Quantum Ensemble Classification: A Sampling-Based Learning Control Approach

doi:10.1109/TNNLS.2016.2540719

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	Quantum Ensemble Classification: A Sampling-Based Learning Control Approach
	Chen, Chunlin 1,2; Dong, Daoyi 3; Qi, Bo4 ; Petersen, Ian R.3; Rabitz, Herschel 2
	2017-06-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	28 期号:6 页码:1345-1359
摘要	Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.
关键词	Inhomogeneous ensembles quantum discrimination quantum ensemble classification (QEC) sampling-based learning control (SLC)
DOI	10.1109/TNNLS.2016.2540719
语种	英语
资助项目	National Natural Science Foundation of China[61004049] ; National Natural Science Foundation of China[61273327] ; National Natural Science Foundation of China[61374092] ; Australian Research Council[DP130101658] ; Australian Research Council[FL110100020] ; U.S. National Science Foundation[CHE-0718610]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000401982100008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/25473
专题	系统科学研究所
通讯作者	Dong, Daoyi
作者单位	1.Nanjing Univ, Sch Management & Engn, Dept Control & Syst Engn, Nanjing 210093, Jiangsu, Peoples R China 2.Princeton Univ, Dept Chem, Princeton, NJ 08544 USA 3.Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia 4.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Chunlin,Dong, Daoyi,Qi, Bo,et al. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(6):1345-1359.
APA	Chen, Chunlin,Dong, Daoyi,Qi, Bo,Petersen, Ian R.,&Rabitz, Herschel.(2017).Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(6),1345-1359.
MLA	Chen, Chunlin,et al."Quantum Ensemble Classification: A Sampling-Based Learning Control Approach".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.6(2017):1345-1359.