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arXiv · 1805.00055

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

Johannes Hauschild·Frank Pollmann
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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) [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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Johannes Hauschild, Frank Pollmann. 2018-04-30. Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy). https://doi.org/10.21468/scipostphyslectnotes.5

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