A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters.
Researchers have experimentally demonstrated that the combination of 171Yb+ atomic sensors with adequately trained neural networks enables to investigate target fields in distinct challenging scenarios.
In particular, they characterized 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 they significantly extended the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior.
These 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.
npj Quantum Information, Published online: 29 December 2022; doi:10.1038/s41534-022-00669-2
