中国科学院量子信息重点实验室学术报告视频--王欣

图 | 王欣博士

题   目：Application of machine learning methods to quantum control problems

报告人：王欣

单   位：香港城市大学

时   间：2019年2月23日

地   点：中国科学技术大学

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报告简介

In this talk, I will present results from our recent attempts to apply machine learning methods to quantum control problems. In particular, I will discuss how supervised learning can be used to design composite pulse sequences that are robust against noise, while at the same time can be used to measure the noise spectra in a spin-qubit device while used in conjunction with randomized benchmarking .

On the other hand, I will demonstrate that reinforcement learning can be used to improve the quantum speed limit of transferring a spin in a chain,and can help in efficiently preparing a quantum state.
The pros and cons of reinforcement learning compared to other methods commonly used (such as gradient-based ones) are also studied in detail.

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个人简介

Xin Wang received his B.S. in Physics from Peking University, China, in 2005. He then went to the United States for graduate study in Columbia University in the City of New York, where he received Ph.D. in Physics in 2010. His Ph.D. study was focused on the theory of strongly correlated materials, in particular the high-temperature superconductors. From 2010-2015, Dr. Wang was a Research Associate in Condensed Matter Theory Center at University of Maryland, College Park, U.S.A. He joined City University of Hong Kong in 2015. His current research interests include quantum computation using electron spins and correlated electron systems.

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