Structure of Optimal State Discrimination in Generalized Probabilistic Theories

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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.
Publisher
MDPI AG
Issue Date
2016-02
Language
English
Article Type
Article
Citation

ENTROPY, v.18, no.2

ISSN
1099-4300
DOI
10.3390/e18020039
URI
http://hdl.handle.net/10203/244350
Appears in Collection
EE-Journal Papers(저널논문)
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