Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917183878070272 |
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| author | Liu, Zongkai Ren, Qiming Yang, Wenguang Tong, Yanjie Wang, Huizhen Zhang, Yijie Zhi, Ruohao Xie, Junyao Jing, Mingyong Zhang, Hao Xiao, Liantuan Jia, Suotang Tang, Ke Zhang, Linjie |
| author_facet | Liu, Zongkai Ren, Qiming Yang, Wenguang Tong, Yanjie Wang, Huizhen Zhang, Yijie Zhi, Ruohao Xie, Junyao Jing, Mingyong Zhang, Hao Xiao, Liantuan Jia, Suotang Tang, Ke Zhang, Linjie |
| contents | We report a self-supervised deep learning framework for Rydberg sensors that enables single-shot noise suppression matching the accuracy of multi-measurement averaging. The framework eliminates the need for clean reference signals (hardly required in quantum sensing) by training on two sets of noisy signals with identical statistical distributions. When evaluated on Rydberg sensing datasets, the framework outperforms wavelet transform and Kalman filtering, achieving a denoising effect equivalent to 10,000-set averaging while reducing computation time by three orders of magnitude. We further validate performance across diverse noise profiles and quantify the complexity-performance trade-off of U-Net and Transformer architectures, providing actionable guidance for optimizing deep learning-based denoising in Rydberg sensor systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01924 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising Liu, Zongkai Ren, Qiming Yang, Wenguang Tong, Yanjie Wang, Huizhen Zhang, Yijie Zhi, Ruohao Xie, Junyao Jing, Mingyong Zhang, Hao Xiao, Liantuan Jia, Suotang Tang, Ke Zhang, Linjie Quantum Physics Atomic Physics We report a self-supervised deep learning framework for Rydberg sensors that enables single-shot noise suppression matching the accuracy of multi-measurement averaging. The framework eliminates the need for clean reference signals (hardly required in quantum sensing) by training on two sets of noisy signals with identical statistical distributions. When evaluated on Rydberg sensing datasets, the framework outperforms wavelet transform and Kalman filtering, achieving a denoising effect equivalent to 10,000-set averaging while reducing computation time by three orders of magnitude. We further validate performance across diverse noise profiles and quantify the complexity-performance trade-off of U-Net and Transformer architectures, providing actionable guidance for optimizing deep learning-based denoising in Rydberg sensor systems. |
| title | Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising |
| topic | Quantum Physics Atomic Physics |
| url | https://arxiv.org/abs/2601.01924 |