A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond
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2026
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| author | Yao, Changyu Shen, Haochen Liu, Zhongyuan Gong, Ruotian Kashem, Md Shakil Bin Varnum, Stella Li, Liangyu Li, Hangyue Yu, Yue Wang, Yizhou Lin, Xiaoshui Brestoff, Jonathan Lu, Chenyang Mukherji, Shankar Zhang, Chuanwei Zu, Chong |
| author_facet | Yao, Changyu Shen, Haochen Liu, Zhongyuan Gong, Ruotian Kashem, Md Shakil Bin Varnum, Stella Li, Liangyu Li, Hangyue Yu, Yue Wang, Yizhou Lin, Xiaoshui Brestoff, Jonathan Lu, Chenyang Mukherji, Shankar Zhang, Chuanwei Zu, Chong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14728 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond Yao, Changyu Shen, Haochen Liu, Zhongyuan Gong, Ruotian Kashem, Md Shakil Bin Varnum, Stella Li, Liangyu Li, Hangyue Yu, Yue Wang, Yizhou Lin, Xiaoshui Brestoff, Jonathan Lu, Chenyang Mukherji, Shankar Zhang, Chuanwei Zu, Chong Quantum Physics Applied Physics Computational Physics Data Analysis, Statistics and Probability 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. |
| title | A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond |
| topic | Quantum Physics Applied Physics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.14728 |