = (\n",
    "            f\"classical_rand_kitchen_sinks_R_{args.r}_sigma_{1.0 / args.gamma}.png\"\n",
    "        )\n",
    "        figure_title = \"Decision boundary of SVC with classical Random Kitchen Sinks\"\n",
    "\n",
    "    else:  # Quantumly approximate the random kitchen sinks\n",
    "        grid_r_i_s = get_x_r_i_s(grid_points, args.w, args.b, args.r, args.gamma)\n",
    "        x_r_i_s_train = get_x_r_i_s(x_train, args.w, args.b, args.r, args.gamma)\n",
    "\n",
    "        grid_input = (\n",
    "            torch.Tensor(grid_r_i_s).view(len(grid_r_i_s) * args.r, -1)\n",
    "            * args.pre_encoding_scaling\n",
    "        )\n",
    "        train_input = (\n",
    "            torch.Tensor(x_r_i_s_train).view(len(x_r_i_s_train) * args.r, -1)\n",
    "            * args.pre_encoding_scaling\n",
    "        )\n",
    "\n",
    "        grid_z_s = q_model_opti(grid_input)\n",
    "        z_s_train = q_model_opti(train_input)\n",
    "\n",
    "        grid_z_s = grid_z_s.view(len(grid_r_i_s), args.r)\n",
    "        z_s_train = z_s_train.view(len(x_r_i_s_train), args.r)\n",
    "\n",
    "        # In the paper, their multiply by 1/sqrt(R)\n",
    "        grid_z_s = grid_z_s * args.z_q_matrix_scaling_value\n",
    "        z_s_train = z_s_train * args.z_q_matrix_scaling_value\n",
    "\n",
    "        k_grid = get_approx_kernel_predict(\n",
    "            grid_z_s.detach().numpy(), z_s_train.detach().numpy()\n",
    "        )\n",
    "\n",
    "        figure_name = f\"q_rand_kitchen_sinks_R_{args.r}_sigma_{1.0 / args.gamma}.png\"\n",
    "        figure_title = (\n",
    "            \"Decision boundary of SVC with quantum approximated Random Kitchen Sinks\"\n",
    "        )\n",
    "\n",
    "    # Predict on the kernelized grid\n",
    "    z = svc.decision_function(k_grid)\n",
    "    z = z.reshape(xx.shape)\n",
    "\n",
    "    # Plotting\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    cmap_light = ListedColormap([\"#FFAAAA\", \"#AAAAFF\"])\n",
    "\n",
    "    # Decision boundary\n",
    "    plt.contourf(xx, yy, z > 0, cmap=cmap_light, alpha=0.6)\n",
    "\n",
    "    # Plot data points\n",
    "    plt.scatter(\n",
    "        x_train[y_train == 0][:, 0],\n",
    "        x_train[y_train == 0][:, 1],\n",
    "        color=\"red\",\n",
    "        label=\"Class 0 - Train\",\n",
    "        marker=\"o\",\n",
    "    )\n",
    "    plt.scatter(\n",
    "        x_test[y_test == 0][:, 0],\n",
    "        x_test[y_test == 0][:, 1],\n",
    "        color=\"red\",\n",
    "        label=\"Class 0 - Test\",\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    plt.scatter(\n",
    "        x_train[y_train == 1][:, 0],\n",
    "        x_train[y_train == 1][:, 1],\n",
    "        color=\"blue\",\n",
    "        label=\"Class 1 - Train\",\n",
    "        marker=\"o\",\n",
    "    )\n",
    "    plt.scatter(\n",
    "        x_test[y_test == 1][:, 0],\n",
    "        x_test[y_test == 1][:, 1],\n",
    "        color=\"blue\",\n",
    "        label=\"Class 1 - Test\",\n",
    "        marker=\"x\",\n",
    "    )\n",
    "\n",
    "    plt.scatter(\n",
    "        x_test[incorrect][:, 0],\n",
    "        x_test[incorrect][:, 1],\n",
    "        color=\"black\",\n",
    "        label=\"Incorrectly predicted\",\n",
    "        marker=\"o\",\n",
    "        s=10,\n",
    "    )\n",
    "\n",
    "    plt.text(\n",
    "        0.05,\n",
    "        0.95,\n",
    "        f\"{acc:.3}\",\n",
    "        transform=plt.gca().transAxes,\n",
    "        fontsize=40,\n",
    "        fontweight=\"bold\",\n",
    "        verticalalignment=\"top\",\n",
    "    )\n",
    "\n",
    "    if args.gamma == 1:\n",
    "        s = f\"R = {args.r}\\n$\\\\sigma = 1$\"\n",
    "    else:\n",
    "        s = f\"R = {args.r}\\n$\\\\sigma = 1 / {args.gamma}$\"\n",
    "    plt.text(\n",
    "        0.05,\n",
    "        0.05,\n",
    "        s,\n",
    "        transform=plt.gca().transAxes,\n",
    "        fontsize=20,\n",
    "        verticalalignment=\"bottom\",\n",
    "    )\n",
    "\n",
    "    plt.title(figure_title)\n",
    "    plt.xlabel(\"Feature 1\")\n",
    "    plt.ylabel(\"Feature 2\")\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    if args.decision_boundary_output == \"show\":\n",
    "        plt.show()\n",
    "    elif args.decision_boundary_output == \"save\":\n",
    "        os.makedirs(\"results\", exist_ok=True)\n",
    "        plt.savefig(f\"./results/{figure_name}\")\n",
    "\n",
    "    plt.close()\n",
