{ "cells": [ { "cell_type": "markdown", "source": [ "# Quantum-Classical Hybrid Neural Network Comparison\n", "\n", "This notebook compares different neural network architectures including:\n", "- Classical Multi-Layer Perceptrons (MLP)\n", "- Quantum circuits with LINEAR output mapping\n", "- Quantum circuits with various GROUPING strategies" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "\n", "## 1. Import Required Libraries\n", "\n", " First, we'll import all necessary libraries for our experiment:\n", " - **PyTorch**: For neural network implementation and training\n", " - **Perceval**: For quantum circuit simulation\n", " - **Matplotlib/Seaborn**: For visualization\n", " - **Custom modules**: For quantum layers and MLP models\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "from collections import defaultdict\n", "\n", "import matplotlib.pyplot as plt\n", "import perceval as pcvl\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "from tqdm import tqdm\n", "import matplotlib\n", "\n", "%matplotlib inline\n", "from merlin.core.layer import QuantumLayer, OutputMappingStrategy\n", "from merlin.datasets.mlp_model import MLP, MLPConfig\n", "\n", "from merlin.datasets import iris" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:37.062879Z", "start_time": "2025-06-08T16:25:29.054372Z" } }, "outputs": [], "execution_count": 2 }, { "cell_type": "markdown", "source": [ "p\n", " ## 2. Load and Prepare the Iris Dataset\n", "\n", " We'll use the classic Iris dataset for multi-class classification. This dataset is ideal for comparing different architectures as it provides a simple but non-trivial classification task.\n", "\n", " The dataset contains:\n", " - **4 features**: sepal length, sepal width, petal length, petal width\n", " - **3 classes**: Setosa, Versicolor, Virginica\n", " - **150 samples** total (split into train/test sets)\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "train_features, train_labels, train_metadata = iris.get_data_train()\n", "test_features, test_labels, test_metadata = iris.get_data_test()\n", "\n", "# Convert data to PyTorch tensors\n", "X_train = torch.FloatTensor(train_features)\n", "y_train = torch.LongTensor(train_labels)\n", "X_test = torch.FloatTensor(test_features)\n", "y_test = torch.LongTensor(test_labels)\n", "\n", "print(f\"Training samples: {X_train.shape[0]}\")\n", "print(f\"Test samples: {X_test.shape[0]}\")\n", "print(f\"Features: {X_train.shape[1]}\")\n", "print(f\"Classes: {len(torch.unique(y_train))}\")" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:48.981322Z", "start_time": "2025-06-08T16:25:48.952480Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training samples: 120\n", "Test samples: 30\n", "Features: 4\n", "Classes: 3\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "source": [ "\n", " ## 3. Define Model Variants\n", "\n", " We'll test several model architectures to compare classical and quantum approaches:\n", "\n", " **Classical Models:**\n", " - `MLP`: Multi-layer perceptron with 8 hidden units, ReLU activation, and 0.1 dropout\n", "\n", " **Quantum Models:**\n", " - `LINEAR-7modes-nobunching`: Uses linear output mapping with 7 quantum modes and no-bunching constraint\n", " - `LEXGROUPING-7modes`: Uses lexicographic grouping strategy for output mapping\n", " - `MODGROUPING-7modes`: Uses modular grouping strategy for output mapping\n", "\n", " Each model type has a distinct color and line style for visualization.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def get_model_variants():\n", " \"\"\"Define different variants for each model type\"\"\"\n", " # Define consistent colors for each model type\n", " MODEL_COLORS = {\n", " 'MLP': '#1f77b4', # Blue\n", " 'LINEAR': '#2ca02c', # Green\n", " 'GROUPING': '#ff7f0e' # Orange\n", " }\n", "\n", " # Define line styles for variants\n", " LINE_STYLES = ['--', '-', ':', '-.']