NVRNet: Deep Learning Model for Fast Nitrogen Vacancy Characterization under Room Temperature

• URL: http://arxiv.org/abs/2603.14144v1
• Date: Sat, 14 Mar 2026 22:20:57 GMT
• Title: NVRNet: Deep Learning Model for Fast Nitrogen Vacancy Characterization under Room Temperature
• Abstract summary: NVRNet is a physics-informed simulation-to-reality pipeline.<n>It maps a fast acquisition, noisy Ramsey photoluminescence trace to a denoised waveform.<n>It estimates hyperfine coupling to $13mathrmC$ spins in the environment.
• Score: 2.6383100880758663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Characterization of the local spin environment of single diamond nitrogen-vacancy centers is a critical task for quantum sensing, quantum networking, and diamond materials optimization. We introduce NVRNet, a physics-informed simulation-to-reality pipeline that maps a fast acquisition, noisy Ramsey photoluminescence (PL) trace to a denoised waveform as well as outputting a direct estimate of hyperfine coupling to ${}^{13}\mathrm{C}$ spins in the environment. The denoiser is a two-stage time-frequency UNet followed by an attention-augmented time-domain UNet, pretrained on Hamiltonian-based simulations with experimentally calibrated noise. The simulation-pretrained, experimentally fine-tuned denoiser reduces the median reconstruction error on held-out few-sweep experimental traces to $0.44$-$0.67\times$ that of the raw experimental noisy traces across the three NV centers. A transformer-based estimator trained on simulation labels then predicts hyperfine parameters, and forward reconstruction from the inferred parameters reproduces the dominant experimental time- and frequency-domain features, with representative normalized FFT reconstruction errors of 0.10-0.19. These results establish NVRNet as a fast, hardware-compatible route to hyperfine inference from minimal Ramsey data.

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