[Joint CQSE & NCTS Seminar] Hybrid Quantum-Classical Machine Learning with Tensor Networks

Title: [Joint CQSE & NCTS Seminar] Hybrid Quantum-Classical Machine Learning with Tensor Networks
Speaker: Dr. Yen-Chi Chen (Computational Science Initiative, Brookhaven National Laboratory)
Date: Mar. 18, 2022, 14:30-15:30
Place: Online
Online Link: https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=m3efd6c4a404b85f1e7a186a89d9b9009

Recent advances in machine learning (ML) and quantum computing (QC)
hardware draw significant attention to building quantum machine learning (QML)
applications. One of the challenges is that the scale of existing quantum devices is small
and special methods are required to preprocess or compress the large-dimensional
inputs. The choice of such dimensional reduction method plays a crucial role in the
performance of QML. In this talk, I will first provide a quick overview of the hybrid
quantum-classical machine learning paradigm. Then I will present the recent progress of
tensor network (TN) based dimensional reduction methods used in QML. Specifically,
the hybrid TN-VQC models in both supervised learning (classification) tasks and
reinforcement learning will be described. Potential advantage and scalability in the
NISQ era will be discussed as well.

Biography Brief:
Dr. 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