Approximate decoherence free subspaces for distributed sensing

TL;DR

This paper develops methods to create approximate decoherence-free subspaces in sensor networks, enabling precise field measurements despite multiple noise sources, by exploiting spatial correlations and classical electrostatics analogies.

Contribution

It introduces the concept of approximate decoherence-free subspaces for distributed sensing, extending noise suppression techniques to complex spatial configurations and large sensor networks.

Findings

Maintains Heisenberg-scaling over long times with multiple noise sources.

Constructs efficient sensor states for noise suppression in large volumes.

Demonstrates wide applicability through multiple examples.

Abstract

We consider the sensing of scalar valued fields with specific spatial dependence using a network of sensors, e.g. multiple atoms located at different positions within a trap. We show how to harness the spatial correlations to sense only a specific signal, and be insensitive to others at different positions or with unequal spatial dependence by constructing a decoherence-free subspace for noise sources at fixed, known positions. This can be extended to noise sources lying on certain surfaces, where we encounter a connection to mirror charges and equipotential surfaces in classical electrostatics. For general situations, we introduce the notion of an approximate decoherence-free subspace, where noise for all sources within some volume is significantly suppressed, at the cost of reducing the signal strength in a controlled way. We show that one can use this approach to maintain Heisenberg-scaling over long times and for a large number of sensors, despite the presence of multiple noise sources in large volumes. We introduce an efficient formalism to construct internal states and sensor configurations, and apply it to several examples to demonstrate the usefulness and wide applicability of our approach.