    "    return\n",
    "\n",
    "\n",
    "def train_svm(\n",
    "    kernel_matrix_training,\n",
    "    kernel_matrix_test,\n",
    "    q_model_opti,\n",
    "    x_train,\n",
    "    x_test,\n",
    "    y_train,\n",
    "    y_test,\n",
    "    args,\n",
    "):\n",
    "    svc = SVC(C=args.C, kernel=\"precomputed\", random_state=args.random_state)\n",
    "    svc.fit(kernel_matrix_training, y_train)\n",
    "    preds = svc.predict(kernel_matrix_test)\n",
    "    acc = accuracy_score(y_test, preds)\n",
    "    incorrect = y_test != preds\n",
    "\n",
    "    visu_decision_boundary(\n",
    "        svc, q_model_opti, x_train, x_test, y_train, y_test, acc, incorrect, args\n",
    "    )\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e685a334447f4a1e",
   "metadata": {},
   "source": [
    "## 4. Running the algorithm\n",
    "\n",
    "Let's start with a single run of the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cda4dff1725b0c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:53.050054Z",
     "start_time": "2025-11-10T13:45:52.964461300Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_single_gamma_r(x_train, x_test, y_train, y_test, args):\n",
    "    # Get random features w and b for both methods\n",
    "    w, b = get_random_w_b(args.r, args.random_state)\n",
    "    args.set_random(w, b)\n",
    "\n",
    "    q_model_opti, q_kernel_matrix_train, q_kernel_matrix_test = q_rand_kitchen_sinks(\n",
    "        x_train, x_test, args\n",
    "    )\n",
    "    q_acc = train_svm(\n",
    "        q_kernel_matrix_train,\n",
    "        q_kernel_matrix_test,\n",
    "        q_model_opti,\n",
    "        x_train,\n",
    "        x_test,\n",
    "        y_train,\n",
    "        y_test,\n",
    "        args,\n",
    "    )\n",
    "    print(f\"q_rand_kitchen_sinks acc: {q_acc}\")\n",
    "\n",
    "    kernel_matrix_train, kernel_matrix_test = classical_rand_kitchen_sinks(\n",
    "        x_train, x_test, args\n",
    "    )\n",
    "    acc = train_svm(\n",
    "        kernel_matrix_train,\n",
    "        kernel_matrix_test,\n",
    "        None,\n",
    "        x_train,\n",
    "        x_test,\n",
    "        y_train,\n",
    "        y_test,\n",
    "        args,\n",
    "    )\n",
    "    print(f\"rand_kitchen_sinks acc: {acc}\")\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "30e86e77e91999a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:57.938915200Z",
     "start_time": "2025-11-10T13:45:52.980762700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 56.61it/s, Test Loss=tensor(0.0286, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.029 at epoch 120\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "q_rand_kitchen_sinks acc: 0.7125\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rand_kitchen_sinks acc: 0.7375\n"
     ]
    }
   ],
   "source": [
    "run_single_gamma_r(x_train, x_test, y_train, y_test, base_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7db265790a3aa0a",
   "metadata": {},
   "source": [
    "Now that we know everything works, let's run the algorithm for several values of R and $\\sigma$ (which is equal to $\\frac{1}{\\gamma}$). More specifically, we will run for $R \\in [1, 10, 100]$ and $\\gamma \\in [1, 2, ... , 10]$. But first, we have to define some new functions just as helpers so that everything is clear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "407a06b33f10e024",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:57.938915200Z",
     "start_time": "2025-11-10T13:45:57.508358100Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_data(args):\n",
    "    x, y = get_moon_dataset(args.random_state)\n",
    "    x_train, x_test, y_train, y_test = split_train_test(x, y, args.random_state)\n",
    "    x_train, x_test = scale_dataset(x_train, x_test, args.scaling)\n",
    "\n",
    "    return x_train, x_test, y_train, y_test\n",
    "\n",
    "\n",
    "def combine_saved_figures(q_approx=True):\n",
    "    r_values = [1, 10, 100]\n",
    "    gamma_values = list(range(1, 11))  # gamma from 1 to 10\n",
    "\n",
    "    with plt.style.context(\"default\"):\n",
    "        fig, axes = plt.subplots(len(r_values), len(gamma_values), figsize=(15, 4))\n",
    "\n",
    "        for i, r in enumerate(r_values):\n",
    "            for j, gamma in enumerate(gamma_values):\n",
    "                sigma = 1.0 / gamma\n",
    "                if q_approx:\n",
    "                    filename = f\"q_rand_kitchen_sinks_R_{r}_sigma_{sigma}.png\"\n",
    "                else:\n",