\n", "\n", " variants = {\n", " 'MLP': [\n", " {\n", " 'name': 'MLP',\n", " 'config': MLPConfig(hidden_sizes=[8], dropout=0.1, activation='relu'),\n", " 'color': MODEL_COLORS['MLP'],\n", " 'linestyle': LINE_STYLES[0]\n", " }\n", " ],\n", " 'LINEAR': [\n", " {\n", " 'name': 'LINEAR-7modes-nobunching',\n", " 'config': {\n", " 'm': 7,\n", " 'output_mapping_strategy': OutputMappingStrategy.LINEAR,\n", " 'no_bunching': True\n", " },\n", " 'color': MODEL_COLORS['LINEAR'],\n", " 'linestyle': LINE_STYLES[1]\n", " }\n", " ],\n", " 'GROUPING': [\n", " {\n", " 'name': 'LEXGROUPING-7modes',\n", " 'config': {\n", " 'm': 6,\n", " 'output_mapping_strategy': OutputMappingStrategy.LEXGROUPING\n", " },\n", " 'color': MODEL_COLORS['GROUPING'],\n", " 'linestyle': LINE_STYLES[2]\n", " },\n", " {\n", " 'name': 'MODGROUPING-7modes',\n", " 'config': {\n", " 'm': 6,\n", " 'output_mapping_strategy': OutputMappingStrategy.MODGROUPING\n", " },\n", " 'color': MODEL_COLORS['GROUPING'],\n", " 'linestyle': LINE_STYLES[1]\n", " }\n", " ]\n", " }\n", " return variants" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:50.932866Z", "start_time": "2025-06-08T16:25:50.906611Z" } }, "outputs": [], "execution_count": 4 }, { "cell_type": "markdown", "source": [ " ## 4. Quantum Circuit Architecture\n", "\n", " The quantum circuit is composed of three main sections:\n", "\n", " 1. **Left interferometer (WL)**: Performs initial quantum state transformation using beam splitters and phase shifters\n", " 2. **Variable phase shifters**: Encodes the 4 input features into quantum phases\n", " 3. **Right interferometer (WR)**: Performs final quantum state transformation\n", "\n", " The interferometers use a rectangular arrangement of optical elements, where each layer consists of beam splitters (BS) and programmable phase shifters (PS). The phase shifters contain trainable parameters that are optimized during training.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def create_quantum_circuit(m):\n", " \"\"\"Create quantum circuit with specified number of modes\n", "\n", " Parameters:\n", " -----------\n", " m : int\n", " Number of quantum modes in the circuit\n", "\n", " Returns:\n", " --------\n", " pcvl.Circuit\n", " Complete quantum circuit with trainable parameters\n", " \"\"\"\n", " # Left interferometer with trainable parameters\n", " wl = pcvl.GenericInterferometer(m,\n", " lambda i: pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_li{i}\")) // \\\n", " pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_lo{i}\")),\n", " shape=pcvl.InterferometerShape.RECTANGLE)\n", "\n", " # Variable phase shifters for input encoding\n", " c_var = pcvl.Circuit(m)\n", " for i in range(4): # 4 input features\n", " px = pcvl.P(f\"px{i + 1}\")\n", " c_var.add(i + (m - 4) // 2, pcvl.PS(px))\n", "\n", " # Right interferometer with trainable parameters\n", " wr = pcvl.GenericInterferometer(m,\n", " lambda i: pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_ri{i}\")) // \\\n", " pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_ro{i}\")),\n", " shape=pcvl.InterferometerShape.RECTANGLE)\n", "\n", " # Combine all components\n", " c = pcvl.Circuit(m)\n", " c.add(0, wl, merge=True)\n", " c.add(0, c_var, merge=True)\n", " c.add(0, wr, merge=True)\n", "\n", " return c" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:53.047254Z", "start_time": "2025-06-08T16:25:53.036233Z" } }, "outputs": [], "execution_count": 5 }, { "cell_type": "markdown", "source": [ "\n", " ## 5. Model Factory Functions\n", "\n", " These utility functions handle model creation and parameter counting. The `create_model` function serves as a factory that instantiates either classical MLP models or quantum models based on the specified configuration.\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def create_model(model_type, variant):\n", " \"\"\"Create model instance based on type and variant\n", "\n", " Parameters:\n", " -----------\n", " model_type : str\n", " Type of model ('MLP' or quantum model types)\n", " variant : dict\n", " Configuration dictionary for the model variant\n", "\n", " Returns:\n", " --------\n", " nn.Module\n", " PyTorch model instance\n", " \"\"\"\n", " if model_type == 'MLP':\n", " return MLP(input_size=4, output_size=3, config=variant['config'])\n", " else:\n", " m = variant['config']['m']\n", " no_bunching = variant['config'].get('no_bunching', False)\n", " c = create_quantum_circuit(m)\n", " return QuantumLayer(\n", " input_size=4, output_size=3,\n", " circuit=c,\n", " trainable_parameters=[\"theta\"],\n", " input_parameters=[\"px\"],\n", " input_state=[1, 0] * (m // 2) + [0] * (m % 2),\n", " no_bunching=no_bunching,\n", " output_mapping_strategy=variant['config']['output_mapping_strategy']\n", " )\n", "\n", "def count_parameters(model):\n", " \"\"\"Count trainable parameters in a PyTorch