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Entropy 2016, 18(2), 39; doi:10.3390/e18020039

Structure of Optimal State Discrimination in Generalized Probabilistic Theories

1
Department of Applied Mathematics, Hanyang University (ERICA), 55 Hanyangdaehak-ro, Ansan 426-791, Korea
2
Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore
3
Institute of Advanced Studies, Nanyang Technological University, 60 Nanyang View, 639673, Singapore
4
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, 637616, Singapore
5
MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654, 117543, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Jay Lawrence
Received: 30 November 2015 / Revised: 12 January 2016 / Accepted: 20 January 2016 / Published: 26 January 2016
(This article belongs to the Section Quantum Information)
View Full-Text   |   Download PDF [494 KB, uploaded 27 January 2016]   |  

Abstract

We consider optimal state discrimination in a general convex operational framework, so-called generalized probabilistic theories (GPTs), and present a general method of optimal discrimination by applying the complementarity problem from convex optimization. The method exploits the convex geometry of states but not other detailed conditions or relations of states and effects. We also show that properties in optimal quantum state discrimination are shared in GPTs in general: (i) no measurement sometimes gives optimal discrimination, and (ii) optimal measurement is not unique. View Full-Text
Keywords: optimal state discrimination; generalized probabilistic theories; min-entropy optimal state discrimination; generalized probabilistic theories; min-entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bae, J.; Kim, D.-G.; Kwek, L.-C. Structure of Optimal State Discrimination in Generalized Probabilistic Theories. Entropy 2016, 18, 39.

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