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分层系统的高效张量网络算法。

Efficient Tensor Network Algorithm for Layered Systems.

机构信息

Institute for Theoretical Physics and Delta Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.

出版信息

Phys Rev Lett. 2023 Mar 31;130(13):130601. doi: 10.1103/PhysRevLett.130.130601.

DOI:10.1103/PhysRevLett.130.130601
PMID:37067308
Abstract

Strongly correlated layered 2D systems are of central importance in condensed matter physics, but their numerical study is very challenging. Motivated by the enormous successes of tensor networks for 1D and 2D systems, we develop an efficient tensor network approach based on infinite projected entangled-pair states for layered 2D systems. Starting from an anisotropic 3D infinite projected entangled-pair state ansatz, we propose a contraction scheme in which the weakly interacting layers are effectively decoupled away from the center of the layers, such that they can be efficiently contracted using 2D contraction methods while keeping the center of the layers connected in order to capture the most relevant interlayer correlations. We present benchmark data for the anisotropic 3D Heisenberg model on a cubic lattice, which shows close agreement with quantum Monte Carlo and full 3D contraction results. Finally, we study the dimer to Néel phase transition in the Shastry-Sutherland model with interlayer coupling, a frustrated spin model that is out of reach of quantum Monte Carlo due to the negative sign problem.

摘要

强关联层状二维系统在凝聚态物理中具有核心重要性,但对其进行数值研究极具挑战性。受张量网络在一维和二维系统中取得巨大成功的启发,我们为层状二维系统开发了一种基于无限投影纠缠态的高效张量网络方法。从各向异性的 3D 无限投影纠缠态假设出发,我们提出了一种缩并方案,其中弱相互作用的层有效地从层的中心解耦,从而可以使用二维缩并方法有效地缩并它们,同时保持层的中心连接以捕获最相关的层间相关性。我们为立方晶格上各向异性 3D Heisenberg 模型提供了基准数据,与量子蒙特卡罗和全 3D 缩并结果非常吻合。最后,我们研究了具有层间耦合的 Shastry-Sutherland 模型中的二聚体到奈尔相转变,由于负号问题,该模型使量子蒙特卡罗无法处理,这是一个受挫的自旋模型。

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