Physics-guided machine learning for sim-to-real calibration of NV diamond magnetometers

Abstract:Ensemble nitrogen-vacancy (NV) centers in diamond enable robust vector magnetometry in unshielded environments, yet deployment remains bottlenecked by complex calibration and a reliance on external data references. Conventional statistical machine learning requires an exorbitantly large volume of training data and suffers from severe simulation-to-reality mismatches. To address this, we introduce a physics-guided hybrid machine learning framework that embeds the Zeeman splitting directly into the learning pipeline. Our physics-guided model significantly reduces the average tracking error demonstrating a 372-fold precision improvement over purely statistical baselines. Furthermore, our hybrid architecture pairs a sparse physical measurement with scalable synthetic data generation, seamlessly incorporating real-world hardware non-idealities. When deployed to decode uncalibrated, raw experimental ODMR data, our framework delivers exceptional predictive accuracy for the scalar magnetic field. This work paves the way toward self-calibrated sensors while establishing a machine learning training method applicable to other data-scarce physical systems

Quantum Physics (quant-ph); Materials Science (cond-mat.mtrl-sci); Applied Physics (physics.app-ph)

arXiv:2608.19582 [quant-ph]

(or arXiv:2608.19582v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2608.19582 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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