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Tensor Network Python (TeNPy) version 1

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arxiv 2408.02010 v3 pith:4EQLNVV2 submitted 2024-08-04 cond-mat.str-el

Johannes Hauschild , Jakob Unfried , Sajant Anand , Bartholomew Andrews , Marcus Bintz , Umberto Borla , Stefan Divic , Markus Drescher
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Jan Geiger Martin Hefel Kévin Hémery Wilhelm Kadow Jack Kemp Nico Kirchner Vincent S. Liu Gunnar Möller Daniel Parker Michael Rader Anton Romen Samuel Scalet Leon Schoonderwoerd Maximilian Schulz Tomohiro Soejima Philipp Thoma Yantao Wu Philip Zechmann Ludwig Zweng Roger S. K. Mong Michael P. Zaletel Frank Pollmann
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TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

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Cited by 2 Pith papers

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  1. Exciting terahertz magnons with amplitude modulated light: spin pumping, squeezed states, symmetry breaking and pattern formation

    cond-mat.mes-hall 2025-07 conditional novelty 6.0 of 10

    Amplitude-modulated optical light can parametrically excite THz antiferromagnetic magnons, generating spin currents, squeezed magnon pairs, and ordered spin patterns.

  2. Shortcuts to Analog Preparation of Non-Equilibrium Quantum Lakes

    quant-ph 2025-02 conditional novelty 6.0 of 10

    Approximate counterdiabatic driving naturally targets the hemidiabatic 'quantum lakes' state and speeds up its preparation by nearly an order of magnitude in a Rydberg ruby lattice model.

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