Barycentric bounds on the error exponents of quantum hypothesis exclusion

TL;DR

This paper introduces new bounds on the error exponents for quantum state and channel exclusion tasks, improving previous bounds and providing computationally feasible methods with implications for classical and binary channel discrimination.

Contribution

It presents a single-letter upper bound on the error exponent using barycentric Chernoff divergence, extending analysis to quantum channel exclusion with adaptive strategies, and solves classical channel exclusion error exponent.

Findings

Improved upper bound on quantum state exclusion error exponent.

Efficient computability of bounds using barycentric Chernoff divergence.

Exact error exponent for classical channel exclusion with parallel strategies.

Abstract

Quantum state exclusion is an operational task with application to ontological interpretations of quantum states. In such a task, one is given a system whose state is randomly selected from a finite set, and the goal is to identify a state from the set that is not the true state of the system. An error occurs if and only if the state identified is the true state. In this paper, we study the optimal error probability of quantum state exclusion and its error exponent from an information-theoretic perspective. Our main finding is a single-letter upper bound on the error exponent of state exclusion given by the multivariate log-Euclidean Chernoff divergence, and we prove that this improves upon the best previously known upper bound. We also extend our analysis to quantum channel exclusion, and we establish a single-letter and efficiently computable upper bound on its error exponent, admitting the use of adaptive strategies. We derive both upper bounds, for state and channel exclusion, based on one-shot analysis and formulate them as a type of multivariate divergence measure called a barycentric Chernoff divergence. Moreover, our result on channel exclusion has implications in two important special cases. First, when there are two hypotheses, our result provides the first known efficiently computable upper bound on the error exponent of symmetric binary channel discrimination. Second, when all channels are classical, we show that our upper bound is achievable by a parallel strategy, thus solving the exact error exponent of classical channel exclusion.