model\"\"\"\n", " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:54.988881Z", "start_time": "2025-06-08T16:25:54.979707Z" } }, "outputs": [], "execution_count": 6 }, { "cell_type": "markdown", "source": [ "\n", "\n", " ## 6. Training Function\n", "\n", " The training function implements a standard supervised learning loop with the following characteristics:\n", "\n", " - **Optimizer**: Adam with learning rate 0.02\n", " - **Loss function**: Cross-entropy for multi-class classification\n", " - **Batch size**: 32 samples\n", " - **Epochs**: 50 (default)\n", "\n", " The function tracks training loss, training accuracy, and test accuracy at each epoch. After training completes, it generates a detailed classification report.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def train_model(model, X_train, y_train, X_test, y_test, model_name, n_epochs=50, batch_size=32, lr=0.02):\n", " \"\"\"Train a model and track metrics\n", "\n", " Parameters:\n", " -----------\n", " model : nn.Module\n", " Model to train\n", " X_train, y_train : torch.Tensor\n", " Training data and labels\n", " X_test, y_test : torch.Tensor\n", " Test data and labels\n", " model_name : str\n", " Name for progress bar display\n", " n_epochs : int\n", " Number of training epochs\n", " batch_size : int\n", " Batch size for mini-batch training\n", " lr : float\n", " Learning rate\n", "\n", " Returns:\n", " --------\n", " dict\n", " Training results including losses, accuracies, and final report\n", " \"\"\"\n", " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", " criterion = nn.CrossEntropyLoss()\n", "\n", " losses = []\n", " train_accuracies = []\n", " test_accuracies = []\n", "\n", " model.train()\n", "\n", " pbar = tqdm(range(n_epochs), leave=False, desc=f\"Training {model_name}\")\n", " for epoch in pbar:\n", " # Shuffle training data\n", " permutation = torch.randperm(X_train.size()[0])\n", " total_loss = 0\n", "\n", " # Mini-batch training\n", " for i in range(0, X_train.size()[0], batch_size):\n", " indices = permutation[i:i + batch_size]\n", " batch_x, batch_y = X_train[indices], y_train[indices]\n", "\n", " outputs = model(batch_x)\n", " loss = criterion(outputs, batch_y)\n", "\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " total_loss += loss.item()\n", "\n", " avg_loss = total_loss / (X_train.size()[0] // batch_size)\n", " losses.append(avg_loss)\n", " pbar.set_description(f\"Training {model_name} - Loss: {avg_loss:.4f}\")\n", "\n", " # Evaluation\n", " model.eval()\n", " with torch.no_grad():\n", " # Training accuracy\n", " train_outputs = model(X_train)\n", " train_preds = torch.argmax(train_outputs, dim=1).numpy()\n", " train_acc = accuracy_score(y_train.numpy(), train_preds)\n", " train_accuracies.append(train_acc)\n", "\n", " # Test accuracy\n", " test_outputs = model(X_test)\n", " test_preds = torch.argmax(test_outputs, dim=1).numpy()\n", " test_acc = accuracy_score(y_test.numpy(), test_preds)\n", " test_accuracies.append(test_acc)\n", "\n", " model.train()\n", "\n", " # Generate final classification report\n", " model.eval()\n", " with torch.no_grad():\n", " final_test_outputs = model(X_test)\n", " final_test_preds = torch.argmax(final_test_outputs, dim=1).numpy()\n", " final_report = classification_report(y_test.numpy(), final_test_preds)\n", "\n", " return {\n", " 'losses': losses,\n", " 'train_accuracies': train_accuracies,\n", " 'test_accuracies': test_accuracies,\n", " 'final_test_acc': test_accuracies[-1],\n", " 'classification_report': final_report\n", " }" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:25:58.268166Z", "start_time": "2025-06-08T16:25:58.254059Z" } }, "outputs": [], "execution_count": 7 }, { "cell_type": "markdown", "source": [ "\n", " ## 7. Multi-Run Training Function\n", "\n", " To ensure robust and statistically meaningful results, we train each model variant multiple times with different random initializations. This approach helps us:\n", "\n", " - Understand the variance in model performance\n", " - Identify which architectures are more stable\n", " - Avoid drawing conclusions from lucky/unlucky single runs\n", " - Calculate confidence intervals for performance metrics\n", "\n", " The function tracks both individual run results and aggregate statistics across all runs.