Current Trends in Global Quantum Metrology

TL;DR

This paper reviews recent advances in global quantum metrology, focusing on bounds and algorithms for parameter estimation without prior knowledge, contrasting Bayesian and frequentist approaches.

Contribution

It categorizes emerging developments in global quantum sensing into two main approaches: adaptive Bayesian methods and fixed measurement optimization.

Findings

Global quantum sensing bounds are being actively researched.

Algorithms for achieving these bounds are being developed.

Two main strategies: Bayesian updating and fixed measurement optimization.

Abstract

Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assumed. Moreover, the framework provides a systematic approach for optimizing the sensing protocol. In contrast, the paradigm of global sensing aims to find a precision bound for parameter estimation in the absence of such prior information. In recent years, vigorous research has been pursued to describe the contours of global quantum estimation. Here, we review some of these emerging developments. These developments are both in the realm of finding ultimate precision bounds with respect to appropriate figures of merit in the global sensing paradigm, as well as in the search for algorithms that achieve these bounds. We categorize these developments into two largely mutually exclusive camps; one employing Bayesian updating and the other seeking to generalize the frequentist picture of local sensing towards the global paradigm. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively. In the second approach, the measurement setting is fixed, however the challenge is to identify this fixed measurement optimally.