{ "cells": [ { "cell_type": "markdown", "id": "91ae3118", "metadata": {}, "source": [ "# Quantum Optical Reservoir Computing (QORC) with MerLin\n", "\n", "This notebook is a first practical introduction to **quantum optical reservoir computing** inspired by:\n", "\n", "- [**Quantum optical reservoir computing powered by boson sampling**](https://opg.optica.org/opticaq/fulltext.cfm?uri=opticaq-3-3-238), Sakurai et al., (2025)\n", "- [**Photonic Quantum-Accelerated Machine Learning**](https://arxiv.org/abs/2512.08318), Rambach et al., (2025)\n", "\n", "Goals:\n", "\n", "1. Explain the reservoir structure and intuition.\n", "2. Build a first reservoir with `QuantumLayer.simple()`.\n", "3. Build a reservoir with the same fixed Haar-random unitary used **before and after** encoding.\n", "4. Evaluate it and compare against classical baselines." ] }, { "cell_type": "markdown", "id": "5ab38606", "metadata": {}, "source": [ "## 1) Reservoir computing intuition\n", "\n", "![Quantum optical reservoir scheme](../_static/img/reservoir_scheme.png)\n", "\n", "\n", "Reservoir computing separates the model into two parts:\n", "\n", "- **Reservoir (A)**: a fixed nonlinear feature map (not trained).\n", "- **Readout**: a small trainable classical model (often linear).\n", "\n", "If we look at the image above, we could have 3 ways to classify the images:\n", "- Option A: a fixed Boson Sampler applied to a specific input (PCA components or raw inputs, depending on the input size and the experiment). The output of this Boson Sampler is fed to a trainable Linear layer that maps it to the correct output (here, the classes)\n", "- Option B: the image is directly fed to the Linear layer and we operate a linear classification\n", "- Option C (and the case in the 2 papers mentioned): the input to the linear readout is the concatenation of the quantum features and the raw (flattened) input\n", "\n", "NOTE: we can imagine using another kind of readout (MLP, CNN). For the purpose of this tutorial and because it is used in the 2 papers, we use a linear readout.\n", "\n", "Why this is useful:\n", "\n", "- training is simpler and faster (few trainable parameters),\n", "- nonlinear dynamics in the reservoir can lift data into a richer feature space.\n", "\n", "In a photonic quantum setting, the reservoir is a fixed interferometer + encoding + measurement pipeline.\n", "\n", "Before diving in this tutorial, let's import all necessary tools:" ] }, { "cell_type": "code", "execution_count": 54, "id": "dd7559d0", "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "\n", "import merlin as ML\n", "import perceval as pcvl\n", "from perceval.components import BS, PS\n", "\n", "from sklearn.datasets import make_moons\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import accuracy_score, f1_score" ] }, { "cell_type": "markdown", "id": "b1b52f3f", "metadata": {}, "source": [ "## 2) Experimental setup\n", "\n", "### 2.1) Loading a simple dataset" ] }, { "cell_type": "code", "execution_count": 57, "id": "eae95f9e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SEED = 7\n", "np.random.seed(SEED)\n", "torch.manual_seed(SEED)\n", "\n", "def make_dataset(seed=SEED):\n", " X, y = make_moons(n_samples=320, noise=0.20, random_state=seed)\n", " X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.30, stratify=y, random_state=seed\n", " )\n", "\n", " scaler = StandardScaler()\n", " X_train = scaler.fit_transform(X_train)\n", " X_test = scaler.transform(X_test)\n", "\n", " return (\n", " X_train.astype(np.float32),\n", " X_test.astype(np.float32),\n", " y_train.astype(np.int64),\n", " y_test.astype(np.int64),\n", " )\n", "\n", "X_train, X_test, y_train, y_test = make_dataset()\n", "\n", "plt.figure(figsize=(5, 4))\n", "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=20, alpha=0.8)\n", "plt.title('Training data (two moons)')\n", "plt.xlabel('x1')\n", "plt.ylabel('x2')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9e74ace3", "metadata": {}, "source": [ "### 2.2) QOR structure used here\n", "\n", "We use the following pattern:\n", "\n", "1. fixed interferometer `U` (mixing stage),\n", "2. data encoding `E(x)` (phase shifts from input features),\n", "3. the **same** fixed interferometer `U` again,\n", "4. photonic measurement gives reservoir features,\n", "5. as mentioned above, we train a classical readout on top.\n", "\n", "This follows the common \"fixed random optical map + trainable readout\" idea used in QOR works." ] }, { "cell_type": "markdown", "id": "0840bdcb", "metadata": {}, "source": [ "### Note on PCA\n", "\n", "In the MNIST experiments from the referenced papers, PCA is often applied before encoding to reduce dimensionality.\n", "\n", "For this notebook we use two-moons (2 features), so PCA is not necessary.\n", "\n", "If you later switch this notebook to image datasets (e.g., MNIST), adding a PCA preprocessing block is recommended for a fair protocol match.\n" ] }, { "cell_type": "markdown", "id": "ffdf763b", "metadata": {}, "source": [ "## 3) Classical baselines\n", "\n", "We train two standard baselines:\n", "\n", "- Logistic Regression\n", "- 1-hidden-layer MLP" ] }, { "cell_type": "code", "execution_count": 56, "id": "dd34fa83", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1MLP(16)0.9166670.9183670.166300
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" ], "text/plain": [ " model accuracy f1 train_time_s\n", "0 LogReg 0.802083 0.808081 0.019834\n", "1 MLP(16) 0.916667 0.918367 0.166300" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def run_classical_baselines(X_train, y_train, X_test, y_test):\n", " results = []\n", "\n", " baselines = {\n", " 'LogReg': LogisticRegression(max_iter=1000, random_state=SEED),\n", " 'MLP(16)': MLPClassifier(hidden_layer_sizes=(16,), max_iter=1200, random_state=SEED),\n", " }\n", "\n", " for name, model in baselines.items():\n", " t0 = time.perf_counter()\n", " model.fit(X_train, y_train)\n", " dt = time.perf_counter() - t0\n", "\n", " y_pred = model.predict(X_test)\n", " results.append({\n", " 'model': name,\n", " 'accuracy': accuracy_score(y_test, y_pred),\n", " 'f1': f1_score(y_test, y_pred),\n", " 'train_time_s': dt,\n", " })\n", "\n", " return results\n", "\n", "classical_results = run_classical_baselines(X_train, y_train, X_test, y_test)\n", "pd.DataFrame(classical_results)" ] }, { "cell_type": "markdown", "id": "6cb32498", "metadata": {}, "source": [ "## 4) Shared helper: cached reservoir features + trainable linear readout\n", "\n", "In the referenced QOR workflows, the readout input is the concatenation `[x, r(x)]`.\n", "\n", "For efficiency, we compute `r(x)` once, cache it, then train only a linear readout on cached features.