Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments
Dong, Daoyi1,2; Xing, Xi2; Ma, Hailan3; Chen, Chunlin3; Liu, Zhixin4; Rabitz, Herschel2
AbstractRobust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.
KeywordNonhomogeneous media Robust control Quantum computing Task analysis Chemistry Machine learning algorithms Uncertainty Differential evolution femtosecond laser quantum control quantum learning quantum robust control
Indexed BySCI
Funding ProjectAustralian Research Council[DP190101566] ; National Natural Science Foundation of China[61828303] ; National Natural Science Foundation of China[61833010] ; NSF[CHE-1464569] ; Army Research Office[W911NF-16-1-0014]
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000548811800014
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Document Type期刊论文
Corresponding AuthorDong, Daoyi
Affiliation1.Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia
2.Princeton Univ, Dept Chem, Princeton, NJ 08544 USA
3.Nanjing Univ, Sch Management & Engn, Dept Control & Syst Engn, Nanjing 210093, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
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GB/T 7714
Dong, Daoyi,Xing, Xi,Ma, Hailan,et al. Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(8):3581-3593.
APA Dong, Daoyi,Xing, Xi,Ma, Hailan,Chen, Chunlin,Liu, Zhixin,&Rabitz, Herschel.(2020).Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments.IEEE TRANSACTIONS ON CYBERNETICS,50(8),3581-3593.
MLA Dong, Daoyi,et al."Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments".IEEE TRANSACTIONS ON CYBERNETICS 50.8(2020):3581-3593.
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