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Quantum Machine Learning
Time-dependent atomic magnetometry with a recurrent neural network
arXiv
Authors: Maryam Khanahmadi, Klaus Mølmer
Year
2020
Paper ID
21939
Status
Preprint
Abstract Read
~2 min
Abstract Words
77
Citations
N/A
Abstract
We propose to employ a recurrent neural network to estimate a fluctuating magnetic field from continuous optical Faraday rotation measurement on an atomic ensemble. We show that an encoder-decoder architecture neural network can process measurement data and learn an accurate map between recorded signals and the time-dependent magnetic field. The performance of this method is comparable to Kalman filters while it is free of the theory assumptions that restrict their application to particular measurements and physical systems.
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- We propose to employ a recurrent neural network to estimate a fluctuating magnetic field from continuous optical Faraday rotation measurement on an atomic ensemble.
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