发表机构
Southern Methodist University（南卫理公会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对NISQ时代NV色心磁测量中测量诱导信息损失问题，将磁场传感转化为监督回归任务，对比经典与量子核模型性能，发现QML结合相干量子信息可显著提升传感性能，为实际场景下的量子传感提供了理论与实践依据。
AI 中文摘要
金刚石中的氮空位（NV）色心可作为高灵敏度固态量子传感器，用于高灵敏度磁测量。然而，在有噪声中等规模量子（NISQ）时代，从含噪声、有限 shots 及测量受限的传感数据中提取可靠信息仍是重大挑战。量子机器学习（QML）为通过学习量子传感数据与底层物理信号间的非线性关系来改进参数估计提供了潜在路径。本研究在受NV色心启发的磁测量场景中探究QML在磁场估计中的作用，将磁场传感表述为监督回归任务。我们对比了在基于测量的经典数据上训练的若干经典机器学习模型，与在测量前相干量子态上训练的量子核模型的性能，目的是分离测量诱导信息损失的影响，从而给出传感性能的理论上界，该上界仅当学习模型可直接获取相干量子信息时才能达到。结果表明，基于QML的传感性能随相干量子态信息显著提升，而随模型复杂度或学习范式变化的提升幅度不大。这一观察凸显了紧密整合量子传感器与QML模型的学习 pipeline 在实际约束下增强磁场传感的重要性。
英文摘要
Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extracting reliable information from noisy, finite-shot, and measurement-limited sensing data remains a considerable challenge. Whereas, quantum machine learning (QML) offers a potential path to improve parameter estimation by learning nonlinear relationships between quantum-sensing data and the underlying physical signal. In this work, we investigate the role of QML in magnetic-field estimation within an NV center-inspired magnetometry setting. We formulated magnetic field sensing as a supervised regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.
