Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy). 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) [this https URL]. 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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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- de Felice, Giovanni; Toumi, Alexis; Coecke, Bob: DisCoPy: monoidal categories in Python (2021)
- Roy, Ananda; Schuricht, Dirk; Hauschild, Johannes; Pollmann, Frank; Saleur, Hubert: The quantum sine-Gordon model with quantum circuits (2021)
- Matthew Fishman, Steven R. White, E. Miles Stoudenmire: The ITensor Software Library for Tensor Network Calculations (2020) arXiv
- Johannes Hauschild, Frank Pollmann: Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy) (2018) arXiv