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.
Cite
@article{arxiv.2504.07582,
title = {Nanodiamond quantum thermometry assisted with machine learning},
author = {Kouki Yamamoto and Kensuke Ogawa and Moeta Tsukamoto and Yuto Ashida and Kento Sasaki and Kensuke Kobayashi},
journal= {arXiv preprint arXiv:2504.07582},
year = {2025}
}