Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866917681157898240 |
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| author | Huang, Tangyou Yu, Zhongcheng Ni, Zhongyi Zhou, Xiaoji Li, Xiaopeng |
| author_facet | Huang, Tangyou Yu, Zhongcheng Ni, Zhongyi Zhou, Xiaoji Li, Xiaopeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00484 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates Huang, Tangyou Yu, Zhongcheng Ni, Zhongyi Zhou, Xiaoji Li, Xiaopeng Quantum Physics Quantum Gases 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. |
| title | Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates |
| topic | Quantum Physics Quantum Gases |
| url | https://arxiv.org/abs/2307.00484 |