Tensor network Python (TeNPy) version 1
SciPost Physics Codebases2024
Citations Over TimeTop 1% of 2024 papers
Johannes Hauschild, Jakob Unfried, Sajant Anand, Bartholomew Andrews, Marcus Bintz, Umberto Borla, Stefan Divic, Markus Drescher, J. Geiger, Martin Hefel, Kévin Hémery, Wilhelm Kadow, Jack Kemp, Nico Kirchner, Vincent Liu, Gunnar Möller, Daniel E. Parker, Michael Rader, Anton Romen, Samuel O. Scalet, Leon Schoonderwoerd, Maximilian Schulz, Tomohiro Soejima, P. Thoma, Yantao Wu, Philip Zechmann, Ludwig Zweng, Roger S. K. Mong, Michael P. Zaletel, Frank Pollmann
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.
Related Papers
- → Evaluation of the readability of ACOG patient education pamphlets(1999)44 cited
- The Validity of Some Popular Readability Formulas(2012)
- → Evaluation of the Readability of ACOG Patient Education Pamphlets(1999)7 cited
- A Survey of Studies on Readability(2000)
- → Readability as a Source of Measurement Error in Medical Education Assessment(2019)