Introduction to Hybrid Quantum-Classical Machine Learning with Applications

Topic : Introduction to Hybrid Quantum-Classical Machine Learning with Applications

Recent advances in machine learning (ML) and quantum computing (QC) hardware draw significant attention to building quantum machine learning (QML) applications. In this talk, Dr. Chen will first provide an overview of the hybrid quantum-classical machine learning paradigm. Important ideas such as calculating quantum gradients will be described. Then, Dr. Chen will present the recent progress of QML in various application fields such as classification, distributed or federated learning, speech recognition, natural language processing and reinforcement learning. Potential advantage and scalability in the NISQ era will be discussed as well. Finally, he will briefly discuss several promising research directions.

Dr. Samuel Yen-Chi Chen received the Ph.D. and B.S. degree in physics and the M.D. degree in medicine from National Taiwan University, Taipei City, Taiwan. He is now an assistant computational scientist in the Computational Science Initiative, Brookhaven National Laboratory. His research interests include building quantum machine learning algorithms as well as applying classical machine learning techniques to solve quantum computing problems. He was a recipient of the Theoretical Physics Fellowship from the National Taiwan University Center for Theoretical Physics, in 2015, and the First Prize In the Software Competition (Research Category) from Xanadu Quantum Technologies, in 2019.

Moderator: Pawel Gora, CEO of Quantum AI Foundation