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def train_all_variants(X_train, y_train, X_test, y_test, num_runs=5):\n", " \"\"\"Train all model variants multiple times and return results\n", "\n", " Parameters:\n", " -----------\n", " X_train, y_train : torch.Tensor\n", " Training data and labels\n", " X_test, y_test : torch.Tensor\n", " Test data and labels\n", " num_runs : int\n", " Number of independent training runs per variant\n", "\n", " Returns:\n", " --------\n", " tuple\n", " (all_results, best_models) - Complete results and best performing models\n", " \"\"\"\n", " variants = get_model_variants()\n", " all_results = defaultdict(dict)\n", " best_models = {}\n", "\n", " for model_type, model_variants in variants.items():\n", " print(f\"\\n\\nTraining {model_type} variants:\")\n", " best_acc = 0\n", "\n", " for variant in model_variants:\n", " print(f\"\\nTraining {variant['name']}... ({count_parameters(create_model(model_type, variant))} parameters)\")\n", "\n", " # Store results from multiple runs\n", " variant_runs = []\n", "\n", " for run in range(num_runs):\n", " model = create_model(model_type, variant)\n", " print(f\" Run {run + 1}/{num_runs}...\")\n", "\n", " results = train_model(model, X_train, y_train, X_test, y_test, f\"{variant['name']}-run{run + 1}\")\n", " results['model'] = model\n", " results['color'] = variant['color']\n", " results['linestyle'] = variant['linestyle']\n", " variant_runs.append(results)\n", "\n", " # Track best model for each type\n", " if results['final_test_acc'] > best_acc:\n", " best_acc = results['final_test_acc']\n", " best_models[model_type] = {\n", " 'name': variant['name'],\n", " 'model': model,\n", " 'results': results\n", " }\n", "\n", " # Store all runs for this variant\n", " all_results[model_type][variant['name']] = {\n", " 'runs': variant_runs,\n", " 'color': variant['color'],\n", " 'linestyle': variant['linestyle'],\n", " 'avg_final_test_acc': sum(run['final_test_acc'] for run in variant_runs) / num_runs\n", " }\n", "\n", " return all_results, best_models\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:26:00.320839Z", "start_time": "2025-06-08T16:26:00.308207Z" } }, "outputs": [], "execution_count": 8 }, { "cell_type": "markdown", "source": [ "\n", " ## 8. Visualization Functions\n", "\n", " ### Training Curves Visualization\n", "\n", " This function creates a comprehensive view of the training process by plotting three key metrics:\n", "\n", " 1. **Training Loss**: Shows how well the model fits the training data\n", " 2. **Training Accuracy**: Indicates the model's performance on training samples\n", " 3. **Test Accuracy**: Reveals the model's generalization capability\n", "\n", " For each model variant, we display:\n", " - **Solid line**: Mean performance across all runs\n", " - **Shaded area**: Performance envelope (min to max values)\n", "\n", " This visualization helps identify overfitting, convergence speed, and stability.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def plot_training_curves(all_results):\n", " \"\"\"Plot training curves for all model variants with average and envelope\n", "\n", " Shows three subplots:\n", " 1. Training loss over epochs\n", " 2. Training accuracy over epochs\n", " 3. Test accuracy over epochs\n", "\n", " Each line represents the mean across runs, with shaded areas showing min/max envelope.\n", " \"\"\"\n", " fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n", "\n", " # Plot each metric\n", " for model_type, variants in all_results.items():\n", " for variant_name, variant_data in variants.items():\n", " color = variant_data['color']\n", " linestyle = variant_data['linestyle']\n", " label = f\"{variant_name}\"\n", "\n", " # Get data from all runs\n", " losses_runs = [run['losses'] for run in variant_data['runs']]\n", " train_acc_runs = [run['train_accuracies'] for run in variant_data['runs']]\n", " test_acc_runs = [run['test_accuracies'] for run in variant_data['runs']]\n", "\n", " # Calculate mean values across all runs\n", " epochs = len(losses_runs[0])\n", " mean_losses = [sum(run[i] for run in losses_runs) / len(losses_runs) for i in range(epochs)]\n", " mean_train_acc = [sum(run[i] for run in train_acc_runs) / len(train_acc_runs) for i in range(epochs)]\n", " mean_test_acc = [sum(run[i] for run in test_acc_runs) / len(test_acc_runs) for i in range(epochs)]\n", "\n", " # Calculate min and max values for the envelope\n", " min_losses = [min(run[i] for run in losses_runs) for i in range(epochs)]\n", " max_losses = [max(run[i] for run in losses_runs) for i in range(epochs)]\n", "\n", " min_train_acc = [min(run[i] for run in train_acc_runs) for i in range(epochs)]\n", " max_train_acc = [max(run[i] for run in train_acc_runs) for i in range(epochs)]\n", "\n", " min_test_acc = [min(run[i] for run in test_acc_runs) for i in range(epochs)]\n", " max_test_acc = [max(run[i] for run in test_acc_runs) for i in range(epochs)]\n", "\n", " # Plot mean lines\n", " ax1.plot(mean_losses, label=label, color=color, linestyle=linestyle, linewidth=2)\n", " ax2.plot(mean_train_acc, label=label, color=color, linestyle=linestyle, linewidth=2)\n", " ax3.plot(mean_test_acc, label=label, color=color, linestyle=linestyle, linewidth=2)\n", "\n", " # Plot envelopes (filled area between min and max)\n", " epochs_range = range(epochs)\n", " ax1.fill_between(epochs_range, min_losses, max_losses, color=color, alpha=0.2)\n", " ax2.fill_between(epochs_range, min_train_acc, max_train_acc, color=color, alpha=0.2)\n", " ax3.fill_between(epochs_range, min_test_acc, max_test_acc, color=color, alpha=0.2)\n", "\n", " # Customize plots\n", " for ax, title in zip([ax1, ax2, ax3], ['Training Loss', 'Training Accuracy', 'Test Accuracy']):\n", " ax.set_title(title, fontsize=12, pad=10)\n", " ax.set_xlabel('Epoch', fontsize=10)\n", " ax.set_ylabel(title.split()[-1], fontsize=10)\n", " ax.legend(fontsize=8, bbox_to_anchor=(1.05, 1), loc='upper left')\n", " ax.grid(True, linestyle='--', alpha=0.7)\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", "\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:26:02.301850Z", "start_time": "2025-06-08T16:26:02.289314Z" } }, "outputs": [], "execution_count": 9 }, { "cell_type": "markdown", "source": [ "\n", " ### Confusion Matrix Visualization\n", "\n", " Confusion matrices provide detailed insights into classification performance by showing:\n", " - Which classes are correctly classified (diagonal elements)\n", " - Which classes are confused with each other (off-diagonal elements)\n", " - Overall performance patterns for each model architecture\n", "\n", " We display the confusion matrix for the best-performing model of each type.\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def plot_best_confusion_matrices(best_models, X_test, y_test):\n", " \"\"\"Plot confusion matrices for the best model of each type\"\"\"\n", " fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n", " class_names = ['setosa', 'versicolor', 'virginica']\n", "\n", " for idx, (model_type, best) in enumerate(best_models.items()):\n", " model = best['model']\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(X_test)\n", " predictions = torch.argmax(outputs, dim=1).numpy()\n", "\n", " cm = confusion_matrix(y_test.numpy(), predictions)\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", " xticklabels=class_names, yticklabels=class_names, ax=axes[idx])\n", " axes[idx].set_title(f'Best {model_type}\\n({best[\"name\"]})')\n", " axes[idx].set_xlabel('Predicted')\n", " axes[idx].set_ylabel('True')\n", " plt.setp(axes[idx].get_xticklabels(), rotation=45)\n", " plt.setp(axes[idx].get_yticklabels(), rotation=45)\n", "\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:26:05.455592Z", "start_time": "2025-06-08T16:26:05.442345Z" } }, "outputs": [], "execution_count": 10 }, { "cell_type": "markdown", "source": [ "\n", " ## 9. Results Summary Function\n", "\n", " This function provides a comprehensive statistical analysis of the experimental results, including:\n", "\n", " - **Parameter efficiency**: Number of trainable parameters for each model\n", " - **Performance statistics**: Mean accuracy, standard deviation, min/max values\n", " - **Detailed classification reports**: Precision, recall, and F1-score for each class\n", "\n", " These metrics help in making informed decisions about model selection based on the specific requirements of your application.