QuantumAutoencode

Exploring quantum autoencoders for image patch compression and reconstruction. This project compares quantum and classical autoencoders using Qiskit and TensorFlow to evaluate compression fidelity, resource efficiency, and potential for quantum-enhanced anomaly detection.

Prerequisites

• Python 3.13
• pip (latest version recommended)

Setup

For Linux

Run the following commands in the QuantumAutoencode directory:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip && pip install -r requirements.txt

For Windows

Run the following commands in the QuantumAutoencode directory:

python -m venv venv
./venv/scripts/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

Project Structure

QuantumAutoencode/
├── classical/        # Classical autoencoder implementation
├── datasets/         # Dataset files and README
├── quantum/          # Quantum autoencoder implementation
├── tests/            # Unit tests
├── requirements.txt  # Python dependencies
├── ruff.toml         # Linter configuration
└── README.md         # Project documentation

Unit Tests

Run the following command from the QuantumAutoencode directory to execute the unit tests:

python -m pytest tests