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Usefulness of Adaptive Strategies in Asymptotic Quantum Channel Discrimination by Salek, Farzin; Hayashi, Masahito; Winter, Andreas is a scholarly article available to read on EtoBox.

What is Usefulness of Adaptive Strategies in Asymptotic Quantum Channel Discrimination about?

Adaptiveness is a key principle in information processing including statistics and machine learning. We investigate the usefulness of adaptive methods in the framework of asymptotic binary hypothesis testing, when each hypothesis represents asymptotically many independent instances of a quantum channel, and the tests are based on using the unknown channel and observing outputs. Unlike the familiar setting of quantum states as hypotheses, there is a fundamental distinction between adaptive and non-adaptive strategies with respect to the channel uses, and we introduce a number of further variants of the discrimination tasks by imposing different restrictions on the test strategies. The following results are obtained: (1) We prove that for classical-quantum channels, adaptive and non-adaptive strategies lead to the same error exponents both in the symmetric (Chernoff) and asymmetric (Hoeffding, Stein) settings. (2) The first separation between adaptive and non-adaptive symmetric hypothesis testing exponents for quantum channels, which we derive from a general lower bound on the error probability for non-adaptive strategies; the concrete example we analyze is a pair of entanglement-bre

Author

Salek, Farzin; Hayashi, Masahito; Winter, Andreas

Published

2020

Language

EN