Engineering and Technology

Quantum force sensing by digital twinning of atomic Bose-Einstein condensates

T. Huang, Z. Yu, et al.

This research proposes a groundbreaking data-driven approach utilizing machine learning to enhance the sensitivity of weak-signal detection in atomic force sensors. Conducted by Tangyou Huang, Zhongcheng Yu, Zhongyi Ni, Xiaoji Zhou, and Xiaopeng Li, the method introduces a digital twin combined with anomaly detection, achieving unmatched sensitivity without prior system knowledge.

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Abstract

This paper proposes a data-driven approach using machine learning to significantly improve the sensitivity of weak-signal detection in atomic force sensors. By combining a digital replica (digital twin) of force-free data with an anomaly detection technique, the method achieves an order of magnitude improvement in sensitivity, reaching 1.7(4) × 10<sup>−25</sup> N/Hz, without relying on prior knowledge of the physical system or assumptions about the sensing process. This approach is broadly applicable to various sensing technologies.

Publisher

Communications Physics

Published On

Jun 01, 2024

Authors

Tangyou Huang, Zhongcheng Yu, Zhongyi Ni, Xiaoji Zhou, Xiaopeng Li

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