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Quantum learning control using differential evolution with equally-mixed strategies

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Abstract

Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution (DE) to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the EMSDE method for various quantum systems and show the potential for complex quantum control problems.

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Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, Nanjing Jiangsu, 210093, China

    Hailan Ma, Zhangqing Zhu & Chunlin Chen

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

    Daoyi Dong & Chuan-Cun Shu

Authors
  1. Hailan Ma
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  2. Daoyi Dong
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  3. Chuan-Cun Shu
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  4. Zhangqing Zhu
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  5. Chunlin Chen
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Corresponding author

Correspondence to Chunlin Chen.

Additional information

This paper is dedicated to Professor Ian R. Petersen on the occasion of his 60th birthday. This work was supported by the National Natural Science Foundation of China (Nos. 61374092, 61432008), the National Key Research and Development Program of China (No. 2016YFD0702100) and the Australian Research Council’s Discovery Projects funding scheme under Project DP130101658.

Hailan MA was born in Xiangyang, China, in 1992. She received the B.E. degree in Automation and the M.Sc. degree in Control Science and Engineering from Nanjing University, Nanjing, China, in 2014 and 2017, respectively. Her research interests include machine learning and quantum control.

Daoyi DONG was born in Hubei, China. He received the B.E. degree in Automatic Control and the Ph.D. degree in Pattern Recognition and Intelligent Systems from the University of Science and Technology of China, Hefei, China, in 2001 and 2006, respectively. He was as a Post-Doctoral Fellow with the Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, from 2006 to 2008. He was with the Institute of Cyber-Systems and Control, Zhejiang University, Zhejiang, China. He held visiting positions with Princeton University, Princeton, NJ, U.S.A., the University of Hong Kong, Hong Kong, and the City University of Hong Kong, Hong Kong.

He is currently a Senior Lecturer with the University of New South Wales, Canberra, Australia. His current research interests include quantum control, reinforcement learning, and intelligent systems and control. Dr.Dong is a recipient of an International Collaboration Award and an Australian Post-Doctoral Fellowship from the Australian Research Council, a K. C. Wong Post-Doctoral Fellowship, and a President Scholarship from the Chinese Academy of Sciences. He is also a co-recipient of Guan Zhao-Zhi Award at the 34th Chinese Control Conference and the Best Theory Paper Award at the 11th World Congress on Intelligent Control and Automation (WCICA). He serves as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.

Chuan-Cun SHU graduated from Dalian University of Technology (DUT), China in 2010, earning his Ph.D. in Atomic and Molecular Physics. After obtained his Ph.D., he joined Prof. Niels E. Henriksen’s group at Technical University of Denmark (DTU) by HC ϕrsted Postdoctoral Program, cofunded by Marie Curie Actions. After finished his research project at DTU in 2012, he had three years in Prof. Herschel Rabitz’s group at Princeton University as a full-time postdoctoral research associate. In May 2015, he joined Prof. Ian Petersen’s group at University of New South Wales Canberra as a Vice-Chancellor Postdoctoral Fellow. His current research interest focus on multiple constraint frequency domain quantum optimal control theory and its application to quantum systems. He has published more than 30 papers in peer reviewed international journals, including Journal of Physical Chemistry Letters, Optics Letters, Physical Review A and The Journal of Chemical Physics.

Zhangqing ZHU was born in Wuwei, Anhui province, China, in 1967. He received the Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 2006. He is currently an associate professor in the Department of Control and Systems Engineering of Nanjing University, Nanjing, China. His research interests include network control and nonlinear system.

Chunlin CHEN was born in Anhui, China, in 1979. He received the B.E. degree in Automatic Control and Ph.D. degree in Pattern Recognition and Intelligent Systems from the University of Science and Technology of China, Hefei, China, in 2001 and 2006, respectively. He was a Visiting Scholar with Princeton University, Princeton, NJ, U.S.A., from September 2012 to August 2013. He is currently a full Professor with the Department of Control and Systems Engineering, Nanjing University, Nanjing, China. His current research interests includemachine learning,mobile robotics, and quantum control.

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Ma, H., Dong, D., Shu, CC. et al. Quantum learning control using differential evolution with equally-mixed strategies. Control Theory Technol. 15, 226–241 (2017). https://doi.org/10.1007/s11768-017-7069-y

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