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.
| Comments: | 33 pages, 16 figures |
| Subjects: | Machine Learning (cs.LG); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2605.13988 [cs.LG] |
| (or arXiv:2605.13988v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13988 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tao Zhong [view email]
[v1]
Wed, 13 May 2026 18:02:34 UTC (4,449 KB)