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Robust Learning Control Design for Quantum Unitary Transformations
Wu, Chengzhi1; Qi, Bo2; Chen, Chunlin1,3; Dong, Daoyi4
2017-12-01
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
Volume47Issue:12Pages:4405-4417
AbstractRobust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.
KeywordQuantum learning control quantum unitary transformation robustness sampling-based learning control (SLC)
DOI10.1109/TCYB.2016.2610979
Language英语
Funding ProjectNational Natural Science Foundation of China[61273327] ; National Natural Science Foundation of China[61374092] ; National Natural Science Foundation of China[61432008] ; Australian Research Council[DP130101658]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000415727200032
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26915
Collection系统科学研究所
Affiliation1.Nanjing Univ, Sch Management & Engn, Dept Control & Syst Engn, Nanjing 210093, Jiangsu, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
3.Nanjing Univ, Res Ctr Novel Technol Intelligent Equipments, Nanjing 210093, Jiangsu, Peoples R China
4.Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia
Recommended Citation
GB/T 7714
Wu, Chengzhi,Qi, Bo,Chen, Chunlin,et al. Robust Learning Control Design for Quantum Unitary Transformations[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(12):4405-4417.
APA Wu, Chengzhi,Qi, Bo,Chen, Chunlin,&Dong, Daoyi.(2017).Robust Learning Control Design for Quantum Unitary Transformations.IEEE TRANSACTIONS ON CYBERNETICS,47(12),4405-4417.
MLA Wu, Chengzhi,et al."Robust Learning Control Design for Quantum Unitary Transformations".IEEE TRANSACTIONS ON CYBERNETICS 47.12(2017):4405-4417.
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