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张量网络Python（TeNPy）版本1

Tensor Network Python (TeNPy) version 1

Johannes Hauschild, Jakob Unfried, Sajant Anand, Bartholomew Andrews, Marcus Bintz, Umberto Borla, Stefan Divic, Markus Drescher, Jan Geiger, Martin Hefel, Kévi… 展开作者

Johannes Hauschild, Jakob Unfried, Sajant Anand, Bartholomew Andrews, Marcus Bintz, Umberto Borla, Stefan Divic, Markus Drescher, Jan Geiger, Martin Hefel, Kévin Hémery, Wilhelm Kadow, Jack Kemp, Nico Kirchner, Vincent S. Liu, Gunnar Möller, Daniel Parker, Michael Rader, Anton Romen, Samuel Scalet, Leon Schoonderwoerd, Maximilian Schulz, Tomohiro Soejima, Philipp Thoma, Yantao Wu, Philip Zechmann, Ludwig Zweng, Roger S. K. Mong, Michael P. Zaletel, Frank Pollmann

备注：
v3: published version with small additional clarifications suggested by referees
发表信息：
SciPost Phys. Codebases 41 (2024)
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AI总结：

本文介绍用于模拟强关联量子系统的Python库TeNPy 1.0版本，其兼顾新手可读性与专家算法需求，聚焦1D/2D晶格的MPS算法及相关动力学方法，概述该软件包内容。

摘要：

TeNPy（“Tensor Network Python”的缩写）是一个用于用张量网络模拟强关联量子系统的Python库。该库的理念是在为新手实现可读性和可用性平衡的同时，为专家提供强大的算法。其重点是针对1D和2D晶格的MPS算法，如DMRG基态搜索，以及使用TEBD、TDVP或MPO演化的动力学。本文是TeNPy近期1.0版本发布的配套文章，简要概述了该软件包。

英文摘要：

TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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