Nanodiamond quantum thermometry assisted with machine learning
Abstract 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.
Authors
- Kouki Yamamoto
- Moeta Tsukamoto (ORCID: https://orcid.org/0000-0002-0708-6308)
- Kento Sasaki (ORCID: https://orcid.org/0000-0002-5880-2116)
- Yuto Ashida (ORCID: https://orcid.org/0000-0002-9812-0320)
- Kensuke Kobayashi (ORCID: https://orcid.org/0000-0003-1748-5799)
- Kensuke Ogawa (ORCID: https://orcid.org/0000-0003-0881-9160)
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- Applied Physics Express
- Published
- 2025-02-01
- DOI
- https://doi.org/10.35848/1882-0786/adac2a
- Citations
- 1
- Primary Topic
- Diamond and Carbon-based Materials Research
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- Ministry of Education, Culture, Sports, Science and Technology
- Mitsubishi Foundation
- University of Tokyo
- Japan Society for the Promotion of Science
- Research Institute of Electrical Communication, Tohoku University
- Core Research for Evolutional Science and Technology