A Framework for Spatial Quantum Sensing

TL;DR

This paper develops a comprehensive framework for spatial quantum sensing, enabling optimal estimation of fields using quantum sensor networks and entanglement, with broad applicability across scales.

Contribution

It introduces a novel framework for spatial quantum sensing, including interpolation methods, algebraic geometry conditions, and error-free subspaces for improved sensor efficiency.

Findings

Entanglement enhances maximal precision in distributed sensing.

Explicit constructions for polynomial field interpolation using algebraic geometry.

Error-free subspaces reduce the number of sensors needed based on prior knowledge.

Abstract

Quantum sensor networks promise precision advantages over classical and single-sensor strategies, in particular when the estimator is non-local. We address the problem of finding such estimators through a framework we connote spatial quantum sensing: given an underlying field interrogated by a network of quantum sensors at fixed positions, construct an estimator for a property of the field, for example, distinguishing a source of signal, or evaluating the field or its derivatives at an arbitrary point. We first treat polynomial fields, casting the task as an interpolation problem, and then generalize to fields modeled by analytic functions, which yields general least-squares estimators. A central and largely unaddressed question is under what conditions on sensor placement these estimators are well-defined and error-free. For $m$-dimensional arrays we give explicit constructions and proofs in the interpolation setting using algebraic geometry, and establish necessary and sufficient conditions in the general case. Comparing a non-local entangled protocol with the best local strategy, we show that entanglement yields maximal precision in distributed sensing under global resource constraints. Finally, we introduce error-free subspaces, a technique that translates prior knowledge of the field into a reduction in the number of required sensors. We expect these techniques to be broadly useful in sensing problems across scales, ranging from earth-scale experiments to local applications such as biological imaging.