Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
Johannes Hauschild,Frank Pollmann +1 more
- 08 Oct 2018
- pp 005
TL;DR: TeNPy as discussed by the authors is a tensor library for Python that provides a compact review of basic tensor state (TPS) concepts and a practical guide to implement abelian symmetries to accelerate tensor operations.
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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) [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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The ITensor Software Library for Tensor Network Calculations
TL;DR: The philosophy behind ITensor, a system for programming tensor network calculations with an interface modeled on tensor diagram notation, and examples of each part of the interface including Index objects, the ITensor product operator, Tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, and the NDTensors library are discussed.
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