The Tensor Network Python (TeNPy) Library

ORAL

Abstract

We present TeNPy [1], a Python library for the simulation of strongly correlated quantum many body systems with the ansatz of tensor networks, and in particular matrix product states (MPS). The library aims to provide a good balance between code readability, easy implementation of custom models and algorithms, and numerical efficiency for large-scale simulations. After a short overview of the features (and limitations) of the library, we demonstrate how to setup the density matrix renormalization group (DMRG) algorithm for a custom model on a long cylinder geometry as a concrete example. Further, we showcase some applications of TeNPy, present benchmarks, and discuss the roadmap for future developments.
[1] https://github.com/tenpy/tenpy

*This work was funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05- CH11231 through the Scientific Discovery through Advanced Computing (SciDAC) program (KC23DAC Topological and Correlated Matter via Tensor Networks and Quantum Monte Carlo).

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Presenters

  • Johannes Hauschild

    • University of California, Berkeley

Authors

  • Johannes Hauschild

    • University of California, Berkeley

  • Frank Pollmann

    • TU Munich
    • Technical University of Munich
    • Tech Univ Muenchen
    • Technical University Munich

  • Michael Zaletel

    • University of California, Berkeley
    • UC Berkeley
    • Physics, University of California, Berkeley
    • Department of Physics, University of California, Berkeley