QNN Iris Classification

A research-grade implementation of a Quantum Neural Network for the Iris classification task.

Project Structure

qnn_iris_project/
├── config/                 # Configuration files
│   └── config.py           # Global constants and hyperparameters
├── data/                   # Data loading and preprocessing
│   ├── iris_loader.py      # Data loading utilities
│   └── dataset.py          # PyTorch Dataset class
├── qnn/                    # Quantum neural network components
│   ├── encodings.py        # Quantum feature encodings
│   ├── circuit.py          # Quantum circuit implementation
│   ├── qnn_layer.py        # PyTorch module for QNN
│   └── hybrid_model.py     # Full hybrid classical-quantum model
├── train/                  # Training and evaluation
│   ├── trainer.py          # Training loop
│   ├── evaluator.py        # Model evaluation
│   └── plots.py            # Visualization utilities
├── experiments/            # Experiment scripts
│   └── run_experiment.py   # Main experiment pipeline
└── plots/                  # Output plots and figures

Setup

1. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

Usage

Run the main experiment:

python experiments/run_experiment.py

Requirements

• Python 3.8+
• PyTorch
• PennyLane
• scikit-learn
• matplotlib
• numpy