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Machine-learning-enhanced Rydberg transient readout of weak electric pulses

Chunyang Jia, Wenhao Luo, Yue Chang, Jiaao Li, Xuehua Chen, Xiaobo Song, Hongjia Wang, Nan cong, Renfu Yang

Published Aug 27, 2026• 0 citations
Publisher

Abstract

Rydberg-atom electrometry based on electromagnetically induced transparency (EIT) offers an optical approach for electric-field readout, but reliable readout of weak transient responses remains challenging under low signal-to-noise ratio (SNR) conditions. Here, we demonstrate machine-learning-enhanced readout of partial-discharge-like (PD-like) transient responses in a Cs 73 D 5/2 Rydberg-EIT vapor-cell system. Matched filtering (MF) and normalized cross-correlation (NCC) are used for candidate generation, followed by principal component analysis (PCA)/logistic-regression-based waveform-morphology discrimination and a cross-cycle temporal-consistency refinement. Across experimentally acquired low-SNR cycles, the PCA/logistic morphology selector achieved a strict localization accuracy of 63.25%, which increased to 86.32% after incorporating cross-cycle temporal consistency. The results indicate that weak Rydberg-EIT transient responses can retain discriminative local waveform morphology even when conventional template-peak ranking becomes unreliable. Overall, these results demonstrate that morphology-based machine-learning discrimination, complemented by temporal-consistency refinement, can improve the reliability of weak Rydberg-EIT transient readout under low-SNR conditions.

