A Neural Network Assisted 171Yb+ Quantum Magnetometer

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 171Yb+ atomic sensors with adequately trained neural networks enables the characterization of 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.