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "b86a7f25", "metadata": {}, "outputs": [], "source": [ "def compute_reservoir_features(reservoir, X, batch_size=128):\n", " reservoir.requires_grad_(False)\n", " reservoir.eval()\n", "\n", " X_tensor = torch.tensor(X, dtype=torch.float32)\n", " chunks = []\n", "\n", " with torch.no_grad():\n", " for start in range(0, len(X_tensor), batch_size):\n", " xb = X_tensor[start:start + batch_size]\n", " rb = reservoir(xb)\n", " chunks.append(rb)\n", "\n", " return torch.cat(chunks, dim=0)\n", "\n", "\n", "def make_concat_features(reservoir, X):\n", " X_tensor = torch.tensor(X, dtype=torch.float32)\n", " R_tensor = compute_reservoir_features(reservoir, X)\n", " return torch.cat([X_tensor, R_tensor], dim=1)\n", "\n", "\n", "def train_linear_readout_from_features(F_train, y_train, F_test, y_test, epochs=200, lr=0.05):\n", " ytr = torch.tensor(y_train, dtype=torch.long)\n", " yte = torch.tensor(y_test, dtype=torch.long)\n", " n_classes = int(np.max(y_train)) + 1\n", "\n", " readout = nn.Linear(F_train.shape[1], n_classes)\n", " optimizer = torch.optim.Adam(readout.parameters(), lr=lr)\n", " loss_fn = nn.CrossEntropyLoss()\n", "\n", " # Track training history\n", " history = {\n", " 'train_loss': [],\n", " 'val_loss': [],\n", " 'train_acc': [],\n", " 'val_acc': [],\n", " }\n", "\n", " t0 = time.perf_counter()\n", " readout.train()\n", " for epoch in range(epochs):\n", " # Training step\n", " optimizer.zero_grad()\n", " logits = readout(F_train)\n", " loss = loss_fn(logits, ytr)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # Track metrics every epoch\n", " readout.eval()\n", " with torch.no_grad():\n", " # Train metrics\n", " train_logits = readout(F_train)\n", " train_loss = loss_fn(train_logits, ytr).item()\n", " train_pred = train_logits.argmax(dim=1).cpu().numpy()\n", " train_acc = accuracy_score(y_train, train_pred)\n", "\n", " # Val metrics\n", " val_logits = readout(F_test)\n", " val_loss = loss_fn(val_logits, yte).item()\n", " val_pred = val_logits.argmax(dim=1).cpu().numpy()\n", " val_acc = accuracy_score(y_test, val_pred)\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", " readout.train()\n", "\n", " dt = time.perf_counter() - t0\n", "\n", " readout.eval()\n", " with torch.no_grad():\n", " y_pred = readout(F_test).argmax(dim=1).cpu().numpy()\n", "\n", " return {\n", " 'accuracy': accuracy_score(y_test, y_pred),\n", " 'f1': f1_score(y_test, y_pred),\n", " 'train_time_s': dt,\n", " 'history': history,\n", " }" ] }, { "cell_type": "markdown", "id": "deb76f95", "metadata": {}, "source": [ "## 5) First reservoir with `QuantumLayer.simple()`\n", "\n", "This gives a quick fixed photonic feature map. We then learn only the readout." ] }, { "cell_type": "code", "execution_count": 28, "id": "ec719d32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results: Accuracy 0.7917, F1 0.8000, Train time 0.09s\n" ] } ], "source": [ "simple_reservoir = ML.QuantumLayer.simple(input_size=2)\n", "\n", "F_train_simple = make_concat_features(simple_reservoir, X_train)\n", "F_test_simple = make_concat_features(simple_reservoir, X_test)\n", "\n", "simple_metrics = train_linear_readout_from_features(\n", " F_train_simple, y_train, F_test_simple, y_test, epochs=240, lr=0.01\n", ")\n", "\n", "print(f\"Results: Accuracy {simple_metrics['accuracy']:.4f}, F1 {simple_metrics['f1']:.4f}, Train time {simple_metrics['train_time_s']:.2f}s\")" ] }, { "cell_type": "markdown", "id": "4e626472", "metadata": {}, "source": [ "## 6) QOR-style reservoir: fixed Haar-random `U`, same `U` before and after encoding\n", "\n", "We now make the structure explicit:\n", "\n", "- sample a Haar-random unitary `U`,\n", "- decompose it into a photonic circuit,\n", "- build `U -> E(x) -> U`,\n", "- freeze all quantum parameters,\n", "- train only the classical readout." ] }, { "cell_type": "code", "execution_count": 51, "id": "96ae300d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Input state: |1,0,1,0,0,0>\n", "Circuit: 6 modes, 2 photons, 2 input features\n", "\n", "Results: Accuracy 0.9375, F1 0.9412, Train time 0.50s\n" ] } ], "source": [ "def build_qorc_layer(input_size=2, n_modes=6, n_photons=2):\n", " \"\"\"\n", " Build a QORC (Quantum Optical Reservoir Computing) layer with Haar-random unitaries.