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- W3100545578 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." @default.
- W3100545578 created "2020-11-23" @default.
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- W3100545578 date "2018-10-08" @default.
- W3100545578 modified "2023-10-14" @default.
- W3100545578 title "Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)" @default.
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- W3100545578 doi "https://doi.org/10.21468/scipostphyslectnotes.5" @default.
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