{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233514","sets":["1164:2822:11469:11529"]},"path":["11529"],"owner":"44499","recid":"233514","title":["NISQデバイスを用いた量子ニューラルネットワークにおける量子回路の構成と学習性能の評価\\n"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-14"},"_buckets":{"deposit":"d203dba2-95ad-41de-85a3-41fdb47fc8a8"},"_deposit":{"id":"233514","pid":{"type":"depid","value":"233514","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"NISQデバイスを用いた量子ニューラルネットワークにおける量子回路の構成と学習性能の評価\\n","author_link":["634458","634462","634463","634457","634456","634460","634461","634459"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"NISQデバイスを用いた量子ニューラルネットワークにおける量子回路の構成と学習性能の評価\\n"},{"subitem_title":"Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-03-14","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"明星大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Meisei University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda 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/233514/files/IPSJ-EMB24065037.pdf","label":"IPSJ-EMB24065037.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-EMB24065037.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"42"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"4214278c-867f-4810-917d-b7a2f23d15eb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"丸茂, 直樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"和田, 康孝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"上田, 和紀"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"木村, 啓二"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naoki, Marumo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasutaka, Wada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazunori, Ueda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keiji, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12149313","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":"2188-868X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"変分量子アルゴリズム（Variational Quantum Algorithm: VQA）による量子機械学習では，学習モデルを構成する部分（Ansatz）の反復回数を多くするほど，そして多くの量子ビットがもつれ（エンタングルメント）を起こすほど学習能力が上がる．一方，現在実現されている量子ゲート方式コンピュータの NISQ（Noisy Intermediate-Scale Quantum computer）デバイスでは，ノイズを許容し誤り訂正を行わない．そのため回路が深くなるほどノイズによって理論通りの状態を出力できなくなる．すなわち，NISQ デバイスで VQA による量子機械学習を行うと，学習性能とデバイスのノイズ特性がトレードオフの関係になる．以上を踏まえ，本稿では，ノイズのある環境における回路構成の違いによる学習性能の差を精度の観点から評価した．評価の結果，VQA による量子機械学習においてエンタングルメントが必要不可欠であること，及びゲート数が最小限に抑えられる線形なエンタングルメントの場合には Ansatz の反復回数が増え，回路の深さが深くなっても精度の低下は小さく，ノイズのないシミュレーションと同等の性能が得られることを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告組込みシステム（EMB）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-14","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"37","bibliographicVolumeNumber":"2024-EMB-65"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233514,"updated":"2025-01-19T10:04:16.278572+00:00","links":{},"created":"2025-01-19T01:35:00.276528+00:00"}