Application of Machine Learning for High-Sensitivity ODMR Signal Processing

Optically Detected Magnetic Resonance (ODMR) based on nitrogen-vacancy (NV) centers in diamond has emerged as a powerful platform for quantum sensing owing to its exceptional sensitivity to magnetic, electric, and thermal fields. Recent advances in artificial neural networks (ANNs) further enable intelligent signal processing and noise suppression, significantly enhancing sensor robustness under realistic operating conditions. In this work, we present both theoretical modeling and experimental realization of an ANN-integrated ODMR magnetometer. The ANN framework is employed to optimize signal extraction, compensate for experimental drift, and reduce calibration requirements. This hybrid approach demonstrates improved stability and performance in noisy environments, advancing the development of compact, field-deployable quantum sensors with enhanced precision and adaptability.