KMS Of Academy of mathematics and systems sciences, CAS
| Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments | |
| Dong, Daoyi1,2; Xing, Xi2; Ma, Hailan3; Chen, Chunlin3; Liu, Zhixin4; Rabitz, Herschel2 | |
| 2020-08-01 | |
| Source Publication | IEEE TRANSACTIONS ON CYBERNETICS
 |
| ISSN | 2168-2267 |
| Volume | 50Issue:8Pages:3581-3593 |
| Abstract | Robust 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. |
| Keyword | Nonhomogeneous media Robust control Quantum computing Task analysis Chemistry Machine learning algorithms Uncertainty Differential evolution femtosecond laser quantum control quantum learning quantum robust control |
| DOI | 10.1109/TCYB.2019.2921424 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Australian 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 Area | Automation & Control Systems ; Computer Science |
| WOS Subject | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS ID | WOS:000548811800014 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/51820 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Dong, Daoyi |
| Affiliation | 1.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 |
| Recommended Citation 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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