.png){kind=icon}
返回
Generating function for tensor network diagrammatic summation
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevB.103.205155.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The understanding of complex quantum many-body systems has been vastly boosted by tensor network (TN) methods. Among others, excitation spectrum and long-range interacting systems can be studied using TNs, where one however confronts the intricate summation over an extensive number of tensor diagrams. Here, we introduce a set of generating functions, which encode the diagrammatic summations as leading-order series expansion coefficients. Combined with automatic differentiation, the generating function allows us to solve the problem of TN diagrammatic summation. We illustrate this scheme by computing variational excited states and the dynamical structure factor of a quantum spin chain, and further investigating entanglement properties of excited states. Extensions to infinite-size systems and higher dimension are outlined.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
15.4W
被引数:
41.0W
机构
引用论文
分享收藏Towards a cyanobacterial biorefinery: Carbohydrate fingerprint, biocomposition and enzymatic hydrolysis of Nostoc biomass走向蓝细菌生物精炼厂: Nostoc生物质的碳水化合物指纹，生物组成和酶促水解
PREAI
分享收藏Efficient representation of long-range interactions in tensor network algorithms.png)
PHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏
查看更多内容