报告人：Xin Wang（王欣）City University of Hong Kong（香港城市大学）
报告题目： Application of machine learning methods to quantum control problems
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 .
 X.-C. Yang, M.-H. Yung, and XW, Phys. Rev. A 97, 042324 (2018).
 C. Zhang, and XW, arXiv:1810.07914.
 X.-M. Zhang, Z.-W. Cui, XW, and M.-H. Yung, Phys. Rev. A 97, 052333 (2018).
 X.-M. Zhang, Z. Wei, R. Asad, X.-C. Yang, and XW, arXiv:1902.02157.