标题： 基于机器学习的纳米钻石量子测温技术 显示英文标题

标题： Nanodiamond quantum thermometry assisted with machine learning

摘要： 纳米金刚石（NDs）是一种量子传感器，能够利用其微小的尺寸进行局部温度测量。尽管基于模型的分析方法已被用于ND量子测温，但其准确性尚未得到彻底研究。 在这里，我们将无模型的机器学习与高斯过程回归（GPR）应用于ND量子测温，并将其能力与现有方法进行了比较。我们证明了即使对于少量数据点，GPR也能提供比它们更稳健的结果，且无论数据采集方法如何。 本研究通过机器学习扩展了ND量子测温的应用范围。 显示英文摘要

摘要： Nanodiamonds (NDs) are quantum sensors that enable local temperature measurements, taking advantage of their small size. Though the model based analysis methods have been used for ND quantum thermometry, their accuracy has yet to be thoroughly investigated. Here, we apply model-free machine learning with the Gaussian process regression (GPR) to ND quantum thermometry and compare its capabilities with the existing methods. We prove that GPR provides more robust results than them, even for a small number of data points and regardless of the data acquisition methods. This study extends the range of applications of ND quantum thermometry with machine learning.