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "def print_comparison_results(all_results, best_models):\n", " \"\"\"Print detailed comparison of all models and variants with multi-run statistics\"\"\"\n", " print(\"\\n----- Model Comparison Results -----\")\n", "\n", " # Print results for all variants\n", " print(\"\\nAll Model Variants Results (averaged over multiple runs):\")\n", " for model_type, variants in all_results.items():\n", " print(f\"\\n{model_type} Variants:\")\n", " for variant_name, variant_data in variants.items():\n", " print(f\"\\n{variant_name}:\")\n", " print(f\"Parameters: {count_parameters(variant_data['runs'][0]['model'])}\")\n", "\n", " # Calculate statistics across runs\n", " final_accs = [run['final_test_acc'] for run in variant_data['runs']]\n", " avg_acc = sum(final_accs) / len(final_accs)\n", " min_acc = min(final_accs)\n", " max_acc = max(final_accs)\n", " std_acc = (sum((acc - avg_acc) ** 2 for acc in final_accs) / len(final_accs)) ** 0.5\n", "\n", " print(f\"Final Test Accuracy: {avg_acc:.4f} ± {std_acc:.4f} (min: {min_acc:.4f}, max: {max_acc:.4f})\")\n", "\n", " # Print best model results\n", " print(\"\\nBest Models:\")\n", " for model_type, best in best_models.items():\n", " print(f\"\\nBest {model_type} Model: {best['name']}\")\n", " print(f\"Final Test Accuracy: {best['results']['final_test_acc']:.4f}\")\n", " print(f\"Classification Report:\\n{best['results']['classification_report']}\")\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:26:08.651904Z", "start_time": "2025-06-08T16:26:08.638013Z" } }, "outputs": [], "execution_count": 11 }, { "cell_type": "markdown", "source": [ "\n", " ## 10. Run the Complete Experiment\n", "\n", " Now we'll execute the full experimental pipeline. The experiment consists of:\n", "\n", " 1. **Multiple training runs**: Each model variant is trained 5 times with different random seeds\n", " 2. **Performance tracking**: All metrics are recorded for statistical analysis\n", " 3. **Visualization**: Training curves and confusion matrices are generated\n", " 4. **Statistical summary**: Detailed performance statistics are computed\n", "\n", " This comprehensive approach ensures reliable and reproducible results.\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "# Set number of independent runs per model variant\n", "num_runs = 5\n", "\n", "# Train all variants\n", "print(\"Starting training process...\")\n", "all_results, best_models = train_all_variants(X_train, y_train, X_test, y_test, num_runs=num_runs)\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:27:00.693583Z", "start_time": "2025-06-08T16:26:11.508888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting training process...\n", "\n", "\n", "Training MLP variants:\n", "\n", "Training MLP... (67 parameters)\n", " Run 1/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 2/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 3/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 4/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 5/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Training LINEAR variants:\n", "\n", "Training LINEAR-7modes-nobunching... (192 parameters)\n", " Run 1/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 2/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 3/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 4/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 5/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Training GROUPING variants:\n", "\n", "Training LEXGROUPING-7modes... (60 parameters)\n", " Run 1/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 2/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 3/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 4/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 5/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training MODGROUPING-7modes... (60 parameters)\n", " Run 1/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 2/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 3/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "/Users/senellart/DEV/merlin_new/venv/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 4/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Run 5/5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] } ], "execution_count": 12 }, { "cell_type": "markdown", "source": [ " ### Visualize Training Progress\n", "\n", " The following plots show how each model's performance evolves during training. Pay attention to:\n", " - **Convergence speed**: How quickly each model reaches its best performance\n", " - **Stability**: Width of the shaded area indicates variance across runs\n", " - **Final performance**: Where each model