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Rydberg atom and neural network achieve multi-frequency microwave precision detection
2022/4/22
Science and Technology Daily News (reporter Wu Changfeng) On April 17, the Science and Technology Daily reporter learned from the University of Science and Technology of China that the school's Guo Guangcan Academy team Shi Baosen and Ding Dongsheng's research group used artificial intelligence to achieve precision detection based on Rydberg atomic multi-frequency microwave, and the relevant results were recently published in the international journal Nature Communications.
Rydberg atom has a large electric dipole moment, can produce a strong response to weak electric field, so as a very promising microwave measurement system has been favored by people, and has made rapid development. However, there are still many scientific problems to be solved in microwave measurement based on Rydberg atoms, and multi-frequency microwave reception is one of them: this is because multi-frequency microwaves can cause complex interference patterns in atoms, which seriously interferes with signal reception and recognition.
Based on the previous work, the researchers used Rydberg atoms as microwave antennas and modems based on the rubidium atom system at room temperature to successfully detect the phase-modulated multi-frequency microwave field through electromagnetic induced transparency effect, and then analyzed the received modulated signal through deep learning neural network to achieve high-fidelity demodulation of multi-frequency microwave signals. The robustness of the experimental scheme against microwave noise is further tested.
The research results show that the Rydberg microwave receiver based on deep learning enhancement can allow direct decoding of 20 frequency division multiplexing (FDM) signals at once, without the need for multiple bandpass filters and other complex circuits. The innovation of this work is that a scheme is proposed and implemented to effectively detect multifrequency microwave electric fields without solving the main equation, taking advantage of the sensitivity of Rydberg atoms while reducing the impact of noise.
The research results provide an important reference for the cross combination of precision measurement and neural network. In addition, the results can also be applied to detect multiple targets simultaneously. The reviewers spoke highly of the result: "The results presented in this work are very useful to other researchers in the field of atomic and molecular photophysics, as it shows the future application of deep learning in quantum-enhanced sensing of atomic systems."
Source: Science and Technology Daily (April 21, 2022)
http://digitalpaper.stdaily.com/http_www.kjrb.com/kjrb/html/2022-04/21/content_533952.htm?div=-1
Rydberg atom has a large electric dipole moment, can produce a strong response to weak electric field, so as a very promising microwave measurement system has been favored by people, and has made rapid development. However, there are still many scientific problems to be solved in microwave measurement based on Rydberg atoms, and multi-frequency microwave reception is one of them: this is because multi-frequency microwaves can cause complex interference patterns in atoms, which seriously interferes with signal reception and recognition.
Based on the previous work, the researchers used Rydberg atoms as microwave antennas and modems based on the rubidium atom system at room temperature to successfully detect the phase-modulated multi-frequency microwave field through electromagnetic induced transparency effect, and then analyzed the received modulated signal through deep learning neural network to achieve high-fidelity demodulation of multi-frequency microwave signals. The robustness of the experimental scheme against microwave noise is further tested.
The research results show that the Rydberg microwave receiver based on deep learning enhancement can allow direct decoding of 20 frequency division multiplexing (FDM) signals at once, without the need for multiple bandpass filters and other complex circuits. The innovation of this work is that a scheme is proposed and implemented to effectively detect multifrequency microwave electric fields without solving the main equation, taking advantage of the sensitivity of Rydberg atoms while reducing the impact of noise.
The research results provide an important reference for the cross combination of precision measurement and neural network. In addition, the results can also be applied to detect multiple targets simultaneously. The reviewers spoke highly of the result: "The results presented in this work are very useful to other researchers in the field of atomic and molecular photophysics, as it shows the future application of deep learning in quantum-enhanced sensing of atomic systems."
Source: Science and Technology Daily (April 21, 2022)
http://digitalpaper.stdaily.com/http_www.kjrb.com/kjrb/html/2022-04/21/content_533952.htm?div=-1
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