QuDET

Quantum Data Engineering Toolkit

A modular Python framework for building hybrid classical–quantum data pipelines.

Overview

QuDET bridges the gap between classical data engineering and quantum computing. It provides a production-ready, modular framework that lets AI engineers and researchers integrate quantum algorithms into data pipelines without deep quantum physics expertise.

QuDET follows scikit-learn conventions (fit / transform / predict) so you can drop quantum components into existing ML workflows with minimal friction.

Why QuDET?

• Practical quantum integration: Quantum components solve real problems (kernel methods, encoding, anomaly detection) rather than existing for novelty.
• Familiar API: scikit-learn compatible interfaces mean minimal learning curve.
• Modular architecture: Use only what you need. Each module works independently.
• Production-ready: Input validation, proper error handling, logging, and type hints throughout.
• Simulator-first: Works out of the box with Qiskit Aer. Optional IBM Quantum hardware support.

Architecture

QuDET is organized into six specialized layers:

Installation

pip install qudet

Optional Dependencies

# SQL database connectors
pip install "qudet[sql]"

# Encryption and security features
pip install "qudet[crypto]"

# Distributed computing with Dask
pip install "qudet[distributed]"

# IBM Quantum hardware access
pip install "qudet[ibm]"

# Everything (including dev tools)
pip install "qudet[all]"

Testing the installation

Open a Python prompt and run this minimal code to verify QuDET is installed correctly:

import numpy as np
from qudet.encoders import AngleEncoder

# Create dummy data and encode it
data = np.array([0.5, 0.8])
encoder = AngleEncoder(n_qubits=2)
circuit = encoder.encode(data)

print(f"QuDET installed successfully! Generated a {circuit.num_qubits}-qubit circuit.")

Examples & Tutorials

Check out the examples/ directory in this repository! We provide a highly informative Kickstart Guide (kickstart_guide.ipynb) that walks you through an end-to-end real-world workflow using all 6 QuDET modules on a wine quality dataset.

Development Setup

git clone https://github.com/satwiksps/qudet-dev.git
cd qudet-dev
python -m venv .venv

# Linux/macOS
source .venv/bin/activate

# Windows
.venv\Scripts\activate

pip install -e ".[dev]"

Feedback and Contributions

We welcome any feedback on how QuDET is working and are happy to receive bug reports, pull requests, and other feedback:

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Write tests for your changes
4. Ensure all tests pass (pytest)
5. Format code (black . and ruff check .)
6. Submit a pull request

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=qudet --cov-report=term-missing

# Run specific module tests
pytest tests/test_encoders/ -v

# Run only fast tests
pytest -m "not slow"

License

Distributed under the Apache 2.0 License. See LICENSE for details.