【预告】香港城市大学王欣博士：基于强化学习的量子参数估计
直播二维码
量子科学论坛
（第二十五期）
Date & Time日期和时间
2020年12月8日（周二）10:00-11:00
Speaker报告人
Xin Wang
City University of Hong Kong
王 欣
香港城市大学
Host 主持人
刘 东
清华大学物理系
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The speaker will report in English
Title 题目
Quantum parameter estimation via reinforcement learning
基于强化学习的量子参数估计
Abstract 摘要
In this talk, I will present our recent work to apply reinforcement learning to quantum parameter estimation. In single-parameter case [1], we have shown that the control generated by our method is more generalizable than traditional methods such as GRAPE, namely the pulse sequences generated by the trained neuron network can be easily used to measure parameters having a range of values. We further extend the work to cases involving multiple parameters [2] and found that the generalizability of reinforcement learning mostly holds, which becomes much more significant for estimating an ensemble of systems with parameters varied in certain ranges. In the examples that we consider, each GRAPE run, on average, takes tens of hours on a typical CPU, while for reinforcement learning the time is only a few seconds. Therefore it quickly becomes prohibitively expensive for GRAPE to optimize controls of every parameter in the ensemble of systems, while the reinforcement learning method, generating optimal or suboptimal solutions, remains practical. Our results suggest that the usefulness of reinforcement learning, previously under-appreciated in quantum metrology, may play an important role given its generalizability, especially when massive measurements of an ensemble of systems are required.
[1] H. Xu, J. Li, L. Liu, Y. Wang, H. Yuan, and XW, npj Quantum Inf. 5, 82 (2019).
[2] H. Xu, L. Wang, H. Yuan, and XW, in preparation (2020).
About the speaker 报告人简介
王 欣
香港城市大学
Dr. Xin (Sunny) Wang received B.S. from School of Physics, Peking University in 2005, and received his Ph.D. degree from Columbia University in 2010. His Ph.D. study was focused on the theory of strongly correlated materials, in particular the high-Tc superconductors. From 2010-2015, Dr. Wang was a Research Associate in Condensed Matter Theory Center at University of Maryland, College Park. He joined City University of Hong Kong in 2015. His current research interests include the theory of quantum computation using electron spins, correlated electron systems, and numerical methods. He has published 47 journal papers, including those in Nature Communications, npj Quantum Information, and Physical Review Letters.
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