Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
Johannes Hauschild, Frank Pollmann
SciPost Phys. Lect. Notes 5 (2018) · published 8 October 2018
- doi: 10.21468/SciPostPhysLectNotes.5
- Submissions/Reports
-
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.
TY - JOUR
PB - SciPost Foundation
DO - 10.21468/SciPostPhysLectNotes.5
TI - Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
PY - 2018/10/08
UR - https://www.scipost.org/SciPostPhysLectNotes.5
JF - SciPost Physics Lecture Notes
JA - SciPost Phys. Lect. Notes
SP - 5
A1 - Hauschild, Johannes
AU - Pollmann, Frank
AB - 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.
ER -
@Article{10.21468/SciPostPhysLectNotes.5,
title={{Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)}},
author={Johannes Hauschild and Frank Pollmann},
journal={SciPost Phys. Lect. Notes},
pages={5},
year={2018},
publisher={SciPost},
doi={10.21468/SciPostPhysLectNotes.5},
url={https://scipost.org/10.21468/SciPostPhysLectNotes.5},
}
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