用机器学习提升氮空位量子磁感的带宽与灵敏度平衡。
Machine-learning based high-bandwidth magnetic sensing
- 引入机器学习优化氮空位中心磁感应的信号处理流程。
- 在大动态范围下，数据点需求减少至少3倍，误差不变。
- 适合量子传感、量子计算领域研究者参考。
近年来，量子技术特别是量子传感发展迅速，氮空位（NV）色心在金刚石中展现出高灵敏度和高空间分辨率的磁传感能力。然而，现有的自旋共振磁传感方法在灵敏度、动态范围和带宽之间存在权衡。本文针对这一问题，引入机器学习工具以改善NV磁传感在大动态范围下的灵敏度与带宽权衡。结果表明，在保持当前误差水平的前提下，所需数据点数至少可减少3倍。该成果推动了量子机器学习协议在传感应用中的可行性与效率，为更实用高效的量子技术提供了新路径。
原文摘要 · Abstract (English)
Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-spatial-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.
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