{"created":"2025-01-19T01:37:02.799860+00:00","updated":"2025-01-19T09:37:21.281925+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00235057","sets":["1164:10193:11470:11670"]},"path":["11670"],"owner":"44499","recid":"235057","title":["再帰的量子緩和による最大カット手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-20"},"_buckets":{"deposit":"343a2258-71d4-4a13-8b31-be6437eb2403"},"_deposit":{"id":"235057","pid":{"type":"depid","value":"235057","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"再帰的量子緩和による最大カット手法","author_link":["641748","641749","641746","641747"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"再帰的量子緩和による最大カット手法"},{"subitem_title":"Recursive Quantum Relaxation for MAX-CUT Problem","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社豊田中央研究所／慶應義塾大学量子コンピューティングセンター"},{"subitem_text_value":"株式会社豊田中央研究所／慶應義塾大学量子コンピューティングセンター"},{"subitem_text_value":"東京大学工学部情報理工学系研究科／慶應義塾大学量子コンピューティングセンター"},{"subitem_text_value":"慶應義塾大学物理情報工学科／慶應義塾大学量子コンピューティングセンター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Toyota Central R&D Labs., Inc. / Quantum Computing Center, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Toyota Central R&D Labs., Inc. / Quantum Computing Center, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science, The University of Tokyo / Quantum Computing Center, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Department of Applied Physics and Physico-Informatics, Keio University / Quantum Computing Center, Keio University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/235057/files/IPSJ-QS24012010.pdf","label":"IPSJ-QS24012010.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-QS24012010.pdf","filesize":[{"value":"1.0 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":"cffbbc6a-c683-4cab-ac7c-f6294320c1cd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"近藤, 瑠歩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤, 勇気"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"ルディー, レイモンド"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 直樹"}],"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":"最大カット問題を近似的に解く量子ヒューリスティックである Recursive QAOA と Quantum Random Access Optimization (QRAO) を組み合わせた新たな量子ヒューリスティックを提案した．また，提案法を含むこれらの手法がいずれも同一のフレームワークに統一できることを示した．テンソルネットワークを用いた数値実験を 800 ノードのグラフデータセットに対して実施したところ，Recursive QAOA，QRAO，古典近似アルゴリズムの Goemans-Williamson 法のいずれよりも提案手法の方が高いカット重みが得られた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We proposed a quantum algorithm for the MAX-CUT problem combining the existing two quantum heuristics, Recursive QAOA and Quantum Random Access Optimization. We also showed that all these methods including proposed can be unified into a framework that finds the binary solution that is most likely measured from the optimal quantum state. Experiments on standard benchmark graphs with several hundred nodes in the MAX-CUT problem, conducted in a fully classical manner using a tensor network technique, show that our proposed model outperforms the Goemans-Williamson method.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告量子ソフトウェア（QS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2024-QS-12"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":235057,"links":{}}