Hands-On Introduction to Quantum Machine Learning
This tutorial introduces key concepts in Quantum Machine Learning (QML), covering qubits, gates, entanglement, parameterized circuits, and quantum neural networks (QNNs). It highlights recent advances in quantum-enhanced model compression and quantum architecture search (QAS), which improve QML efficiency and scalability. Attendees will gain hands-on experience with QML implementations on quantum simulators and receive guidance on tools for continued learning. Designed for beginners, the tutorial aims to foster cross-disciplinary innovation at the intersection of quantum computing and AI.
Authors
- Samuel Yen-Chi Chen (ORCID: https://orcid.org/0000-0003-0114-4826)
- Muhammad Ismail (ORCID: https://orcid.org/0000-0002-8185-0511)
- H. Eric Tseng (ORCID: https://orcid.org/0000-0001-9544-4226)
Institutions
- Wells Fargo (United States) (US)
- Brookhaven National Laboratory (US)
- Tennessee Technological University (US)
Publication Details
- Journal
- Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
- Published
- 2025-05-14
- DOI
- https://doi.org/10.32473/flairs.38.1.139039
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy
- Laboratory Directed Research and Development