Sampling-based learning control for quantum discrimination and ensemble classification

Chunlin Chen; Daoyi Dong; Bo Qi; Ian R. Petersen; Herschel Rabitz

doi:10.1109/IJCNN.2014.6889590

Sampling-based learning control for quantum discrimination and ensemble classification

Chunlin Chen, Daoyi Dong, Bo Qi, Ian R. Petersen, Herschel Rabitz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. In this paper, we recast quantum ensemble classification 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). Numerical results demonstrate the effectiveness of the proposed approach for the discrimination of two quantum systems and the binary classification of two-level quantum ensembles.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	880-885
Number of pages	6
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889590
State	Published - Sep 3 2014
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: Jul 6 2014 → Jul 11 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/Territory	China
City	Beijing
Period	7/6/14 → 7/11/14

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Keywords

Ensemble classification
inhomogeneous ensembles
quantum discrimination
sampling-based learning control

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10.1109/IJCNN.2014.6889590

Cite this

Chen, C., Dong, D., Qi, B., Petersen, I. R., & Rabitz, H. (2014). Sampling-based learning control for quantum discrimination and ensemble classification. In Proceedings of the International Joint Conference on Neural Networks (pp. 880-885). Article 6889590 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889590

@inproceedings{90e896aab1204c3db48f450495f40768,

title = "Sampling-based learning control for quantum discrimination and ensemble classification",

abstract = "Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. In this paper, we recast quantum ensemble classification 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). Numerical results demonstrate the effectiveness of the proposed approach for the discrimination of two quantum systems and the binary classification of two-level quantum ensembles.",

keywords = "Ensemble classification, inhomogeneous ensembles, quantum discrimination, sampling-based learning control",

author = "Chunlin Chen and Daoyi Dong and Bo Qi and Petersen, {Ian R.} and Herschel Rabitz",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/IJCNN.2014.6889590",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Chen, C, Dong, D, Qi, B, Petersen, IR & Rabitz, H 2014, Sampling-based learning control for quantum discrimination and ensemble classification. in Proceedings of the International Joint Conference on Neural Networks., 6889590, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 880-885, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 7/6/14. https://doi.org/10.1109/IJCNN.2014.6889590

Sampling-based learning control for quantum discrimination and ensemble classification. / Chen, Chunlin; Dong, Daoyi; Qi, Bo et al.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 880-885 6889590 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Sampling-based learning control for quantum discrimination and ensemble classification

AU - Chen, Chunlin

AU - Dong, Daoyi

AU - Qi, Bo

AU - Petersen, Ian R.

AU - Rabitz, Herschel

PY - 2014/9/3

Y1 - 2014/9/3

N2 - Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. In this paper, we recast quantum ensemble classification 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). Numerical results demonstrate the effectiveness of the proposed approach for the discrimination of two quantum systems and the binary classification of two-level quantum ensembles.

AB - Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. In this paper, we recast quantum ensemble classification 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). Numerical results demonstrate the effectiveness of the proposed approach for the discrimination of two quantum systems and the binary classification of two-level quantum ensembles.

KW - Ensemble classification

KW - inhomogeneous ensembles

KW - quantum discrimination

KW - sampling-based learning control

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M3 - Conference contribution

AN - SCOPUS:84908469771

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 880

EP - 885

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

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Chen C, Dong D, Qi B, Petersen IR, Rabitz H. Sampling-based learning control for quantum discrimination and ensemble classification. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. 2014. p. 880-885. 6889590. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2014.6889590

Sampling-based learning control for quantum discrimination and ensemble classification

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