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Tensor Network Python (TeNPy) version 1

Authors :
Hauschild, Johannes
Unfried, Jakob
Anand, Sajant
Andrews, Bartholomew
Bintz, Marcus
Borla, Umberto
Divic, Stefan
Drescher, Markus
Geiger, Jan
Hefel, Martin
Hémery, Kévin
Kadow, Wilhelm
Kemp, Jack
Kirchner, Nico
Liu, Vincent S.
Möller, Gunnar
Parker, Daniel
Rader, Michael
Romen, Anton
Scalet, Samuel
Schoonderwoerd, Leon
Schulz, Maximilian
Soejima, Tomohiro
Thoma, Philipp
Wu, Yantao
Zechmann, Philip
Zweng, Ludwig
Mong, Roger S. K.
Zaletel, Michael P.
Pollmann, Frank
Publication Year :
2024

Abstract

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.<br />Comment: v2: updated funding acknowledgement

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.02010
Document Type :
Working Paper