    "                    filename = f\"classical_rand_kitchen_sinks_R_{r}_sigma_{sigma}.png\"\n",
    "                filepath = os.path.join(\"./results/\", filename)\n",
    "\n",
    "                if os.path.exists(filepath):\n",
    "                    img = mpimg.imread(filepath)\n",
    "                    ax = axes[i, j]\n",
    "                    ax.imshow(img)\n",
    "                    ax.axis(\"off\")  # Hide axis ticks\n",
    "                    if i == 0:\n",
    "                        ax.set_title(f\"γ = {gamma}\", fontsize=10)\n",
    "                    if j == 0:\n",
    "                        ax.text(\n",
    "                            0,\n",
    "                            0.5,\n",
    "                            f\"R = {r}\",\n",
    "                            fontsize=10,\n",
    "                            va=\"center\",\n",
    "                            ha=\"right\",\n",
    "                            transform=ax.transAxes,\n",
    "                        )\n",
    "                else:\n",
    "                    print(f\"Warning: {filepath} not found.\")\n",
    "        if q_approx:\n",
    "            title = (\n",
    "                \"Decision Boundaries of SVC with Quantum-Enhanced Random Kitchen Sinks\"\n",
    "            )\n",
    "        else:\n",
    "            title = \"Decision Boundaries of SVC with Classical Random Kitchen Sinks\"\n",
    "\n",
    "        fig.suptitle(title, fontsize=20)\n",
    "        plt.tight_layout(rect=[0, 0, 1, 0.95])  # Make room for the title\n",
    "        plt.subplots_adjust(left=0.1, wspace=0.05, hspace=0.1)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8dcacce0e57099a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T13:45:57.938915200Z",
     "start_time": "2025-11-10T13:45:57.514875300Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_different_gamma_r(args, type=\"quantum\"):\n",
    "    rs = [1, 10, 100]\n",
    "    gammas = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "    args.visu_losses = False\n",
    "    args.decision_boundary_output = \"save\"\n",
    "\n",
    "    for r in rs:\n",
    "        args.set_r(r)\n",
    "        for gamma in gammas:\n",
    "            args.set_gamma(gamma)\n",
    "            print(\"\\n#############################\")\n",
    "            print(f\"For r={r}, gamma={gamma}\")\n",
    "\n",
    "            # Get data\n",
    "            x_train, x_test, y_train, y_test = get_data(args)\n",
    "\n",
    "            w, b = get_random_w_b(args.r, args.random_state)\n",
    "            args.set_random(w, b)\n",
    "\n",
    "            if type == \"quantum\":\n",
    "                q_model_opti, q_kernel_matrix_train, q_kernel_matrix_test = (\n",
    "                    q_rand_kitchen_sinks(x_train, x_test, args)\n",
    "                )\n",
    "                q_acc = train_svm(\n",
    "                    q_kernel_matrix_train,\n",
    "                    q_kernel_matrix_test,\n",
    "                    q_model_opti,\n",
    "                    x_train,\n",
    "                    x_test,\n",
    "                    y_train,\n",
    "                    y_test,\n",
    "                    args,\n",
    "                )\n",
    "                print(f\"q_rand_kitchen_sinks acc: {q_acc}\")\n",
    "\n",
    "            elif type == \"classical\":\n",
    "                kernel_matrix_train, kernel_matrix_test = classical_rand_kitchen_sinks(\n",
    "                    x_train, x_test, args\n",
    "                )\n",
    "                acc = train_svm(\n",
    "                    kernel_matrix_train,\n",
    "                    kernel_matrix_test,\n",
    "                    None,\n",
    "                    x_train,\n",
    "                    x_test,\n",
    "                    y_train,\n",
    "                    y_test,\n",
    "                    args,\n",
    "                )\n",
    "                print(f\"rand_kitchen_sinks acc: {acc}\")\n",
    "\n",
    "    if type == \"quantum\":\n",
    "        combine_saved_figures(True)\n",
    "    elif type == \"classical\":\n",
    "        combine_saved_figures(False)\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c1bc7b01ed96fc",
   "metadata": {},
   "source": [
    "Method 1: First, let's try with a **non-trainable MZI** for quantum circuit and a **trained linear layer** afterwards for the quantum enhanced random kitchen sinks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d174affa95290013",
   "metadata": {
