Abstract:

Quantum neural networks (QNNs) have been broadly used in various works with different levels of claimed benefits. One of my research interests in quantum machine learning is to understand the power of QNNs. In this talk, I will first compare the expressive power of QNNs with Boltzmann machines. Next, I will provide our results on the learnability of QNNs in terms of its trainability and generalization. Finally, I will provide a few applications of QNNs on machine learning tasks and ground state approximations.

Bio:

Min-Hsiu Hsieh received his BS and MS in electrical engineering from National Taiwan University in 1999 and 2001, and PhD degree in electrical engineering from the University of Southern California, Los Angeles, in 2008. From 2008-2010, he was a Researcher at the ERATO-SORST Quantum Computation and Information Project, Japan Science and Technology Agency, Tokyo, Japan. From 2010-2012, he was a Postdoctoral Researcher at
the Statistical Laboratory, the Centre for Mathematical Sciences, the University of Cambridge, UK. From 2012-2020, he was an Australian Research Council (ARC) Future Fellow and an Associate Professor at the Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. He is now the director of Hon Hai (Foxconn) quantum computing center. His scientific interests include quantum information, quantum learning, and quantum computation.