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.
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.
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.
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.
Microwave electric field (MW E-field) sensing is important for a wide range of applications in the areas of remote sensing, radar astronomy and communications. Over the past decade, Rydberg atoms have been used in ultrasensitive, wide broadband, traceable, stealthy MW E-field sensing because of their exaggerated response to MW E-fields, plentiful optional energy levels and integratable preparation methods. This review first introduces the basic concepts of quantum sensing, the properties of Rydberg atoms and the principles of quantum sensing of MW E-fields with Rydberg atoms. An overview of this very active research direction is gradually expanding, covering the progress of sensitivity and bandwidth in Rydberg atom-based microwave sensing, superheterodyne quantum sensing with microwave-dressed Rydberg atoms, quantum-enhanced sensing of MW E-field and recent advanced quantum measurement systems and approaches to further improve the performance of MW E-field sensing. Finally, a brief outlook on future development directions is provided.
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.
Abstract Developing microwave electric field sensing based on Rydberg atoms has received significant attention due to its unique advantages. However, achieving effective coupling between Rydberg atoms and the microwave electric field in the sensing process is a challenging problem that greatly impacts the sensitivity. To address this, we propose using a microwave resonant cavity to enhance the effective coupling between the Rydberg atoms and the microwave electric field. In our experiment, Rydberg atoms are prepared via a three-photon excitation scheme, and the electric fields are measured without and with a microwave cavity in which the vapor cell is placed inside, respectively. As a result, we achieved an 18 dB enhancement of power sensitivity by adding the cavity, which is an effective enhancement in electric field pulse signal detection. This experimental testing provides a promising direction for enhancing the sensitivity of Rydberg atomic electric field sensors and paves the way for their application in precision electric field measurements.
We have shown that Rydberg states can be used for high-sensitivity, absolute sensing of microwave (MW) electric fields. We achieved a sensitivity of 3 μVcm-1Hz-1/2 for two read-out strategies. Depending on the spectral resolution of the read-out, either the MW induced transmission line frequency splitting, the Autler-Townes regime, or a change in the on-resonant absorption, the amplitude regime, can be used to determine the MW electric field. Results using a Mach-Zehnder interferometer and frequency modulated spectroscopy both achieve similar photon shot noise limited sensitivity. In addition, we have also explored amplitude modulation and the displacement of a probe laser beam due to index of refraction changes in a prism shaped vapor cell. These latter methods were not able to achieve photon shot noise limited performance. Fundamental limits to the sensitivity of the Rydberg atom-based MW electric field sensing have been addressed, but it is important to clarify the differences between noise in different parts or subsystems of the sensor. Shot noise in the probe laser usually dominates the projection noise of the atoms participating in the measurement of the MW electric field because of the desire to operate at low effective Rydberg atom densities in order to avoid collisional dephasing and ionization.