Machine learning enhanced microwave electrometry in many-body Rydberg atoms

Precision microwave electrometry using Rydberg atoms is limited by many-body interactions at moderate or not too low excitation densities, which may result in distorted probe spectra of electromagnetically induced transparency (EIT) as predicted by mean-field superatom simulations. This nonlinearity fundamentally restricts measurement accuracy and renders the linear response formula of Autler-Townes splitting to microwave (MW) amplitude inapplicable. Here we present a machine learning (ML) framework assisted by principal component analysis (PCA) that can well overcome this difficulty by mapping distorted noisy EIT spectra directly to MW electric fields. Numerical results show that a basic PCA-assisted regression model reduces the minimal detectable MW field by a few folds as compared to both linear response formula and nonlinear least squares fitting in the interaction-dominant regime. It is of more interest that we can employ a four-class ensemble ML scheme to further reduce the minimal detectable MW field by two orders of magnitude to approach its noise-dependent physical limit and hence largely improve the measurement sensitivity. Our work transforms detrimental Rydberg interactions into beneficial spectral features, providing a practical route toward optimal sensitivity for MW quantum sensing in the nonlinear regime. Rydberg atom-based microwave electrometry is hindered by many-body interactions, distorting probe spectra and limiting measurement accuracy. Here, the authors employ a machine learning framework with principal component analysis to map distorted spectra to microwave fields, significantly enhancing sensitivity and transforming interaction challenges into advantageous spectral features for quantum sensing.