Enhancing Noisy Quantum Sensing by GHZ State Partitioning

TL;DR

This paper proposes a resource partitioning strategy for GHZ states in noisy quantum sensing, demonstrating improved phase estimation performance under realistic noise conditions through analytical and dynamic analysis.

Contribution

It introduces a simple partitioning method for GHZ states to optimize quantum sensing in noisy environments, supported by analytical formulas and dynamic simulations.

Findings

Partitioning GHZ states enhances quantum Fisher information under noise.

Optimal number of sub-ensembles depends on noise characteristics.

Partitioning improves short-time and sequential sensing performance.

Abstract

Presence of harmful noise is inevitable in entanglement-enhanced sensing systems, requiring careful allocation of resources to optimize sensing performance in practical scenarios. We advocate a simple but effective strategy to improve sensing performance in the presence of noise. Given a fixed number of quantum sensors, we partition the preparation of GHZ states by preparing smaller, independent sub-ensembles of GHZ states instead of a GHZ state across all sensors. We perform extensive analytical studies of the phase estimation performance when using partitioned GHZ states under realistic noise -- including state preparation error, particle loss during parameter encoding, and sensor dephasing during parameter encoding. We derive simple, closed-form expressions that quantify the optimal number of sub-ensembles for partitioned GHZ states. We also examine the explicit noisy quantum sensing dynamics under dephasing and loss, where we demonstrate the advantage from partitioning for maximal QFI, short-time QFI increase, and the sensing performance in the sequential scheme. The results offer quantitative insights into the sensing performance impact of different noise sources and reinforce the importance of resource allocation optimization in realistic quantum applications.