\n", " \n", " Parameters:\n", " -----------\n", " input_size : int (default 2)\n", " Number of input features to encode. For Moon dataset, this is 2.\n", " \n", " n_modes : int (default 6)\n", " Total number of modes in the photonic circuit.\n", " Must be >= input_size. Typically 2-3x input_size for good mixing.\n", " \n", " n_photons : int (default 2)\n", " Total number of photons in the input state.\n", " Affects the measurement output dimensionality.\n", " \n", " Returns:\n", " --------\n", " layer : ML.QuantumLayer\n", " A frozen (non-trainable) quantum layer ready to be used as a reservoir.\n", " \"\"\"\n", " assert n_modes >= input_size, f\"n_modes ({n_modes}) must be >= input_size ({input_size})\"\n", " \n", " # Generate Haar-random unitary\n", " unitary = pcvl.Matrix.random_unitary(n_modes)\n", " interferometer_1 = pcvl.Unitary(unitary)\n", " interferometer_2 = interferometer_1.copy()\n", "\n", " # Input Phase Shifters: apply encoding to first input_size modes\n", " c_var = pcvl.Circuit(n_modes)\n", " for i in range(input_size):\n", " px = pcvl.P(f\"px{i + 1}\")\n", " c_var.add(i, pcvl.PS(px))\n", "\n", " # Compose: U -> E(x) -> U\n", " qorc_circuit = interferometer_1 // c_var // interferometer_2\n", "\n", " # Initialize input state with n_photons photons spread across modes\n", " input_state = [0] * n_modes\n", " # Distribute n_photons photons across even modes for a sparse state\n", " photons_added = 0\n", " for i in range(0, n_modes, 2):\n", " if photons_added < n_photons:\n", " input_state[i] = 1\n", " photons_added += 1\n", " \n", " input_state = pcvl.BasicState(input_state)\n", " \n", " pcvl.pdisplay(qorc_circuit)\n", " print(f\"\\nInput state: {input_state}\")\n", " print(f\"Circuit: {n_modes} modes, {n_photons} photons, {input_size} input features\\n\")\n", " \n", " layer = ML.QuantumLayer(\n", " input_size=input_size,\n", " circuit=qorc_circuit,\n", " input_state=input_state,\n", " n_photons=n_photons,\n", " input_parameters=['px'],\n", " trainable_parameters=[],\n", " measurement_strategy=ML.MeasurementStrategy.probs(), #UNBUNCHED by default\n", " )\n", "\n", " return layer\n", "\n", "qorc_reservoir = build_qorc_layer()\n", "\n", "F_train_qorc = make_concat_features(qorc_reservoir, X_train)\n", "F_test_qorc = make_concat_features(qorc_reservoir, X_test)\n", "\n", "qorc_metrics = train_linear_readout_from_features(\n", " F_train_qorc, y_train, F_test_qorc, y_test, epochs=1200, lr=0.05\n", ")\n", "\n", "print(f\"Results: Accuracy {qorc_metrics['accuracy']:.4f}, F1 {qorc_metrics['f1']:.4f}, Train time {qorc_metrics['train_time_s']:.2f}s\")" ] }, { "cell_type": "markdown", "id": "5d01e28f", "metadata": {}, "source": [ "## 7) Compare all models" ] }, { "cell_type": "code", "execution_count": 52, "id": "fb695bd6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelaccuracyf1train_time_shistory
3QORC (Haar U, U-E(x)-U)0.9375000.9411760.496198{'train_loss': [0.5032566785812378, 0.45455822...
1MLP(16)0.9166670.9183670.166838NaN
0LogReg0.8020830.8080810.001437NaN
2MerLin simple0.7916670.8000000.094933{'train_loss': [0.5948845148086548, 0.58274984...
