Nanodiamond quantum thermometry assisted with machine learning
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 wit...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Applied Physics Express |
| Subjects: | |
| Online Access: | https://doi.org/10.35848/1882-0786/adac2a |
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| _version_ | 1832086410597761024 |
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| author | Kouki Yamamoto Kensuke Ogawa Moeta Tsukamoto Yuto Ashida Kento Sasaki Kensuke Kobayashi |
| author_facet | Kouki Yamamoto Kensuke Ogawa Moeta Tsukamoto Yuto Ashida Kento Sasaki Kensuke Kobayashi |
| author_sort | Kouki Yamamoto |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-10d54f32c2a24a1d86b660e0dcb508b1 |
| institution | Kabale University |
| issn | 1882-0786 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Applied Physics Express |
| spelling | doaj-art-10d54f32c2a24a1d86b660e0dcb508b12025-02-06T15:49:12ZengIOP PublishingApplied Physics Express1882-07862025-01-0118202500110.35848/1882-0786/adac2aNanodiamond quantum thermometry assisted with machine learningKouki Yamamoto0https://orcid.org/0009-0005-1355-711XKensuke Ogawa1Moeta Tsukamoto2Yuto Ashida3Kento Sasaki4Kensuke Kobayashi5Department of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, Japan; Insititute for Physics of Intelligence, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanDepartment of Physics, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, Japan; Insititute for Physics of Intelligence, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, Japan; Trans-Scale Quantum Science Institute, The University of Tokyo , Bunkyo-ku, Tokyo 113-0033, JapanNanodiamonds (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.https://doi.org/10.35848/1882-0786/adac2aquantum sensornitrogen-vacancy centerthermometrymachine learning |
| spellingShingle | Kouki Yamamoto Kensuke Ogawa Moeta Tsukamoto Yuto Ashida Kento Sasaki Kensuke Kobayashi Nanodiamond quantum thermometry assisted with machine learning Applied Physics Express quantum sensor nitrogen-vacancy center thermometry machine learning |
| title | Nanodiamond quantum thermometry assisted with machine learning |
| title_full | Nanodiamond quantum thermometry assisted with machine learning |
| title_fullStr | Nanodiamond quantum thermometry assisted with machine learning |
| title_full_unstemmed | Nanodiamond quantum thermometry assisted with machine learning |
| title_short | Nanodiamond quantum thermometry assisted with machine learning |
| title_sort | nanodiamond quantum thermometry assisted with machine learning |
| topic | quantum sensor nitrogen-vacancy center thermometry machine learning |
| url | https://doi.org/10.35848/1882-0786/adac2a |
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