A Neural Network Assisted $^{171}$Yb$^{+}$ Quantum Magnetometer

• URL: http://arxiv.org/abs/2203.05849v2
• Date: Fri, 30 Dec 2022 10:05:04 GMT
• Title: A Neural Network Assisted $^{171}$Yb$^{+}$ Quantum Magnetometer
• Authors: Yan Chen, Yue Ban, Ran He, Jin-Ming Cui, Yun-Feng Huang, Chuan-Feng Li, Guang-Can Guo, and Jorge Casanova
• Abstract summary: A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. We experimentally demonstrate that the combination of $171$Yb$+$ atomic sensors with adequately trained neural networks enables to investigate target fields in challenging scenarios.
• Score: 40.61445850211335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of $^{171}$Yb$^{+}$ atomic sensors with adequately trained neural networks enables to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses.

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