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

TL;DR

This paper demonstrates that combining $^{171}$Yb$^{+}$ atomic sensors with neural networks enhances quantum magnetometry, enabling accurate detection of RF fields under challenging noise conditions and non-harmonic responses.

Contribution

The study introduces a neural network approach to extend the capabilities of $^{171}$Yb$^{+}$ quantum magnetometers in noisy and complex response scenarios.

Findings

Neural networks improve RF field characterization under shot noise.

Extended magnetometer operation beyond harmonic response regimes.

Effective data processing for quantum sensing applications.

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.