Usefulness of adaptive strategies in asymptotic quantum channel discrimination

TL;DR

This paper investigates the effectiveness of adaptive strategies in asymptotic quantum channel discrimination, revealing conditions where adaptiveness offers no advantage and identifying scenarios with a fundamental separation.

Contribution

It provides the first analysis of adaptive versus non-adaptive strategies in quantum channel discrimination, including new bounds and examples demonstrating when adaptiveness is beneficial or not.

Findings

Adaptive and non-adaptive strategies have the same error exponents for classical-quantum channels.

First separation found between adaptive and non-adaptive symmetric hypothesis testing exponents.

Adaptive strategies with classical feedback do not outperform non-adaptive strategies in channel discrimination.

Abstract

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-breaking channels. (3)We prove, in some sense generalizing the previous statement, that for general channels adaptive strategies restricted to classical feed-forward and product state channel inputs are not superior in the asymptotic limit to non-adaptive product state strategies. (4) As an application of our findings, we address the discrimination power of an arbitrary quantum channel and show that adaptive strategies with classical feedback and no quantum memory at the input do not increase the discrimination power of the channel beyond non-adaptive tensor product input strategies.