Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates

• URL: http://arxiv.org/abs/2307.00484v2
• Date: Sat, 1 Jun 2024 10:09:37 GMT
• Title: Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates
• Authors: Tangyou Huang, Zhongcheng Yu, Zhongyi Ni, Xiaoji Zhou, Xiaopeng Li,
• Abstract summary: We propose a data-driven approach that harnesses the capabilities of machine learning to augment weak-signal detection sensitivity. In an atomic force sensor, our method combines a digital replica of force-free data with anomaly detection technique. Our findings demonstrate a significant advancement in sensitivity, achieving an order of magnitude improvement over conventional protocols.
• Score: 2.916921958708415
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: High sensitivity detection plays a vital role in science discoveries and technological applications. While intriguing methods utilizing collective many-body correlations and quantum entanglements have been developed in physics to enhance sensitivity, their practical implementation remains challenging due to rigorous technological requirements. Here, we propose an entirely data-driven approach that harnesses the capabilities of machine learning, to significantly augment weak-signal detection sensitivity. In an atomic force sensor, our method combines a digital replica of force-free data with anomaly detection technique, devoid of any prior knowledge about the physical system or assumptions regarding the sensing process. Our findings demonstrate a significant advancement in sensitivity, achieving an order of magnitude improvement over conventional protocols in detecting a weak force of approximately $10^{-25}~\mathrm{N}$. The resulting sensitivity reaches $1.7(4) \times 10^{-25}~\mathrm{N}/\sqrt{\mathrm{Hz}}$. Our machine learning-based signal processing approach does not rely on system-specific details or processed signals, rendering it highly applicable to sensing technologies across various domains.

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