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2025-11-10T13:45:57.530347500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#############################\n",
      "For r=1, gamma=1\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 57.85it/s, Test Loss=tensor(0.0286, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.029 at epoch 120\n",
      "q_rand_kitchen_sinks acc: 0.7125\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=2\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 55.90it/s, Test Loss=tensor(0.0525, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.053 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.6875\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=3\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 53.27it/s, Test Loss=tensor(0.2607, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.261 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.725\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=4\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:04<00:00, 40.10it/s, Test Loss=tensor(0.0455, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.046 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.7375\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=5\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 59.88it/s, Test Loss=tensor(0.5304, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.530 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.725\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=6\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:04<00:00, 45.92it/s, Test Loss=tensor(0.1405, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.140 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.725\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=7\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 62.09it/s, Test Loss=tensor(0.8670, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.867 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.6125\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=8\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:04<00:00, 48.93it/s, Test Loss=tensor(0.5209, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.521 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.4625\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=9\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:03<00:00, 58.60it/s, Test Loss=tensor(0.9582, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.948 at epoch 13\n",
      "q_rand_kitchen_sinks acc: 0.4625\n",
      "\n",
      "#############################\n",
      "For r=1, gamma=10\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:04<00:00, 47.37it/s, Test Loss=tensor(0.9875, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.987 at epoch 160\n",
      "q_rand_kitchen_sinks acc: 0.55\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=1\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 22.80it/s, Test Loss=tensor(1.2081, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 1.208 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.9125\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=2\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 22.71it/s, Test Loss=tensor(0.8136, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.814 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.95\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=3\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 23.90it/s, Test Loss=tensor(1.1109, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 1.108 at epoch 99\n",
      "q_rand_kitchen_sinks acc: 0.7\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=4\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 23.99it/s, Test Loss=tensor(0.9718, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.961 at epoch 83\n",
      "q_rand_kitchen_sinks acc: 0.8\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=5\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:09<00:00, 22.21it/s, Test Loss=tensor(0.9100, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.905 at epoch 60\n",
      "q_rand_kitchen_sinks acc: 0.6875\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=6\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 22.65it/s, Test Loss=tensor(0.9298, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.928 at epoch 135\n",
      "q_rand_kitchen_sinks acc: 0.675\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=7\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 22.64it/s, Test Loss=tensor(0.9709, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.969 at epoch 96\n",
