Probe optimization for quantum metrology via closed-loop learning control

TL;DR

This paper introduces a practical quantum metrology scheme using closed-loop learning control, improving feasibility and performance over standard methods through numerical and NMR experiments.

Contribution

It proposes a new controlled sequential scheme for quantum metrology that is easier to implement and more effective, utilizing closed-loop learning control and purity loss measurement.

Findings

The scheme demonstrates superior precision compared to standard quantum metrology.

Numerical analysis confirms the scheme's effectiveness.

Proof-of-principle NMR experiments validate the approach.

Abstract

Experimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance (NMR) system.