Tensor network Python (TeNPy) version 1

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.

Authors

Institutions

Publication Details

Journal
SciPost Physics Codebases
Published
2024-11-26
DOI
https://doi.org/10.21468/scipostphyscodeb.41
Citations
36
Primary Topic
Quantum many-body systems
Type
article
Field-Weighted Citation Impact
12.15

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tensor network Python (TeNPy) version 1

Michael P. Zaletel, Sajant Anand, P. Thoma, Umberto Borla et al.
36 citations
SciPost Physics Codebases
Quantum many-body systems
12.15
article

Tensor network Python (TeNPy) version 1

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

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.

SciPost Physics Codebases
Harvard University (US), Lawrence Berkeley National Laboratory (US), University of St Andrews (GB), University of Pittsburgh (US), Universität Innsbruck (AT), Hebrew University of Jerusalem (IL), University of Kent (GB), University of Cambridge (GB), Harvard University Press (US), ETH Zurich (CH), University of California San Diego (US), Nippon Soken (Japan) (JP), RIKEN (JP), Max Planck Institute for the Physics of Complex Systems (DE), Instituto de Física Teórica (ES), Max Planck Institute of Quantum Optics (DE), Fraunhofer Austria (AT), Munich Center for Quantum Science and Technology (DE), École Polytechnique Fédérale de Lausanne (CH), Technical University of Munich (DE), University of California, Berkeley (US)
U.S. Department of Energy, Gordon and Betty Moore Foundation, Royal Society, University of Kent, Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Austrian Science Fund, Engineering and Physical Sciences Research Council, Japan Science and Technology Agency, RIKEN, Horizon 2020, Army Research Office
Openalex Percentile: Top 1%
Quantum many-body systems
12.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.