Distributed quantum multiparameter estimation with optimal local measurements

TL;DR

This paper demonstrates that local measurements on a distributed entangled quantum sensor array can reach optimal sensitivity bounds for multiparameter phase estimation, outperforming independent sensors with the same resource count.

Contribution

It shows that local measurements suffice for optimal sensitivity in distributed quantum sensors and compares entangled versus independent configurations.

Findings

Local measurements saturate quantum Cramér-Rao bounds.

Entangled sensors outperform independent sensors with the same total particles.

Sensitivity scales as 1/average number of particles squared, with a factor of d improvement.

Abstract

We study the multiparameter sensitivity bounds of a sensor made by an array of $d$ spatially-distributed Mach-Zehnder interferometers (MZIs). A generic single non-classical state is mixed with $d-1$ vacuums to create a $d$-modes entangled state, each mode entering one input port of a MZI, while a coherent state enters its second port. We show that local measurements, independently performed on each MZI, are sufficient to provide a sensitivity saturating the quantum Cram\'er-Rao bound. The sensor can overcome the shot noise limit for the estimation of arbitrary linear combinations of the $d$ phase shifts, provided that the non-classical probe state has an anti-squeezed quadrature variance. We compare the sensitivity bounds of this sensor with that achievable with $d$ independent MZIs, each probed with a nonclassical state and a coherent state. We find that the $d$ independent interferometers can achieve the same sensitivity of the entangled protocol but at the cost of using additional $d$ non-classical states rather than a single one. When using in the two protocols the same average number of particles per shot $\bar{n}_T$, we find analytically a sensitivity scaling $1/\bar{n}_T^2$ for the entangled case which provides a gain factor $d$ with respect to the separable case where the sensitivity scales as $d/\bar{n}_T^2$. We have numerical evidences that the gain factor $d$ is also obtained when fixing the total average number of particles, namely when optimizing with respect to the number of repeated measurements.