Scaling Enhancement in Distributed Quantum Sensing via Causal Order Switching

Abstract: Sensing networks underpin applications from fundamental physics to real-world engineering. Recently, distributed quantum sensing (DQS) has been investigated to boost the sensing performance, yet current schemes typically rely on entangled probes that are fragile to noise and difficult to scale. Here, we propose a DQS protocol that incorporates a causal-order switch into a cyclic network, enabling a single probe to sequentially query N independent sensors in a coherent superposition or a probabilistic mixture of opposite causal orders. By exploiting the noncommutativity between propagation and sensing processes, our scheme achieves a 1/N^2-scaling precision limit without involving entangled probes. Importantly, our approach utilizes a classical mixture of causal orders rather than a quantum switch, making it more feasible for practical realization. We experimentally implement this scheme for distributed beam tilts sensing in a free-space quantum optical network comprising up to 9 sensors, achieving picoradian-scale precision in estimating tilt angle. Our results demonstrate a robust and scalable DQS protocol that surpasses the conventional 1/N Heisenberg scaling in precision, advancing the practical deployment of quantum sensing networks.