2018/05/31 by Johannes Hauschild, Frank Pollmann · 4 citations
Physics and Astronomy · #cond-mat.str-el
paper · pdf · doi:10.21468/scipostphyslectnotes.5
published as SciPost Phys. Lect. Notes 5 (2018) · 38 pages, 13 figures. Preprint of the published version
arxiv created 2018/11/30 · arxiv updated 2018/12/03
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.