Neural Fields for NV-Center Inverse Sensing

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. On a 512-sample benchmark generated by a direct source-side simulator and inverted under the tensor operator, NeTMY achieves the best localization and distributional metrics. Mechanism experiments show that NeTMY's parameterization filters the raw density-space gradient and avoids the center collapse reached by free-density solvers. These results position NV quantum sensing as a testbed for physics-faithful neural inverse problems.