Sampling-based learning control for quantum discrimination and ensemble classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages880-885
Number of pages6
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period7/6/147/11/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Ensemble classification
  • inhomogeneous ensembles
  • quantum discrimination
  • sampling-based learning control

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  • Cite this

    Chen, C., Dong, D., Qi, B., Petersen, I. R., & Rabitz, H. A. (2014). Sampling-based learning control for quantum discrimination and ensemble classification. In Proceedings of the International Joint Conference on Neural Networks (pp. 880-885). [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