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.
Additional
information: https://arxiv.org/abs/1710.02581,
https://arxiv.org/abs/1809.01731, https://arxiv.org/abs/1901.03254,
https://arxiv.org/abs/1904.02276
报告人简介:
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
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