{"created":"2025-04-08T04:39:16.771855+00:00","id":2005226,"links":{},"metadata":{"_buckets":{"deposit":"899646b0-36d3-442a-a94b-c3bfcccc2196"},"_deposit":{"created_by":133,"id":"2005226","owners":[133],"pid":{"revision_id":0,"type":"depid","value":"2005226"},"status":"published"},"_oai":{"id":"oai:teapot.lib.ocha.ac.jp:02005226","sets":["1728029753363"]},"author_link":[],"control_number":"2005226","item_30002_access_rights4":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_30002_creator2":{"attribute_name":"作成者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Ochanomizu University","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Asaoka, Hinako","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Ochanomizu University","affiliationNameLang":"en"}]},{"affiliationNames":[{"affiliationName":"Tohoku University","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Kudo, Kazue","creatorNameLang":"en"}]}]},"item_30002_date11":{"attribute_name":"日付","attribute_value_mlt":[{"subitem_date_issued_datetime":"2023-10-02","subitem_date_issued_type":"Issued"}]},"item_30002_description9":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_30002_file35":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2025-04-08"}],"filename":"s41598-023-43729-z.pdf","filesize":[{"value":"2.7 MB"}],"format":"application/pdf","licensetype":"license_0","mimetype":"application/pdf","url":{"url":"https://teapot.lib.ocha.ac.jp/record/2005226/files/s41598-023-43729-z.pdf"},"version_id":"e884a250-8b9d-49e7-81f3-922e6ca4131e"},{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2025-04-08"}],"filename":"https://github.com/asaocha/NBMF-classification","url":{"url":"https://github.com/asaocha/NBMF-classification"},"version_id":""}]},"item_30002_funding_reference21":{"attribute_name":"助成情報","attribute_value_mlt":[{"subitem_award_numbers":{"subitem_award_number":"23H04499","subitem_award_number_type":"JGN","subitem_award_uri":"https://kaken.nii.ac.jp/ja/grant/KAKENHI-PUBLICLY-23H04499/"},"subitem_award_titles":[{"subitem_award_title":"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","subitem_award_title_language":"ja"},{"subitem_award_title":"Unraveling factors that determine the difficulty of learning in quantum machine learning","subitem_award_title_language":"en"}],"subitem_funder_identifiers":{"subitem_funder_identifier_type":"Crossref Funder","subitem_funder_identifier_type_uri":"https://doi.org/10.13039/501100001691"},"subitem_funder_names":[{"subitem_funder_name":"日本学術振興会（JSPS）","subitem_funder_name_language":"ja"},{"subitem_funder_name":"Japan Society for the Promotion of Science","subitem_funder_name_language":"en"}],"subitem_funding_stream_identifiers":{"subitem_funding_stream_identifier_type":"JGN_fundingStream"},"subitem_funding_streams":[{"subitem_funding_stream":"科学研究費助成事業","subitem_funding_stream_language":"ja"}]}]},"item_30002_language12":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_30002_page_start27":{"attribute_name":"開始ページ","attribute_value_mlt":[{"subitem_start_page":"16527"}]},"item_30002_publisher10":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Springer Nature","subitem_publisher_language":"en"}]},"item_30002_relation18":{"attribute_name":"関連情報","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1038/s41598-023-43729-z","subitem_relation_type_select":"DOI"}}]},"item_30002_resource_type13":{"attribute_name":"item_30002_resource_type13","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_30002_rights6":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"This article is licensed under a Creative Commons Attribution 4.0 International License","subitem_rights_language":"en","subitem_rights_resource":"https://creativecommons.org/licenses/by/4.0/"},{"subitem_rights":"© The Author(s) 2023","subitem_rights_language":"en"}]},"item_30002_source_identifier22":{"attribute_name":"収録物識別子","attribute_value_mlt":[{"subitem_source_identifier":"2045-2322","subitem_source_identifier_type":"EISSN"}]},"item_30002_source_title23":{"attribute_name":"収録物名","attribute_value_mlt":[{"subitem_source_title":"Scientific Reports"}]},"item_30002_title0":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Nonnegative/Binary matrix factorization for image classification using quantum annealing","subitem_title_language":"en"}]},"item_30002_version_type15":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_30002_volume_number24":{"attribute_name":"巻","attribute_value_mlt":[{"subitem_volume":"13"}]},"item_title":"Nonnegative/Binary matrix factorization for image classification using quantum annealing","item_type_id":"40034","owner":"133","path":["1728029753363"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-04-08"},"publish_date":"2025-04-08","publish_status":"0","recid":"2005226","relation_version_is_last":true,"title":["Nonnegative/Binary matrix factorization for image classification using quantum annealing"],"weko_creator_id":"133","weko_shared_id":-1},"updated":"2025-04-14T04:37:20.074635+00:00"}