Improving spin-based noise sensing by adaptive measurements

TL;DR

This paper advances spin-based quantum sensors by developing adaptive measurement techniques specifically for sensing random magnetic noise, leading to faster noise characterization and potential improvements in coherence protection.

Contribution

It introduces a novel approach for adaptive sensing of magnetic noise using localized spins, distinct from deterministic field estimation, and demonstrates its effectiveness through numerical simulations.

Findings

Adaptive measurements significantly speed up noise characterization.

Distinct features differentiate noise sensing from deterministic field estimation.

Numerical results show improved efficiency in estimating spin decoherence time.

Abstract

Localized spins in the solid state are attracting widespread attention as highly sensitive quantum sensors with nanoscale spatial resolution and fascinating applications. Recently, adaptive measurements were used to improve the dynamic range for spin-based sensing of deterministic Hamiltonian parameters. Here we explore a very different direction -- spin-based adaptive sensing of random noises. First, we identify distinguishing features for the sensing of magnetic noises compared with the estimation of deterministic magnetic fields, such as the different dependences on the spin decoherence, the different optimal measurement schemes, the absence of the modulo-2\pi phase ambiguity, and the crucial role of adaptive measurement. Second, we perform numerical simulations that demonstrate significant speed up of the characterization of the spin decoherence time via adaptive measurements. This paves the way towards adaptive noise sensing and coherence protection.