plateaus" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "plot_training_curves(all_results)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:29:14.530219Z", "start_time": "2025-06-08T16:29:14.124832Z" } }, "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "execution_count": 13 }, { "cell_type": "markdown", "source": [ "\n", " ### Confusion Matrices for Best Models\n", "\n", " These confusion matrices reveal the classification patterns of the best-performing model from each architecture type. Look for:\n", " - **Perfect classification**: Bright blue diagonal with zeros elsewhere\n", " - **Common confusions**: Off-diagonal elements show which classes are hard to distinguish\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "print_comparison_results(all_results, best_models)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-06-08T16:29:17.080824Z", "start_time": "2025-06-08T16:29:17.066153Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "----- Model Comparison Results -----\n", "\n", "All Model Variants Results (averaged over multiple runs):\n", "\n", "MLP Variants:\n", "\n", "MLP:\n", "Parameters: 67\n", "Final Test Accuracy: 0.9133 ± 0.0400 (min: 0.8667, max: 0.9667)\n", "\n", "LINEAR Variants:\n", "\n", "LINEAR-7modes-nobunching:\n", "Parameters: 192\n", "Final Test Accuracy: 0.9467 ± 0.0267 (min: 0.9000, max: 0.9667)\n", "\n", "GROUPING Variants:\n", "\n", "LEXGROUPING-7modes:\n", "Parameters: 60\n", "Final Test Accuracy: 0.6800 ± 0.0267 (min: 0.6333, max: 0.7000)\n", "\n", "MODGROUPING-7modes:\n", "Parameters: 60\n", "Final Test Accuracy: 0.7000 ± 0.0699 (min: 0.6333, max: 0.8333)\n", "\n", "Best Models:\n", "\n", "Best MLP Model: MLP\n", "Final Test Accuracy: 0.9667\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 13\n", " 1 1.00 0.83 0.91 6\n", " 2 0.92 1.00 0.96 11\n", "\n", " accuracy 0.97 30\n", " macro avg 0.97 0.94 0.96 30\n", "weighted avg 0.97 0.97 0.97 30\n", "\n", "\n", "Best LINEAR Model: LINEAR-7modes-nobunching\n", "Final Test Accuracy: 0.9667\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 13\n", " 1 1.00 0.83 0.91 6\n", " 2 0.92 1.00 0.96 11\n", "\n", " accuracy 0.97 30\n", " macro avg 0.97 0.94 0.96 30\n", "weighted avg 0.97 0.97 0.97 30\n", "\n", "\n", "Best GROUPING Model: MODGROUPING-7modes\n", "Final Test Accuracy: 0.8333\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 13\n", " 1 0.55 1.00 0.71 6\n", " 2 1.00 0.55 0.71 11\n", "\n", " accuracy 0.83 30\n", " macro avg 0.85 0.85 0.80 30\n", "weighted avg 0.91 0.83 0.83 30\n", "\n" ] } ], "execution_count": 14 }, { "cell_type": "markdown", "source": [ " ## 11. Key Findings and Conclusions\n", "\n", " Based on the experimental results, we can draw several important conclusions:\n", "\n", " **Model Complexity vs Performance:**\n", " - Compare the number of parameters required by each architecture\n", " - Assess whether quantum models achieve similar performance with fewer parameters\n", "\n", " **Training Stability:**\n", " - The variance across multiple runs indicates how sensitive each model is to initialization\n", " - Lower variance suggests more reliable training\n", "\n", " **Generalization Capability:**\n", " - The gap between training and test accuracy reveals overfitting tendencies\n", " - Smaller gaps indicate better generalization\n", "\n", " **Classification Patterns:**\n", " - Confusion matrices show which flower species are most difficult to distinguish\n", " - This can guide feature engineering or model selection\n", "\n", " **Practical Considerations:**\n", "\n", " For deployment, consider:\n", " - **Classical MLPs**: Well-understood, fast inference, established deployment pipelines\n", " - **Quantum models**: Potentially more parameter-efficient, may offer advantages on quantum hardware\n", "\n", " **Future Research Directions:**\n", "\n", " 1. **Scaling to larger datasets**: Test on more complex classification tasks\n", " 2. **Noise modeling**: Investigate performance under realistic quantum noise conditions\n", " 3. **Hybrid architectures**: Combine classical and quantum layers\n", " 4. **Hardware implementation**: Evaluate on actual quantum photonic devices\n", " 5. **Feature encoding strategies**: Explore different ways to encode classical data into quantum states" ], "metadata": { "collapsed": false } } ], "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": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 0 }