The theories of optimization and machine learning answer foundational questions in computer science and lead to new algorithms for practical applications. While these topics have been extensively studied in the context of classical computing, their quantum counterparts are far from well-understood. In this talk, I will introduce my research that bridges the gap between the fields of quantum computing and machine learning.
To be more specific, I will briefly introduce some of my recent developments on quantum advantages for optimization and machine learning, including semidefinite programming (QIP 2019), convex optimization (QIP 2019), classification (ICML 2019), generative adversarial networks (NeurIPS 2019), volume estimation (QIP 2020), etc. I will also introduce limitations of quantum machine learning (QIP 2020).
Tongyang Li is a Ph.D. candidate at the Department of Computer Science, University of Maryland. He received B.E. from Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from Department of Mathematical Sciences, Tsinghua University, both in 2015; he also received a Master degree from Department of Computer Science, University of Maryland in 2018. He is currently a recipient of the IBM Ph.D. Fellowship and the NSF QISE-NET Triplet Award. His research focuses on designing quantum algorithms for optimization and machine learning.