The Quantum_ML repository is a collection of hybrid quantum-classical machine learning applications designed to showcase the integration of quantum computing with traditional machine learning frameworks. Each module explores the application of quantum circuits for solving complex problems in various domains.
In particular, the repository emphasizes Variational Quantum Circuits (VQCs), which leverage parameterized quantum gates to optimize tasks through iterative classical-quantum feedback loops. VQCs are especially effective for machine learning tasks, such as classification and feature extraction, due to their ability to process data in high-dimensional Hilbert spaces. By integrating VQCs, this repository highlights the advantages of quantum computing in enhancing computational efficiency and solving problems intractable for classical systems.
Atit Pokharel
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, USA
Email: ap1284@uah.edu
- PennyLane Documentation: PennyLane
- TensorFlow Documentation: TensorFlow
Special thanks to the Department of Electrical and Computer Engineering at UAH, Dr. Dinh Nguyen and Dr. Thomas Morris for providing resources and guidance for this repository.