Tensor Network Python (TeNPy) version 1
Johannes Hauschild, Jakob Unfried, Sajant Anand, Bartholomew Andrews,, Marcus Bintz, Umberto Borla, Stefan Divic, Markus Drescher, Jan Geiger,, Martin Hefel, K\'evin H\'emery, Wilhelm Kadow, Jack Kemp, Nico Kirchner,, Vincent S. Liu, Gunnar M\"oller, Daniel Parker, Michael Rader
TL;DR
TeNPy is a Python library designed for simulating strongly correlated quantum systems using tensor networks, balancing user-friendliness with advanced algorithms for researchers.
Contribution
The paper introduces version 1.0 of TeNPy, a library that combines accessibility for newcomers with powerful tensor network algorithms for quantum system simulations.
Findings
Supports MPS algorithms for 1D and 2D lattices
Includes DMRG, TEBD, TDVP, and MPO evolution methods
Achieves a balance of usability and computational power
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.
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