Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising

TL;DR

This paper introduces a self-supervised deep learning method for Rydberg microwave sensors that effectively denoises signals in a single shot, matching multi-measurement accuracy without needing clean references.

Contribution

The proposed framework enables noise suppression in Rydberg sensors using only noisy data, outperforming traditional methods and reducing computational costs significantly.

Findings

Achieves denoising comparable to 10,000 measurements

Outperforms wavelet and Kalman filtering methods

Valid across diverse noise profiles

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.