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Cited by 438

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

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

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Hauschild, Johannes, Pollmann, Frank·2018·SciPost Physics Lecture Notes
DOI 10.21468/scipostphyslectnotes.5
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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) [1]. 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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Metadata

Authors
Hauschild, J., & Pollmann, F.
Venue
SciPost Physics Lecture Notes
Publisher
Stichting SciPost
Published
2018-10-08
ISSN
2590-1990 (ISSN-L)
DOI
10.21468/scipostphyslectnotes.5
OpenAlex
W2798727936
Cited by
438
References
67
FWCI
10.63
Type
journal article
Language
en
Open access
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