Phase-Based Approaches for Rapid Construction of Magnetic Fields in NV Magnetometry

TL;DR

This paper introduces phase-based computational methods for rapid magnetic field estimation in NV magnetometry, significantly reducing processing time compared to traditional Lorentzian fitting techniques.

Contribution

It proposes efficient phase estimation approaches using Fourier Transform, filtering, and ESPRIT for NV center-based magnetometry, improving speed and computational efficiency.

Findings

Significant reduction in computational time

Effective magnetic field mapping from ODMR signals

Validation with experimental data

Abstract

With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software is becoming integral parts of these emerging sensing systems to reap the benefits of this progress. This paper looks into widefield Nitrogen Vacancy Center-based magnetometry and focuses on estimating the magnetic field from the Optically Detected Magnetic Resonances (ODMR) signal, a crucial output for various applications. Mapping the shifts of ODMR signals to phase estimation, a computationally efficient approaches are proposed. Involving Fourier Transform and Filtering as pre-processing steps, the suggested approaches involve linear curve fit or complex frequency estimation based on well-known super-resolution technique Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT). The existing methods in the quantum sensing literature take different routes based on Lorentzian fitting for determining magnetic field maps. To showcase the functionality and effectiveness of the suggested techniques, relevant results, based on experimental data are provided, which shows a significant reduction in computational time with the proposed method over existing methods