Learning robust and high-precision quantum controls

Re-Bing Wu, Haijin Ding, Daoyi Dong, and Xiaoting Wang
Phys. Rev. A 99, 042327 – Published 18 April 2019

Abstract

Robust and high-precision quantum control is extremely important but challenging for the functionalization of scalable quantum computation. In this paper, we show that this hard problem can be translated to a supervised machine learning task by thinking of the time-ordered quantum evolution as a layer-ordered neural network (NN). The seeking of robust quantum controls is then equivalent to training a highly generalizable NN, to which numerous tuning skills matured in machine learning can be transferred. This opens up a door through which a family of robust control algorithms can be developed. We exemplify such potential by introducing the commonly used trick of batch-based optimization, and the resulting batch-based gradient algorithm is numerically shown to be able to remarkably enhance the control robustness while maintaining high fidelity.

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  • Received 12 November 2018

DOI:https://doi.org/10.1103/PhysRevA.99.042327

©2019 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Re-Bing Wu* and Haijin Ding

  • Department of Automation, Tsinghua University, Beijing 100084, People's Republic of China and Center for Quantum Information Science and Technology, BNRist, Beijing 100084, People's Republic of China

Daoyi Dong

  • School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia

Xiaoting Wang

  • Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China

  • *rbwu@tsinghua.edu.cn
  • daoyidong@gmail.com
  • xiaoting@uestc.edu.cn

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Issue

Vol. 99, Iss. 4 — April 2019

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