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  1. 研究報告
  2. 量子ソフトウェア(QS)
  3. 2022
  4. 2022-QS-007

Quantum Generative Model with Optimal Transport

https://ipsj.ixsq.nii.ac.jp/records/220425
https://ipsj.ixsq.nii.ac.jp/records/220425
94e134c4-5dad-49b5-be57-eed7ad852403
名前 / ファイル ライセンス アクション
IPSJ-QS22007021.pdf IPSJ-QS22007021.pdf (1.6 MB)
Copyright (c) 2022 by the Information Processing Society of Japan
オープンアクセス
Item type SIG Technical Reports(1)
公開日 2022-10-20
タイトル
タイトル Quantum Generative Model with Optimal Transport
タイトル
言語 en
タイトル Quantum Generative Model with Optimal Transport
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
Sony Group Corporation/Quantum Computing Center, Keio University/Graduate School of Science and Technology, Keio University
著者所属
Mizuho Research & Technologies, Ltd.
著者所属
Quantum Computing Center, Keio University/Department of Applied Physics and Physico-Informatics, Keio University
著者所属(英)
en
Sony Group Corporation / Quantum Computing Center, Keio University / Graduate School of Science and Technology, Keio University
著者所属(英)
en
Mizuho Research & Technologies, Ltd.
著者所属(英)
en
Quantum Computing Center, Keio University / Department of Applied Physics and Physico-Informatics, Keio University
著者名 Hiroyuki, Tezuka

× Hiroyuki, Tezuka

Hiroyuki, Tezuka

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Shumpei, Uno

× Shumpei, Uno

Shumpei, Uno

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Naoki, Yamamoto

× Naoki, Yamamoto

Naoki, Yamamoto

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著者名(英) Hiroyuki, Tezuka

× Hiroyuki, Tezuka

en Hiroyuki, Tezuka

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Shumpei, Uno

× Shumpei, Uno

en Shumpei, Uno

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Naoki, Yamamoto

× Naoki, Yamamoto

en Naoki, Yamamoto

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論文抄録
内容記述タイプ Other
内容記述 Generative model is an unsupervised machine learning framework, that exhibits strong performance in imaging or anomaly detection in classical machine learning regime. Recently we find several quantum version of generative model, some of which are even proven to have quantum advantage. However, those proposals have a strict limitation; that is, the quantum state to be learned (i.e., the quantum state that the model produces) is limited to a single quantum state, and thus those methods are not applicable to a set of quantum states. In this paper, we propose a quantum generative model that can learn a set of quantum state, in an unsupervised machine learning framework. The key idea is to introduce a loss function calculated based on optimal transport distance, i.e. Wasserstein distance. We then apply the proposed method to an anomaly detection task, that cannot be handled via existing methods. The proposed model paves the way for a wide application such as the health check of quantum devices and efficient initialization of quantum computation.
論文抄録(英)
内容記述タイプ Other
内容記述 Generative model is an unsupervised machine learning framework, that exhibits strong performance in imaging or anomaly detection in classical machine learning regime. Recently we find several quantum version of generative model, some of which are even proven to have quantum advantage. However, those proposals have a strict limitation; that is, the quantum state to be learned (i.e., the quantum state that the model produces) is limited to a single quantum state, and thus those methods are not applicable to a set of quantum states. In this paper, we propose a quantum generative model that can learn a set of quantum state, in an unsupervised machine learning framework. The key idea is to introduce a loss function calculated based on optimal transport distance, i.e. Wasserstein distance. We then apply the proposed method to an anomaly detection task, that cannot be handled via existing methods. The proposed model paves the way for a wide application such as the health check of quantum devices and efficient initialization of quantum computation.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA12894105
書誌情報 研究報告量子ソフトウェア(QS)

巻 2022-QS-7, 号 21, p. 1-11, 発行日 2022-10-20
ISSN
収録物識別子タイプ ISSN
収録物識別子 2435-6492
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
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