      "q_rand_kitchen_sinks acc: 0.675\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=8\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:08<00:00, 23.12it/s, Test Loss=tensor(0.9373, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.937 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.5625\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=9\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:09<00:00, 21.71it/s, Test Loss=tensor(1.0282, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 1.025 at epoch 56\n",
      "q_rand_kitchen_sinks acc: 0.55\n",
      "\n",
      "#############################\n",
      "For r=10, gamma=10\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:09<00:00, 22.08it/s, Test Loss=tensor(0.9950, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.995 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.6125\n",
      "\n",
      "#############################\n",
      "For r=100, gamma=1\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs: 100%|██████████| 200/200 [00:26<00:00,  7.49it/s, Test Loss=tensor(0.9932, grad_fn=<MseLossBackward0>)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test MSE: 0.993 at epoch 199\n",
      "q_rand_kitchen_sinks acc: 0.9375\n",
      "\n",
      "#############################\n",
      "For r=100, gamma=2\n",
      "Sequential(\n",
      "  (0): QuantumLayer(\n",
      "    (_photon_loss_transform): PhotonLossTransform()\n",
      "    (_detector_transform): DetectorTransform()\n",
      "    (measurement_mapping): Probabilities()\n",
      "  )\n",
      "  (1): Linear(in_features=11, out_features=1, bias=True)\n",
      ")\n",
      "Trainable parameters: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Epochs:  26%|██▌       | 51/200 [00:07<00:20,  7.26it/s, Train Loss=0.978]                                    "
     ]
    }
   ],
   "source": [
    "run_different_gamma_r(base_args, type=\"quantum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc9b7adec8275fc",
   "metadata": {},
   "source": [
    "Method 2: We can compare with the results when not training the hybrid model and simply using it for its intial output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2433630a8768a84",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "base_args.train_hybrid_model = False\n",
    "run_different_gamma_r(base_args, type=\"quantum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6585ce4a4b9ccc8c",
   "metadata": {},
   "source": [
    "From this, we see that with the hyperparameters used, training the hybrid model does not make a big difference in the final decision boundary of the SVC.\n",
    "\n",
    "Finally, let us compare with the classical random kitchen sinks algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a883b14433498fb",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "run_different_gamma_r(base_args, type=\"classical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98fa8279b15ea343",
   "metadata": {},
   "source": [
    "One can see  that the results obtained using the quantum enhanced version of the algorithm yields better test classification accuracies with small $\\gamma$ than the ones obtained with the classical version. However, when $\\gamma$ gets big, it is the opposite. Moreover, for both versions of the random kitchen sinks, we observe that increasing R increases the model's performance too since that parameters controls the precision of the approximated Gaussian kernel.\\\\\n",
    "\n",
    "I now encourage you to experiment by modifying some hyperparameters. We chose to operate using 10 photons to replicate the results from [this paper](https://arxiv.org/abs/2107.05224) but we can obtain some interesting results using less photons:\n",
    "\n",
    "`base_args.num_photon = 2`\n",
    "\n",
    "Other than that, it also is interesting to use a more complex photonic circuit that is trainable:\n",
    "\n",
    "`base_args.circuit = 'general'`\n",
    "\n",
    "For better optimization of the hybrid model when training it, you can change the data used for optimization with:\n",
    "\n",
    "`base_args.hybrid_model_data = 'Generated'`\n",
    "\n",
    "This basically increases the amount of training data for the hybrid model instead of only using the moon dataset. It also spreads the data points used for training better on the domain between the minimum and the maximum values. With this setup for training, the quantum-enhanced random kitchen sinks algorithm is on par with the classical one."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}