\n", "
" ], "text/plain": [ " model accuracy f1 train_time_s \\\n", "3 QORC (Haar U, U-E(x)-U) 0.937500 0.941176 0.496198 \n", "1 MLP(16) 0.916667 0.918367 0.166838 \n", "0 LogReg 0.802083 0.808081 0.001437 \n", "2 MerLin simple 0.791667 0.800000 0.094933 \n", "\n", " history \n", "3 {'train_loss': [0.5032566785812378, 0.45455822... \n", "1 NaN \n", "0 NaN \n", "2 {'train_loss': [0.5948845148086548, 0.58274984... " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_results = []\n", "all_results.extend(classical_results)\n", "\n", "all_results.append({\n", " 'model': 'MerLin simple',\n", " **simple_metrics,\n", "})\n", "\n", "all_results.append({\n", " 'model': 'QORC (Haar U, U-E(x)-U)',\n", " **qorc_metrics,\n", "})\n", "\n", "results_df = pd.DataFrame(all_results).sort_values(by='accuracy', ascending=False)\n", "results_df" ] }, { "cell_type": "code", "execution_count": 37, "id": "12089a0a", "metadata": {}, "outputs": [], "source": [ "def plot_training_curves(history, title):\n", " \"\"\"Plot training and validation loss/accuracy curves.\"\"\"\n", " fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", " \n", " # Plot loss\n", " axes[0].plot(history['train_loss'], label='Train Loss', linewidth=2)\n", " axes[0].plot(history['val_loss'], label='Val Loss', linewidth=2)\n", " axes[0].set_xlabel('Epoch')\n", " axes[0].set_ylabel('Loss')\n", " axes[0].set_title(f'{title} - Loss')\n", " axes[0].legend()\n", " axes[0].grid(True, alpha=0.3)\n", " \n", " # Plot accuracy\n", " axes[1].plot(history['train_acc'], label='Train Accuracy', linewidth=2)\n", " axes[1].plot(history['val_acc'], label='Val Accuracy', linewidth=2)\n", " axes[1].set_xlabel('Epoch')\n", " axes[1].set_ylabel('Accuracy')\n", " axes[1].set_title(f'{title} - Accuracy')\n", " axes[1].legend()\n", " axes[1].grid(True, alpha=0.3)\n", " \n", " plt.tight_layout()\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 38, "id": "b088b1b7", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training_curves(simple_metrics['history'], 'MerLin Simple Reservoir')\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "d1ca216c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training_curves(qorc_metrics['history'], 'QORC (Haar U, U-E(x)-U) Reservoir')\n" ] }, { "cell_type": "markdown", "id": "96b20875", "metadata": {}, "source": [ "### Analysis\n", "\n", "**Accuracy Performance:**\n", "\n", "Looking at the results above, we compare the models on the Moon dataset:\n", "\n", "- **LogReg**: Linear baseline with relatively low accuracy due to the non-linearity of the problem.\n", "- **MLP(16)**: Classical neural network with one hidden layer achieves good accuracy.\n", "- **MerLin simple**: Fixed photonic feature map provides non-linear features for the linear readout.\n", "- **QORC (Haar U, U-E(x)-U)**: Haar-random unitary reservoirs typically outperform simpler designs.\n", "\n", "**Overfitting Analysis:**\n", "\n", "From the training curves, we observe:\n", "\n", "- **Simple Reservoir**: Shows clear **overfitting** — the training loss continues to decrease while validation loss increases or plateaus. The gap between red and blue lines in the accuracy plot widens, indicating the model is memorizing training data rather than generalizing.\n", "\n", "- **QORC Reservoir**: Exhibits more **stable generalization** — training and validation curves track more closely together, suggesting better regularization through the fixed random optical structure. The validation loss and accuracy are more stable throughout training.\n", "\n", "This demonstrates that the **fixed Haar-random interferometer provides better feature lifting and regularization** compared to the simpler design.\n", "\n", "**Training Time:**\n", "\n", "- Classical methods (LogReg, MLP) train in **milliseconds**, as they operate on raw inputs.\n", "- Quantum reservoirs require **computing reservoir features** (photonic simulation) for each input, making them slower. The QORC and simple reservoir training is dominated by classical readout training once features are cached.