Selective and noise-resilient wave estimation with quantum sensor networks

TL;DR

This paper introduces methods for selective wave sensing using quantum sensor networks, demonstrating that entanglement enhances precision and noise resilience, with potential applications beyond wave detection.

Contribution

The paper presents novel quantum sensor control techniques that improve selectivity and noise suppression, leveraging entanglement and decoherence-free subspaces for wave sensing.

Findings

Entanglement enables Heisenberg scaling in precision.

Decoherence-free subspaces eliminate correlated noise.

Exponential advantage when sensor locations match noise sources.

Abstract

We consider the selective sensing of planar waves in the presence of noise. We present different methods to control the sensitivity of a quantum sensor network, which allow one to decouple it from arbitrarily selected waves while retaining sensitivity to the signal. Comparing these methods with classical (non-entangled) sensor networks we demonstrate two advantages. First, entanglement increases precision by enabling the Heisenberg scaling. Second, entanglement enables the elimination of correlated noise processes corresponding to waves with different propagation directions, by exploiting decoherence-free subspaces. We then provide a theoretical and numerical analysis of the advantage offered by entangled quantum sensor networks, which is not specific to waves and can be of general interest. We demonstrate an exponential advantage in the regime where the number of sensor locations is comparable to the number of noise sources. Finally, we outline a generalization to other waveforms, e.g., spherical harmonics and general time-dependent fields.