| Title: |
Nanodiamond quantum thermometry assisted with machine learning |
| Authors: |
Yamamoto, Kouki; Ogawa, Kensuke; Tsukamoto, Moeta; Ashida, Yuto; Sasaki, Kento; Kobayashi, Kensuke |
| Contributors: |
MERIT-WINGS, The University of Tokyo; Ministry of Education, Culture, Sports, Science and Technology; Cooperative Research Project of RIEC, Tohoku University; Mitsubishi Foundation; Kondo Memorial Foundation; JST SPRING; FoPM, WINGS Program, The University of Tokyo; Japan Society for the Promotion of Science; JST, CREST; JST FOREST Program; Daikin Industry Ltd. |
| Source: |
Applied Physics Express ; volume 18, issue 2, page 025001 ; ISSN 1882-0778 1882-0786 |
| Publisher Information: |
IOP Publishing |
| Publication Year: |
2025 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| DOI: |
10.35848/1882-0786/adac2a |
| DOI: |
10.35848/1882-0786/adac2a/pdf |
| Availability: |
https://doi.org/10.35848/1882-0786/adac2a; https://iopscience.iop.org/article/10.35848/1882-0786/adac2a; https://iopscience.iop.org/article/10.35848/1882-0786/adac2a/pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining |
| Accession Number: |
edsbas.C6191F9B |
| Database: |
BASE |