南京大学计算机科学与技术系

软件新技术与产业化协同创新中心

摘 要：

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 and optimization, including classification (ICML 2019), convex optimization (QIP 2019), generative adversarial networks (NeurIPS 2019), semidefinite programming (QIP 2019), etc. I will also introduce limitations of quantum computers by giving quantum-inspired classical machine learning algorithms.

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 a recipient of the IBM Ph.D. Fellowship,the NSF QISE-NET Triplet Award, and was a recipient of the Lanczos Fellowship. His research focuses on designing quantum algorithms for machine learning and optimization.

时间:1月2日   15:00-16:00

地点:计算机科学技术楼224室