量子传感中的非重复事件增强：基于传感器内量子储备计算
Quantum Sensing of Non-Repeatable Events Enhanced by In-Sensor Quantum Reservoir Computing
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- Chuo University（中央大学）
- The University of Electro-Communications（电气通信大学）
- The University of Tokyo（东京大学）
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中文总结 AI 辅助
本文提出基于NV色心的传感器内量子储备计算（NV-QRC），用于感知非重复事件，通过单次事件的多特征提取，在固定DD协议失效时仍能保留类依赖信息，实现有效分类。
中文摘要 AI 辅助
金刚石中的高密度氮-空位（NV）色心集合体能够实现灵敏的磁力测量。许多量子传感协议依赖于可重复的目标场，从而允许在不同传感条件下进行重复测量。在动力学解耦（DD）磁力测量中，通过扫描重复测量中π脉冲之间的间隔，可以估计未知交变场的频率和幅度。然而，对于非重复事件，相同的场波形无法在不同设置下重现以供测量。在此，我们提出基于NV的传感器内量子储备计算（NV-QRC）来感知此类事件。场驱动的多体动力学以及来自多个空间区域的同步荧光读出，为经典分类器提供了来自单个事件的多个特征。对于二元相位分类，我们使用预先固定的单一脉冲序列，将NV-QRC与DD磁力测量进行基准比较。我们表明，NV-QRC能够在固定DD协议无法捕获类依赖信息的机制中保留这些信息。这些结果将非重复事件传感确定为量子储备计算的一个有前景的应用。
英文摘要
High-density nitrogen-vacancy (NV) ensembles in diamond enable sensitive magnetometry. Many quantum-sensing protocols rely on reproducible target fields, allowing repeated measurements under different sensing conditions. In dynamical decoupling (DD) magnetometry, sweeping the interval between $π$ pulses across repeated measurements enables estimation of the frequency and amplitude of an unknown alternating field. For non-repeatable events, however, the same field waveform cannot be reproduced for measurements under different settings. Here we propose NV-based in-sensor quantum reservoir computing (NV-QRC) for sensing such events. Field-driven many-body dynamics and simultaneous fluorescence readout from multiple spatial regions provide a classical classifier with multiple features from a single event. For binary phase classification, we benchmark NV-QRC against DD magnetometry using a single pulse sequence fixed in advance. We show that NV-QRC can retain class-dependent information in regimes where the fixed-DD protocol fails to capture it. These results identify non-repeatable-event sensing as a promising application of quantum reservoir computing.
