Fast Single Nitrogen-Vacancy Center Ramsey Characterization using a Physics-Informed Neural Network

TL;DR

This paper introduces NVRNet, a physics-informed neural network that significantly accelerates the characterization of single NV centers by denoising and analyzing minimal-sweep Ramsey data, enabling faster quantum sensing and materials research.

Contribution

The authors develop a novel machine learning pipeline combining physics-informed denoising and hyperfine parameter estimation, reducing data requirements and measurement time for NV center characterization.

Findings

Reduces median reconstruction error to 0.44-0.67 times experimental noise level.

Achieves FFT reconstruction errors of 0.10-0.19, faithfully reproducing experimental features.

Enables approximately 40-fold faster NV center hyperfine characterization.

Abstract

Precise characterization of the local spin environment of single diamond nitrogen-vacancy (NV) centers is crucial for advancing quantum sensing, quantum networking, and the optimization of quantum materials. However, single NV center fluorescence measurements requires long averaging times to obtain clean data that is suitable for conventional model fitting, and that constitutes a key experimental bottleneck for high-throughput characterization. To address this, we introduce \textsc{NVRNet}, a physics-informed simulation-to-reality machine learning pipeline that maps minimal-sweep, noisy Ramsey data to a denoised waveform while directly estimating the hyperfine coupling to proximal ${}^{13}\mathrm{C}$ nuclear spins. The pipeline's denoiser utilizes a two-stage time-frequency U-Net and an attention-augmented time-domain U-Net, pretrained on Hamiltonian-based spin-dynamics simulations with experimentally calibrated noise. To effectively bridge the simulation-to-reality gap, parameter-efficient adapters are attached to the backbone and fine-tuned on targeted experimental data. Across three distinct NV centers, this experimentally fine-tuned model reduces the median reconstruction error on held-out, few-sweep traces to $0.44\text{-}0.67\times$ of the raw experimental noise level. Subsequently, a transformer-based estimator extracts the underlying hyperfine parameters. Forward reconstructions derived from these inferred parameters faithfully reproduce the dominant experimental time- and frequency-domain features, yielding representative normalized fast Fourier transform (FFT) reconstruction errors of $0.10\text{-}0.19$. By reducing both the required data volume and acquisition time, \textsc{NVRNet} enables up to $\sim 40\times$ acceleration of the measurement process, establishing a fast, hardware-compatible pathway for robust hyperfine inference and autonomous qubit characterization.