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Tensor Network Python (TeNPy) version 1
by 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
Submission summary
| Authors (as registered SciPost users): | Johannes Hauschild |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2408.02010v1 (pdf) |
| Code repository: | https://github.com/tenpy/tenpy |
| Date submitted: | 2024-08-30 14:05 |
| Submitted by: | Hauschild, Johannes |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
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.
Current status:
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Report
The TenPy package is well-known and heavily used in the community. This publication is a straightforward extension of the recent release of Version 1.
I don't see any weaknesses in this manuscript, so I recommend publication as it is.
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