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A high-performance matrix product state(MPS) algorithms library based on GraceQ/tensor

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GraceQ/MPS2

A high-performance matrix product state algorithms library based on GraceQ/tensor

Easily push your bond dimension to 10k

Project status

Documentation Status

Last release

Project homepage

For more information, user guide, and documentation, please visit project homepage.

Current developers and maintainers

Note: For a complete list of the contributors, see CONTRIBUTORS.txt

How to cite

You can cite the GraceQ/MPS2 where you use it as a support to this project. Please cite GraceQ/MPS2 as

GraceQuantum.org . GraceQ/MPS2: A high-performance matrix product state algorithms library based on GraceQ/tensor. Homepage: https://mps2.gracequantum.org . For a complete list of the contributors, see CONTRIBUTORS.txt .

Acknowledgments

We highly acknowledge the following people, project(s) and organization(s) (sorted in alphabetical order):

ALPS project, Chunyu Sun, Donna Sheng, Grace Song, Hao-Kai Zhang, Hao-Xin Wang, Hong-Chen Jiang, Hong-Hao Tu, Hui-Ke Jin, itensor.org, Jisi Xu, Le Zhao, Shuai Chen, Shuo Yang, Thomas P. Devereaux, Wayne Zheng, Xiaoyu Dong, Yi Zhou, Yifan Jiang, Zheng-Yu Weng

You can not meet this project without anyone of them. And the basic part of this project (before version 0.1) was developed by Rong-Yang Sun and Cheng Peng, when Rong-Yang Sun was a visiting student at Stanford University. So R.-Y. Sun want to give special thanks to his co-advisors Hong-Chen Jiang, Prof. Thomas P. Devereaux and their postdoctors Yifan Jiang and Cheng Peng.

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A high-performance matrix product state(MPS) algorithms library based on GraceQ/tensor

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