{"created":"2023-05-15T15:01:59.951013+00:00","id":84092,"links":{},"metadata":{"_buckets":{"deposit":"7e15d6b6-fb84-4a7d-a80d-9862f2b1ef1b"},"_deposit":{"created_by":1,"id":"84092","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"84092"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00084092","sets":["10:29"]},"author_link":["1016172","1016173"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2021-12-08","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Machine learning algorithms specialized for neural data have allowed the extraction of information encoded in the brain. As an example, in previous studies, the images human subjects see were reconstructed from their brain activity measured via functional magnetic resonance imaging (fMRI) [1]. However, the application of those machine learning algorithms to high-resolution fMRI data, which may become mainstream in the near future, is limited due to their high computational cost. To solve this problem, scalable machine learning algorithms are being designed by utilizing computational techniques developed in the field of quantum computation [2,3]. In this report, taking one of the popular statistical methods, principal component analysis (PCA), as an example, we show that machine algorithms can be approximated with the use of such quantum-inspired techniques. The computational time and approximation performance of quantum-inspired PCA are demonstrated. The main results of this report have been presented in a previous paper by the author [3].\n\nReferences\n1G. Shen, T. Horikawa, K. Majima, Y. Kamitani, PLOS Computational Biology, Volume 15, e1006633 (2019).\n2E. Tang, Physical Review Letters, Volume 127, 060503 (2021).\n3N. Koide-Majima, K. Majima, Neural Networks, Volume 135, 55–67 (2021).","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"QUANTUM INNOVATION 2021","subitem_description_type":"Other"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kei, Majima"}],"nameIdentifiers":[{"nameIdentifier":"1016172","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kei, Majima","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1016173","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-03-28"}],"displaytype":"detail","filename":"ee2db7ef84247c08decc555bd565ce0b.pdf","filesize":[{"value":"233.3 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"1-page summary","url":"https://repo.qst.go.jp/record/84092/files/ee2db7ef84247c08decc555bd565ce0b.pdf"},"version_id":"19577969-60fe-4fc2-a4bb-cae2fd97a2c5"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"Quantum-inspired machine learning for exponentially big neural data analysis","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Quantum-inspired machine learning for exponentially big neural data analysis"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-11-23"},"publish_date":"2021-11-23","publish_status":"0","recid":"84092","relation_version_is_last":true,"title":["Quantum-inspired machine learning for exponentially big neural data analysis"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T18:41:28.447657+00:00"}