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Quantum Machine Learning

Quantifying the advantage of vector over scalar magnetic sensor networks for undersea surveillance

arXiv
Authors: Wenchao Li, Xuezhi Wang, Qiang Sun, Allison N. Kealy, Andrew D. Greentree

Year

2025

Paper ID

36073

Status

Preprint

Abstract Read

~2 min

Abstract Words

95

Citations

N/A

Abstract

Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here we explore various magnetic sensor network architectures for target identification. Our modelling compares networks of scalar vs vector magnetometers. We implement an unscented Kalman filter approach to perform target tracking, and we find that vector networks provide a significant improvement in target tracking, specifically tracking accuracy and resilience compared with scalar networks.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.24250 [2] arXiv https://arxiv.org/abs/2512.24250 [3] Publisher https://arxiv.org/abs/2512.24250

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