Neural Fields for NV-Center Inverse Sensing

TL;DR

This paper introduces NeTMY, a neural inverse sensing method for NV centers that improves localization accuracy by addressing failure modes in traditional models, demonstrating effectiveness in benchmark tests.

Contribution

The paper presents NeTMY, a novel neural field approach that incorporates a differentiable NV forward model with advanced optimization techniques for improved inverse sensing.

Findings

NeTMY outperforms existing methods in localization and distribution metrics.

Replacing scalar approximations with tensor operators reveals failure modes in inverse models.

NeTMY's parameterization mitigates center-collapse failures in sparse reconstructions.

Abstract

Inverse problems in scientific sensing are often solved with either hand-designed regularizers or supervised networks trained on simulated labels, yet both can fail when the forward model is nonlinear, spectrally coupled, and physically delicate. We study this issue for noise sensing based on nitrogen-vacancy (NV) centers in diamond, where a quantum sensor measures magnetic-noise spectra generated by sparse spin sources. We show that replacing a common scalar/coherent forward approximation with a tensor power-summed dipolar operator changes the inverse landscape and exposes a center-collapse failure mode in free-density optimization. We propose NeTMY, an amortization-free coordinate neural field coupled to the differentiable NV forward model, with annealed positional encoding, multiscale optimization, sparsity/gating, and spectrum-fidelity losses. Across sparse synthetic reconstructions generated by the corrected operator, NeTMY achieves the best localization and distributional metrics in the tested benchmark. Mechanism experiments show that NeTMY does not directly execute the raw density-space gradient; its parameterization smooths and redistributes updates, mitigating the center-collapse pathology. These results position NV quantum sensing as a useful testbed for physics-faithful neural inverse problems.