Nanodiamond quantum thermometry assisted with machine learning

被引：0

作者：

Yamamoto, Kouki ^{[1
]}

Ogawa, Kensuke ^{[1
]}

Tsukamoto, Moeta ^{[1
]}

Ashida, Yuto ^{[1
,2
]}

Sasaki, Kento ^{[1
]}

Kobayashi, Kensuke ^{[1
,2
,3
]}

机构：

[1] Univ Tokyo, Dept Phys, Bunkyo Ku, Tokyo 1130033, Japan

[2] Univ Tokyo, Insititute Phys Intelligence, Bunkyo Ku, Tokyo 1130033, Japan

[3] Univ Tokyo, Transscale Quantum Sci Inst, Bunkyo Ku, Tokyo 1130033, Japan

来源：

APPLIED PHYSICS EXPRESS | 2025年 / 18卷 / 02期

基金：

日本学术振兴会;

关键词：

quantum sensor; nitrogen-vacancy center; thermometry; machine learning; NANOSCALE; SENSORS; CENTERS; SPIN;

D O I：

10.35848/1882-0786/adac2a

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Nanodiamonds (NDs) are quantum sensors that enable local temperature measurements, taking advantage of their small size. Though 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.

引用

页数：4

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