Abstract

The theories of machine learning and optimization 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 theoretical machine learning. To be more specific, I will briefly introduce some of my recent developments on quantum advantages for machine learning, covering all of supervised learning, unsupervised learning, and reinforcement learning. I will also briefly introduce the quantum algorithms I designed for convex and nonconvex optimization.

Biography

Tongyang Li is a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology. He received Master and PhD degrees from the Department of Computer Science, University of Maryland in 2018 and 2020, respectively. He received B.E. from the Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from the Department of Mathematical Sciences, Tsinghua University, both in 2015. He was a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship. His research focuses on designing quantum algorithms for machine learning and optimization.

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