Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)

Johannes Hauschild, Frank Pollmann
4/30/2018
cond-mat.str-el

Official code: tenpy/tenpy (504 stars, Apache-2.0, last commit 2026-10-20).

Abstract

Tensor product state (TPS) based methods are powerful tools to efficiently simulate quantum many-body systems in and out of equilibrium. In particular, the one-dimensional matrix-product (MPS) formalism is by now an established tool in condensed matter theory and quantum chemistry. In these lecture notes, we combine a compact review of basic TPS concepts with the introduction of a versatile tensor library for Python (TeNPy) [https://github.com/tenpy/tenpy]. As concrete examples, we consider the MPS based time-evolving block decimation and the density matrix renormalization group algorithm. Moreover, we provide a practical guide on how to implement abelian symmetries (e.g., a particle number conservation) to accelerate tensor operations.

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Code Implementations(1)

tenpy/tenpyOfficial100%
Apache-2.0

Tensor Network Python (TeNPy)

504159Shell, CythonFeb 19, 2018yesterdayApache-2.0

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Cite this paper

@article{hauschild2018efficient,
  title  = {Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)},
  author = {Johannes Hauschild and Frank Pollmann},
  year   = {2018},
  eprint = {1805.00055},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1805.00055}
}

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