Keynote Talk 1: Toward Trustworthy and Adaptive Quantum Machine Learning

Dr. Samuel Yen-Chi Chen received his Ph.D. and B.S. in physics, and an M.D. in medicine, from National Taiwan University. He is a Lead Research Scientist at Wells Fargo Bank, specializing in quantum machine learning (QML), and formerly an Assistant Computational Scientist at Brookhaven National Laboratory. He pioneered variational quantum circuits for reinforcement learning and invented quantum LSTM. His research spans QML algorithms, quantum error correction, architecture search, and privacy-preserving quantum AI. Dr. Chen has published 100+ papers in IEEE, APS, IOP, and major AI conferences, and is recognized for contributions to QRL, QLSTM, Quantum Fast Weight Programming, and Differentiable Quantum Architecture Search (DiffQAS). His achievements include First Prize in the Xanadu Quantum Technologies Software Competition (2019) and the IEEE QCE 2025 Best Paper Award. He has organized workshops and tutorials at leading IEEE conferences, and his current work focuses on self-evolving quantum agents and structure-aware QNNs for time-series learning and communication systems.