A variational toolbox for quantum multi-parameter estimation

TL;DR

This paper introduces a variational quantum algorithm framework for optimizing probes and measurements in noisy multi-parameter quantum metrology, demonstrating improved performance over standard methods through simulations.

Contribution

It presents a general variational approach for quantum metrology that adapts to noise, including a parameter-shift rule, advancing quantum-aided design of measurement protocols.

Findings

Tailored probes and measurements outperform standard methods in noisy regimes.

The framework is applicable to both discrete and continuous-variable systems.

A general parameter-shift rule for noisy evolutions is proven.

Abstract

With an ever-expanding ecosystem of noisy and intermediate-scale quantum devices, exploring their possible applications is a rapidly growing field of quantum information science. In this work, we demonstrate that variational quantum algorithms feasible on such devices address a challenge central to the field of quantum metrology: The identification of near-optimal probes and measurement operators for noisy multi-parameter estimation problems. We first introduce a general framework which allows for sequential updates of variational parameters to improve probe states and measurements and is widely applicable to both discrete and continuous-variable settings. We then demonstrate the practical functioning of the approach through numerical simulations, showcasing how tailored probes and measurements improve over standard methods in the noisy regime. Along the way, we prove the validity of a general parameter-shift rule for noisy evolutions, expected to be of general interest in variational quantum algorithms. In our approach, we advocate the mindset of quantum-aided design, exploiting quantum technology to learn close to optimal, experimentally feasible quantum metrology protocols.