Distributed quantum sensing enhanced by continuous-variable error correction

A distributed sensing protocol uses a network of local sensing nodes to estimate a global feature of the network, such as a weighted average of locally detectable parameters. In the noiseless case, continuous-variable multipartite entanglement shared by the nodes can improve the precision of parameter estimation relative to the precision attainable by a network without shared entanglement; for an entangled protocol, the root-mean-square estimation error scales like $1/M$ with the number $M$ of sensing nodes, the so-called Heisenberg scaling, while for protocols without entanglement, the error scales like $1/\sqrt{M}$. However, in the presence of loss and other noise sources, although multipartite entanglement still has some advantages for sensing displacements and phases, the scaling of the precision with $M$ is less favorable. In this paper, we show that using continuous-variable error correction codes can enhance the robustness of sensing protocols against imperfections and reinstate Heisenberg scaling up to moderate values of $M$. Furthermore, while previous distributed sensing protocols could measure only a single quadrature, we construct a protocol in which both quadratures can be sensed simultaneously. Our work demonstrates the value of continuous-variable error correction codes in realistic sensing scenarios.