arXiv cs.AI· Evan Zhang, Zhenghang Zhi, Zewei Wang, Zeheng Wang·· 14 小时前AI 评分18
从单中心到系综：面向 NV 光谱的校准符号回归流程
From Single Spins to Ensembles: A Calibrated Symbolic Regression Pipeline for NV Spectroscopy
AI 导读
研究者提出一套符号回归流程，将单中心自旋结构通过显式权重组合成校准后的 NV 系综光谱模型。在公开单中心与系综数据上，零场系综保频均方根误差从 0.008835 降至 0.003872（归一化光致发光），较未校正组合改善 56.2%。
正文 · 原文
Abstract:Characterizing nitrogen-vacancy (NV) ensembles requires connecting their collective spectra to individual centers that experience different local environments. We develop a symbolic-regression pipeline that carries single-center spin structure into a calibrated ensemble model by generating individual spectral responses and combining them with explicit weights. A constrained search selects Hermitian operator terms from single-center optically detected magnetic resonance spectra, fits their coefficients, and retains the selected expressions as source models for the ensemble. Ensemble calibration then accounts for resonance shifts, local disorder, orientation, and optical response while keeping the individual spectral contributions accessible. On public single-center and ensemble data, the zero-field ensemble held-frequency root-mean-square error decreases from 0.008835 to 0.003872 in normalized photoluminescence, a 56.2% improvement over matched uncorrected composition. In controlled QuTiP ensembles of 128 sites spanning four NV orientations, models calibrated at 0 and 3 mT predict an independently held-out 5.17 mT spectrum with errors of 0.000207-0.000228 across three random seeds, comparable to the 0.000200 noise standard deviation. Resolving the simulated response into its individual-center contributions shows how local spectral diversity combines into the ensemble line shape. By retaining the source Hamiltonians while calibrating sample-dependent spectral differences, the pipeline connects local spin physics to collective optical readout and makes the contributions to an ensemble line shape available for physical interpretation.
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35016 [quant-ph] |
| (or arXiv:2610.35016v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35016 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zeheng Wang [view email]
[v1]
Mon, 26 Oct 2026 08:29:07 UTC (2,542 KB)
来源：arXiv cs.AI · arxiv.org