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The State Preparation of Multivariate Normal Distributions using Tree Tensor Network
https://ipsj.ixsq.nii.ac.jp/records/235048
https://ipsj.ixsq.nii.ac.jp/records/2350486a5bbc68-8c60-4c0a-b65d-0fc9281d0591
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-QS24012001.pdf (1.1 MB)
2026年6月20日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, QS:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2024-06-20 | |||||||||
| タイトル | ||||||||||
| タイトル | The State Preparation of Multivariate Normal Distributions using Tree Tensor Network | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | The State Preparation of Multivariate Normal Distributions using Tree Tensor Network | |||||||||
| 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 | ||||||||||
| Graduate School of Engineering Science, Osaka University | ||||||||||
| 著者所属 | ||||||||||
| Department of Nuclear Engineering, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Engineering Science, Osaka University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Department of Nuclear Engineering, Kyoto University | ||||||||||
| 著者名 |
Hidetaka, Manabe
× Hidetaka, Manabe
× Yuichi, Sano
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| 著者名(英) |
Hidetaka, Manabe
× Hidetaka, Manabe
× Yuichi, Sano
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | The quantum state preparation of probability distributions is an important subroutine for many quantum algorithms. When embedding D-dimensional multivariate probability distributions by discretizing each dimension into 2n-point, we need a state preparation circuit comprising a total of nD qubits, which is often difficult to compile. In this study, we propose a method to generate state preparation circuits for D-dimensional multivariate normal distributions, utilizing tensor networks. We represent the probability distribution with a tree tensor network and perform the task of quantum circuit compilation through the optimization of tensor networks. Especially, by employing structural optimization, we can search for a network structure that efficiently represents the correlations between variables. The numerical results suggest that our method can dramatically reduce the circuit depth while maintaining fidelity compared to existing approaches. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | The quantum state preparation of probability distributions is an important subroutine for many quantum algorithms. When embedding D-dimensional multivariate probability distributions by discretizing each dimension into 2n-point, we need a state preparation circuit comprising a total of nD qubits, which is often difficult to compile. In this study, we propose a method to generate state preparation circuits for D-dimensional multivariate normal distributions, utilizing tensor networks. We represent the probability distribution with a tree tensor network and perform the task of quantum circuit compilation through the optimization of tensor networks. Especially, by employing structural optimization, we can search for a network structure that efficiently represents the correlations between variables. The numerical results suggest that our method can dramatically reduce the circuit depth while maintaining fidelity compared to existing approaches. | |||||||||
| 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 2024-QS-12, 号 1, p. 1-6, 発行日 2024-06-20 |
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| 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 | |||||||||
