Quantum hypothesis testing in many-body systems

TL;DR

This paper explores quantum hypothesis testing in many-body systems, analyzing optimal measurement strategies and error bounds when distinguishing quantum states, with applications to spin chains and conformal field theories.

Contribution

It introduces new error inequalities involving relative entropy variance and studies optimal measurements in complex quantum many-body contexts.

Findings

Derived a new inequality for the variance of relative entropy.

Analyzed optimal measurement strategies for spin chains.

Explored distinguishability of reduced density matrices in conformal field theory.

Abstract

One of the key tasks in physics is to perform measurements in order to determine the state of a system. Often, measurements are aimed at determining the values of physical parameters, but one can also ask simpler questions, such as "is the system in state A or state B?". In quantum mechanics, the latter type of measurements can be studied and optimized using the framework of quantum hypothesis testing. In many cases one can explicitly find the optimal measurement in the limit where one has simultaneous access to a large number $n$ of identical copies of the system, and estimate the expected error as $n$ becomes large. Interestingly, error estimates turn out to involve various quantum information theoretic quantities such as relative entropy, thereby giving these quantities operational meaning. In this paper we consider the application of quantum hypothesis testing to quantum many-body systems and quantum field theory. We review some of the necessary background material, and study in some detail the situation where the two states one wants to distinguish are parametrically close. The relevant error estimates involve quantities such as the variance of relative entropy, for which we prove a new inequality. We explore the optimal measurement strategy for spin chains and two-dimensional conformal field theory, focusing on the task of distinguishing reduced density matrices of subsystems. The optimal strategy turns out to be somewhat cumbersome to implement in practice, and we discuss a possible alternative strategy and the corresponding errors.