[Joint CQSE & NCTS Seminar] Hybrid Quantum-Classical Machine Learning with Applications
Title: [Joint CQSE & NCTS Seminar] Hybrid Quantum-Classical Machine Learning with Applications
Speaker: Dr. Yen-Chi Chen (Senior Software Engineer, Wells Fargo Bank)
Time: Dec. 16, 2022, 14:30-15:30
Place: Rm. 104, Chin-Pao Yang Lecture Hall, CCMS & New Physics Building, NTU
Recent advances in machine learning (ML) and quantum computing (QC) hardware draw significant attention to building quantum machine learning (QML) applications. In this talk, I will provide an overview of the hybrid quantum-classical machine learning paradigm. Important ideas such as calculating quantum gradients will be described. Then I 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 advantages, scalability and use cases in the NISQ era will be discussed as well.
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 a
senior software engineer at Wells Fargo Bank. Prior to that, he was 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 won the First Prize in the Software Competition (Research
Category) from Xanadu Quantum Technologies, in 2019.