Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising
Abstract
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.
Cite
@article{arxiv.2601.01924,
title = {Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising},
author = {Zongkai Liu and Qiming Ren and Wenguang Yang and Yanjie Tong and Huizhen Wang and Yijie Zhang and Ruohao Zhi and Junyao Xie and Mingyong Jing and Hao Zhang and Liantuan Xiao and Suotang Jia and Ke Tang and Linjie Zhang},
journal= {arXiv preprint arXiv:2601.01924},
year = {2026}
}