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

@article{Hauschild2018EfficientNS, title={Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)}, author={Johannes Hauschild and F. Pollmann}, journal={SciPost Physics Lecture Notes}, year={2018} }

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…

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