DOI
10.1364/oe.607219

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Rydberg-atom electrometry based on electromagnetically induced transparency (EIT) offers an optical approach for electric-field readout, but reliable readout of weak transient responses remains challenging under low signal-to-noise ratio (SNR) conditions. Here, we demonstrate machine-learning-enhanced readout of partial-discharge-like (PD-like) transient responses in a Cs 73 D 5/2 Rydberg-EIT vapor-cell system. Matched filtering (MF) and normalized cross-correlation (NCC) are used for candidate generation, followed by principal component analysis (PCA)/logistic-regression-based waveform-morphology discrimination and a cross-cycle temporal-consistency refinement. Across experimentally acquired low-SNR cycles, the PCA/logistic morphology selector achieved a strict localization accuracy of 63.25%, which increased to 86.32% after incorporating cross-cycle temporal consistency. The results indicate that weak Rydberg-EIT transient responses can retain discriminative local waveform morphology even when conventional template-peak ranking becomes unreliable. Overall, these results demonstrate that morphology-based machine-learning discrimination, complemented by temporal-consistency refinement, can improve the reliability of weak Rydberg-EIT transient readout under low-SNR conditions.
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.
The high polarizability of Rydberg atoms enables the multi-parameters measurement of electromagnetic fields. In this paper, we report on an atomic antenna based on Rydberg atoms in a room temperature vapor cell. The EIT is a destructive interference spectroscopy with a narrow linewidth and can be used to detect small electric fields through Autler-Townes splitting or Stark shifts. In our experiments, we employ cascade-type two-photon excitation electromagnetically induced transparency (EIT) spectroscopy to measure the shift of the Rydberg energy level. We introduce a low-frequency electric field (~kHz frequency) using a built-in electrode technique in the cesium cell. The interaction between the Rydberg atom and electric field induces the Stark shifts, where the amplitude of the electric field is converted into corresponding two-photon detuning by the EIT effect. Furthermore, the amplitude of the low-frequency electric field is converted into an intensity signal of EIT probe beam. Under weak field conditions, it is an approximate linear relationship between EIT transmission signal and input electric field amplitude, enabling measurement of waveform, amplitude, and frequency. We have demonstrated optical measurements of low-frequency electric field using Rydberg atoms. By increasing the power of probe beam and coupling beam, the EIT can increase the response bandwidth from ~MHz to hundreds of MHz. This provides a scalable approach for measuring high-frequency electric fields.
Partial discharge originates from microscopic insulation imperfections in high-voltage apparatus and is widely considered a critical marker of incipient deterioration. Conventional partial discharge detection methods are typically constrained by limited bandwidth and often rely on predefined feature extraction, which impedes reliable recognition of broadband transient signals. In this work, we employ a Rydberg atomic sensor to directly capture time-domain responses of partial discharge emissions and construct distinctive spectral fingerprints for different types. A 1D ResNet deep learning model is then applied to recognize these fingerprints from time-domain signals without manual feature engineering. Under increased source-antenna distances, where spectral features are significantly attenuated, the model attains a recognition accuracy of approximately 94\% across four partial discharge categories, demonstrating robustness to attenuation and noise. We further validate the approach in a simulated early-warning scenario, where partial discharge signals mixed with noise are analyzed and the model successfully generates predictive alarms. These results underscore the potential of integrating Rydberg-based broadband sensing with data-driven analysis for non-invasive, high-sensitivity diagnostics of electrical insulation systems.
Accurate detection of internal partial discharge (PD) in gas-insulated switchgear (GIS), a core component of modern power systems, is crucial for assessing insulation condition, ensuring equipment reliability, and maintaining power grid stability. However, the practical application of traditional ultra-high frequency (UHF) electromagnetic sensors in GIS condition-based maintenance faces significant limitations, including severe signal attenuation (20～40 dB/m) caused by the metal enclosure, the risk of SF6 leakage due to compromised airtightness with window-based installation, and a limited dynamic range (60～80 dB) insufficient for covering the full range of discharge magnitudes. To overcome these technical bottlenecks, this paper innovatively proposes a quantum-based UHF electric field sensing method utilizing the Rydberg atomic electromagnetically induced transparency (EIT) effect. The core advantages of this method lie in: leveraging the extremely high electric polarizability of highly excited cesium atoms (n=72D5/2) to achieve intrinsically safe measurement through an optical readout mechanism; establishing a stable EIT window using a dual-frequency laser system (852 nm probe light and 509 nm pump light) combined with precise frequency-locking technology; and designing a non-metallic atomic vapor cell antenna to realize the first near-field sensing of transient pulse electric fields in the 0.5～3 GHz band excited by particle discharge within GIS. This research demonstrates the capability of Rydberg atomic sensing technology to effectively detect discharges from a typical GIS defect (metal particles) for the first time. By employing quantum precision measurement, it provides a novel optical fiber sensing solution characterized by high sensitivity, broadband capability, and intrinsic safety for the online insulation monitoring of power equipment. This approach holds significant promise for advancing condition-based maintenance towards intelligent and precise evolution, offering key supporting technologies for the development of smart grids.
Rydberg-atom electrometry is a core technique in the development of highly sensitive quantum electric-field sensors. Its sensitivity based on atom-photon interaction is typically limited by photon shot-noise (PSN) and spectral broadenings. Here, we experimentally demonstrate a near PSN-limited Rydberg electrometry from a 85Rb atomic vapor cell. By engineering atomic coherence through control of residual magnetic fields and laser frequency noise, we achieve the Rydberg electromagnetically induced transparency (EIT) with the narrow linewidth of 1.6 MHz, yielding an enhanced spectral slope for high-sensitivity Rydberg-EIT electrometry. Under optimized superheterodyne detection conditions, we obtain an electric-field sensitivity of 12.5(8) nV cm^-1 Hz^-1/2 at 37 GHz, in close agreement with the calculated PSN limit. These results provide direct experimental evidence of the high-sensitive quantum electrometry and establish a practical route toward quantum-noise-limited Rydberg electrometry.
Abstract Partial discharge (PD) detection has garnered considerable attention due to its critical role in condition assessment and fault prediction of power system equipment. However, efficient and accurate acquisition of PD signals remains a significant technical challenge. Among mainstream technologies, the ultra-high frequency (UHF) detection method plays a key role in capturing PD signals. This work introduces a Rydberg atom-based electric field sensing approach for PD detection, employing a three-photon excitation scheme in cesium vapor. Four representative PD types are investigated: void, floating, particle, and corona, revealing the ability of the nonmetallic atomic sensor to capture their distinct time-domain characteristics and periodic behaviors at the power frequency. In addition, an antenna–waveguide–vapor cell coupling configuration is proposed. This electro-optical to opto-electronic conversion mechanism allows the sensor to achieve long-range detection while preserving favorable signal-to-noise ratios. Although the sensor’s limited instantaneous bandwidth leads to pulse broadening in high-speed signals, its theoretical high sensitivity and wide operating bandwidth highlight its potential as a complement to traditional metal-antenna solutions. These findings provide a robust foundation for implementing quantum-enhanced PD monitoring in practical power engineering scenarios.