SciPost Phys. Lect. Notes 5 (2018) ·
published 8 October 2018
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· pdf
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.
