Structure of Optimal State Discrimination in Generalized Probabilistic Theories

Structure of Optimal State Discrimination in Generalized Probabilistic Theories

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...

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Bibliographic Details
Main Authors: Joonwoo Bae, Dai-Gyoung Kim, Leong-Chuan Kwek
Format: Article
Language:English
Published: MDPI AG 2016-01-01
Series:Entropy
Subjects:
optimal state discrimination
generalized probabilistic theories
min-entropy
Online Access:http://www.mdpi.com/1099-4300/18/2/39
Description
Summary: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.
ISSN:1099-4300

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