In recent years, the Rydberg-atom sensor has attracted significant attention due to its high sensitivity, wide dynamic range, and miniaturization capabilities. However, noise from laser power fluctuations, frequency drift, and environment is introduced alongside the intrinsic quantum noise of Rydberg atoms, significantly degrading system performance for microwave sensing. This study tries to reduce the noise impact by combining the Rydberg-atom sensor with the convolutional neural network (CNN)-transformer model. The CNN module captures local temporal features of the signals, while the transformer module extracts global correlations within these features. We apply it to the following classification tasks of atomic microwave sensing: multifrequency microwave recognition, broadband spectrum monitoring, and automatic modulation recognition. We demonstrate that the CNN-transformer model achieves high accuracy across the three tasks and can effectively reduce the noise impact, which holds significant potential for applications in radar detection and wireless communication.
In recent years, the Rydberg-atom sensor has attracted significant attention due to its high sensitivity, wide dynamic range, and miniaturization capabilities. However, noise from laser power fluctuations, frequency drift, and environment is introduced alongside the intrinsic quantum noise of Rydberg atoms, significantly degrading system performance for microwave sensing. This study tries to reduce the noise impact by combining the Rydberg-atom sensor with the convolutional neural network (CNN)-transformer model. The CNN module captures local temporal features of the signals, while the transformer module extracts global correlations within these features. We apply it to the following classification tasks of atomic microwave sensing: multifrequency microwave recognition, broadband spectrum monitoring, and automatic modulation recognition. We demonstrate that the CNN-transformer model achieves high accuracy across the three tasks and can effectively reduce the noise impact, which holds significant potential for applications in radar detection and wireless communication.
Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.
Rydberg-atom sensors have emerged as a promising quantum technology for broadband radio-frequency detection owing to their intrinsic calibration, wide frequency coverage, and high sensitivity. However, their potential for intelligent identification of real-world wireless signals remains largely unexplored. Here, we demonstrate a quantum-enabled framework for mobile signal classification based on Rydberg electromagnetically induced transparency spectroscopy and deep learning. Three types of wireless emissions, including cellular voice call, Wi-Fi, and 5G signals from three commercial smartphone brands, are measured to construct a spectral-fingerprint dataset. A convolutional neural network trained on this dataset achieves an initial baseline classification accuracy of 93.44\%. To compensate for spectral drifts induced by temporal and environmental variations, a progressive fine-tuning strategy is employed to continuously adapt the pretrained model, enabling robust classification across diverse deployment scenarios. Furthermore, continuous real-time monitoring is demonstrated under dynamically varying wireless conditions, including cellular voice call, Wi-Fi, and their coexistence. The proposed system achieves an overall frame-level recognition accuracy of 87.67\% over a 60-s measurement. These results establish a pathway toward intelligent quantum radio-frequency sensing and highlight the potential of Rydberg-atom platforms for real-time wireless signal awareness and spectrum monitoring.
Rydberg atoms are atoms in their highly excited energy states. The physical properties of Rydberg atoms in the presence of external fields play an important role in quantum precision measurement. In recent years, radio-frequency electric field sensing based on Rydberg atoms has attracted significant interest. It is widely recognized as an electromagnetic sensing system fundamentally different from traditional ones. As a quantum sensor, it is expected to surpass several foundational limitations of traditional receivers, such as sensitivity, dynamic range and frequency bandwidth. It was reported that Rydberg sensing systems can sense microwaves of frequencies from DC up to 1THz. Its quantum nature would possibly avoid internal thermal (Johnson) noise, even at room temperature. Recent demonstrations have also observed high precision for terahertz imaging, strong fields sensing, proof-of-principle communication. However, limitations of this novel system have not been addressed, especially for a practical radar, communication or electronic warfare system. Here in this work, we first compare the architectures of different microwave receivers and then analyze the metrics of Rydberg microwave receivers, such as instantaneous bandwidth and dynamic range based on experimental results. We concluded by pointing out several main obstacles to overcome for realizing a practical state-of-the-art microwave receiver.
Highly sensitive detection of microwave (MW) fields throughout ultra-wideband spectrum is always an important subject in metrology, electromagnetism and cosmology. Rydberg atomic electrometers, which implement electric field measurements by detecting the disturbance of atomic energy levels, have the potentials to breakthrough the sensitivity and size limits of conventional sensors. However, due to the discrete structure of the energy levels, current measurements can only exhibit high sensitivity at the discontinuous frequencies which correspond to certain transitions. In this work, by implementing energy level modulation of Rydberg atom through Zeeman effect, we achieve continuous highly sensitive MW fields detection within 1-40 GHz ultra-wideband spectrum where the detection sensitivity is roundly below 65 nV cm-1 Hz-1/2, which is 2-3 orders of magnitude lower than that of the state-of-the-art continuous measurement architectures. The proposed method is expected to expand the working bandwidth from MHz to THz and therefore greatly accelerate the practical applications in the areas of radar and electronic reconnaissance. Detecting microwave fields across an ultra-wideband spectrum is crucial for advancements in metrology, electromagnetism, and cosmology. Here, the authors utilize Zeeman effect modulation of Rydberg atoms to achieve continuous, highly sensitive detection from 1-40 GHz, significantly enhancing sensitivity and potentially transforming radar and electronic reconnaissance applications.
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.
Broadband Rydberg Atomic Microwave Sensing with 44.6MHz Instantaneous Bandwidth
Rydberg atoms have become a promising novel type of microwave sensor due to their excellent physical properties -- broad frequency coverage and large electric dipole moments. High sensitivity and broad instantaneous bandwidth are two indispensable requirements for deployable Rydberg microwave sensors. However, enabling broadband operation while retaining high sensitivity has been a longstanding barrier limiting their applications. We propose and experimentally demonstrate a Rydberg microwave sensor whose instantaneous bandwidth is significantly enhanced via an auxiliary microwave field. By finely modulating the Rydberg energy levels with this field, we broaden the bandwidth substantially while retaining the sensor's inherent high sensitivity. An instantaneous bandwidth of 44.6MHz ($\pm22.3\,MHz)withasensitivityof225.7\,nV\,cm^{-1}\,Hz^{-1/2}$ is realized in a thermal 87Rb vapor with the local microwave frequency of 16.03GHz. Our work delivers concurrent broad instantaneous bandwidth and high sensitivity for Rydberg microwave sensors, paving a technically viable path for their practical deployment in broadband microwave metrology, radar, and wireless communication.