\n", "- In a practical photonic setting, the reservoir computation would be parallelized on dedicated hardware, closing this gap.\n", "\n", "**Key Takeaways:**\n", "\n", "1. The **Haar-random QORC design outperforms** the simple reservoir on this task, demonstrating the value of structured quantum feature maps.\n", "2. The **fixed random structure acts as regularization**, preventing overfitting more effectively than simpler alternatives.\n", "3. For real quantum hardware, the speedup would be more pronounced, as photonic operations execute in constant time regardless of feature space dimensionality." ] }, { "cell_type": "markdown", "id": "318a22da", "metadata": {}, "source": [ "## 8) Scalablity of the reservoir\n", "\n", "We will compare the results of the Haar-based QORC with different number of modes and compare the results\n" ] }, { "cell_type": "code", "execution_count": 66, "id": "bcb07ac7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Input state: |1,0,1>\n", "Circuit: 3 modes, 2 photons, 2 input features\n", "\n", "\n", "Input state: |1,0,1,0>\n", "Circuit: 4 modes, 2 photons, 2 input features\n", "\n", "\n", "Input state: |1,0,1,0,0>\n", "Circuit: 5 modes, 2 photons, 2 input features\n", "\n", "\n", "Input state: |1,0,1,0,0,0>\n", "Circuit: 6 modes, 2 photons, 2 input features\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Accuracy by mode count:\n", " 3 modes: 0.8125\n", " 4 modes: 0.8021\n", " 5 modes: 0.8438\n", " 6 modes: 0.8125\n" ] } ], "source": [ "SEED = 16\n", "all_modes = [3,4,5,6] \n", "accuracies = {}\n", "for m in all_modes:\n", " np.random.seed(SEED)\n", " torch.manual_seed(SEED)\n", "\n", " qorc_reservoir = build_qorc_layer(n_modes=m, n_photons=2)\n", "\n", " F_train_qorc = make_concat_features(qorc_reservoir, X_train)\n", " F_test_qorc = make_concat_features(qorc_reservoir, X_test)\n", "\n", " qorc_metrics = train_linear_readout_from_features(\n", " F_train_qorc, y_train, F_test_qorc, y_test, epochs=240, lr=0.05\n", " )\n", " accuracies[str(m)] = qorc_metrics['accuracy']\n", "\n", "# Plot accuracy vs number of modes\n", "modes = [int(k) for k in accuracies.keys()]\n", "accs = [accuracies[k] for k in sorted(accuracies.keys(), key=lambda x: int(x))]\n", "\n", "plt.figure(figsize=(8, 5))\n", "plt.plot(modes, accs, marker='o', linewidth=2, markersize=8, color='blue', label='QORC Accuracy')\n", "plt.scatter(modes, accs, s=100, color='blue', alpha=0.6)\n", "plt.xlabel('Number of Modes', fontsize=12)\n", "plt.ylabel('Test Accuracy', fontsize=12)\n", "plt.title('QORC Reservoir: Accuracy vs Number of Modes', fontsize=14)\n", "plt.grid(True, alpha=0.3)\n", "plt.xticks(modes)\n", "plt.ylim([0.5, 1.0])\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"\\nAccuracy by mode count:\")\n", "for m in sorted(modes):\n", " print(f\" {m} modes: {accuracies[str(m)]:.4f}\")\n" ] }, { "cell_type": "markdown", "id": "65216cb6", "metadata": {}, "source": [ "**Interpretation**: we observe a boost in accuracy when the number of modes increases (and the Fock space increases as well). This is a very good scalability argument !" ] }, { "cell_type": "markdown", "id": "59ded8e5", "metadata": {}, "source": [ "## 9) Interpretation and next step\n", "\n", "What to look for:\n", "\n", "- If QORC beats the simple reservoir, the fixed random optical mixing likely gave a better feature map for this task.\n", "- If a classical MLP still wins, that is normal on small toy data; the goal here is to validate the QORC pipeline and evaluation protocol.\n", "\n", "We invite you to:\n", "\n", "1. repeat runs over multiple random seeds,\n", "2. observe the sensitivity to `n_modes` and `n_photons`,\n", "3. replace synthetic data with a target dataset from your project,\n", "4. richer readouts (ridge/logistic) and regularization." ] }, { "cell_type": "markdown", "id": "7ce73087", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }