{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218761","sets":["1164:10193:10905:10966"]},"path":["10966"],"owner":"44499","recid":"218761","title":["Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-30"},"_buckets":{"deposit":"55afb544-7349-4e2c-ad2a-9715abd566d2"},"_deposit":{"id":"218761","pid":{"type":"depid","value":"218761","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","author_link":["569600","569602","569597","569599","569598","569601"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network"},{"subitem_title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering Science, Osaka University"},{"subitem_text_value":"Graduate School of Engineering Science, Osaka University／Center for Quantum Information and Quantum Biology, Osaka University／JST, PRESTO"},{"subitem_text_value":"Graduate School of Engineering Science, Osaka University／Center for Quantum Information and Quantum Biology, Osaka University／RIKEN Center for Quantum Computing"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering Science, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Science, Osaka University / Center for Quantum Information and Quantum Biology, Osaka University / JST, PRESTO","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering Science, Osaka University / Center for Quantum Information and Quantum Biology, Osaka University / RIKEN Center for Quantum Computing","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/218761/files/IPSJ-QS22006002.pdf","label":"IPSJ-QS22006002.pdf"},"date":[{"dateType":"Available","dateValue":"2024-06-30"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-QS22006002.pdf","filesize":[{"value":"4.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"53"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4088727c-7f63-477c-9d26-85f44e1da60b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yoshiaki, Kawase"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kosuke, Mitarai"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keisuke, Fujii"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yoshiaki, Kawase","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kosuke, Mitarai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keisuke, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12894105","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2435-6492","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"量子ソフトウェア（QS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2022-QS-6"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218761,"updated":"2025-01-19T15:02:41.733058+00:00","links":{},"created":"2025-01-19T01:19:06.547485+00:00"}