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arXiv 2603.14728quant-phphysics.app-phphysics.comp-phphysics.data-an
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基于深度学习的金刚石氮空位中心量子传感增强框架

A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond

Washington University, St. Louis（华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Washington University McKelvey School of Engineering（华盛顿大学麦凯尔维工程学院）
Changyu Yao, Haochen Shen, Zhongyuan Liu, Ruotian Gong, Md Shakil Bin Kashem, Stella Varnum, Liangyu Li, Hangyue Li, Yue Yu, Yizhou Wang, Xiaoshui Lin, Jonathan… 展开作者

Changyu Yao, Haochen Shen, Zhongyuan Liu, Ruotian Gong, Md Shakil Bin Kashem, Stella Varnum, Liangyu Li, Hangyue Li, Yue Yu, Yizhou Wang, Xiaoshui Lin, Jonathan Brestoff, Chenyang Lu, Shankar Mukherji, Chuanwei Zhang, Chong Zu

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Main text contains: 9 pages, 4 figures. Includes supplementary material
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AI总结：

本文提出一种基于一维卷积神经网络的深度学习框架，用于实时分析光学检测磁共振谱，提升低信噪比下的参数提取效率与鲁棒性，应用于细胞内温度监测和高温超导体磁成像。

摘要：

金刚石中的氮空位（NV）中心是一种多功能的量子传感平台，可用于高灵敏度测量磁场、温度和应变，并具有纳米级空间分辨率。一个常见的瓶颈是光学检测磁共振（ODMR）谱的分析，其中目标量编码在共振特征中。传统非线性拟合通常计算成本高、对初始化敏感且在低信噪比（SNR）下容易失败。本文介绍了一种基于一维卷积神经网络（1D-CNN）的鲁棒且高效的机器学习（ML）框架，用于实时ODMR分析。该模型可直接进行参数推断，无需初始猜测或迭代优化，并且可以自然地在图形处理单元（GPU）上并行处理以实现高通量处理。我们在合成和实验数据集上验证了该方法，显示出比标准非线性拟合更高的吞吐量、准确性和鲁棒性，特别是在低SNR情况下有最大的改进。我们进一步在两个代表性传感应用中验证了我们的方法：使用纳米钻石探针诊断细胞内温度变化和高温超导体中超导涡旋的宽场磁成像。这种深度学习推断框架能够快速可靠地从复杂的ODMR数据中提取物理参数，并提供了一条可扩展的实时量子传感和成像的途径。

英文摘要：

Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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