2023 Fiscal Year Final Research Report
Optimization of entanglement structure in many-body problems and its applications
| Project/Area Number |
20K03766
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 13010:Mathematical physics and fundamental theory of condensed matter physics-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Harada Kenji 京都大学, 情報学研究科, 助教 (80303882)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | テンソルネットワーク / ネットワーク構造最適化 / 基底状態計算 / 量子生成モデル / エンタングルメント構造 |
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| Outline of Final Research Achievements |
The tensor network method efficiently represents correlations between elements in a many-body problem using a network structure that represents a tensor contraction. However, the network structure is typically chosen as a hypothesis and is not dynamically optimized. We have developed a method to automatically optimize the network structure of tensor networks for many-body problems such as ground-state calculations and quantum generative models. We have confirmed the usefulness of this method.
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| Free Research Field |
計算物理学
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| Academic Significance and Societal Importance of the Research Achievements |
量子多体系に関連した基底状態計算や量子生成モデルは、量子科学の先端的話題として研究が進められている．我々の提案したテンソルネットワークのネットワーク構造の自動最適化は従来法と異なり柔軟に問題に応じた構造を見つけることができ、基礎・応用どちらにも展開可能な新しい方法論を提供する．
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