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Nonnegative/Binary matrix factorization for image classification using quantum annealing
http://hdl.handle.net/10083/0002005226
http://hdl.handle.net/10083/0002005226b572f6a7-babe-425a-bda7-0a8c7ba8ec4a
| 名前 / ファイル | ライセンス | アクション |
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s41598-023-43729-z.pdf (2.7 MB)
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https://github.com/asaocha/NBMF-classification
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| Item type | アイテムタイプ（フル）(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-04-08 | |||||||||
| タイトル | ||||||||||
| タイトル | Nonnegative/Binary matrix factorization for image classification using quantum annealing | |||||||||
| 言語 | en | |||||||||
| 作成者 |
Asaoka, Hinako
× Asaoka, Hinako
× Kudo, Kazue
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| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 権利情報 | ||||||||||
| 言語 | en | |||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | This article is licensed under a Creative Commons Attribution 4.0 International License | |||||||||
| 権利情報 | ||||||||||
| 言語 | en | |||||||||
| 権利情報 | © The Author(s) 2023 | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Classical computing has borne witness to the development of machine learning. The integration of quantum technology into this mix will lead to unimaginable benefits and be regarded as a giant leap forward in mankind’s ability to compute. Demonstrating the benefits of this integration now becomes essential. With the advance of quantum computing, several machine-learning techniques have been proposed that use quantum annealing. In this study, we implement a matrix factorization method using quantum annealing for image classification and compare the performance with traditional machine-learning methods. Nonnegative/binary matrix factorization (NBMF) was originally introduced as a generative model, and we propose a multiclass classification model as an application. We extract the features of handwritten digit images using NBMF and apply them to solve the classification problem. Our findings show that when the amount of data, features, and epochs is small, the accuracy of models trained by NBMF is superior to classical machine-learning methods, such as neural networks. Moreover, we found that training models using a quantum annealing solver significantly reduces computation time. Under certain conditions, there is a benefit to using quantum annealing technology with machine learning. | |||||||||
| 言語 | en | |||||||||
| 出版者 | ||||||||||
| 出版者 | Springer Nature | |||||||||
| 言語 | en | |||||||||
| 日付 | ||||||||||
| 日付 | 2023-10-02 | |||||||||
| 日付タイプ | Issued | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 出版タイプ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
| 関連情報 | ||||||||||
| 関連タイプ | isIdenticalTo | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-023-43729-z | |||||||||
| 助成情報 | ||||||||||
| 助成機関識別子タイプ | Crossref Funder | |||||||||
| 助成機関識別子タイプURI | https://doi.org/10.13039/501100001691 | |||||||||
| 助成機関名 | 日本学術振興会（JSPS） | |||||||||
| 言語 | ja | |||||||||
| 助成機関名 | Japan Society for the Promotion of Science | |||||||||
| 言語 | en | |||||||||
| プログラム情報識別子タイプ | JGN_fundingStream | |||||||||
| 言語 | ja | |||||||||
| プログラム情報 | 科学研究費助成事業 | |||||||||
| 研究課題番号URI | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PUBLICLY-23H04499/ | |||||||||
| 研究課題番号 | 23H04499 | |||||||||
| 研究課題番号タイプ | JGN | |||||||||
| 研究課題名 | 量子機械学習における学習の難しさを決定づける要素の解明 | |||||||||
| 言語 | ja | |||||||||
| 研究課題名 | Unraveling factors that determine the difficulty of learning in quantum machine learning | |||||||||
| 言語 | en | |||||||||
| 収録物識別子 | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 2045-2322 | |||||||||
| 収録物名 | ||||||||||
| 収録物名 | Scientific Reports | |||||||||
| 巻 | ||||||||||
| 巻 | 13 | |||||||||
| 開始ページ | ||||||||||
| 開始ページ | 16527 | |||||||||
