TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks

Ashley Milsted, Martin Ganahl, Stefan Leichenauer, Jack Hidary, Guifre Vidal
5/3/2019
cond-mat.str-elcs.LGhep-th

Official code: google/TensorNetwork (1,871 stars, Apache-2.0, last commit 2023-09-04).

Abstract

TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow. We describe a tree tensor network (TTN) algorithm for approximating the ground state of either a periodic quantum spin chain (1D) or a lattice model on a thin torus (2D), and implement the algorithm using TensorNetwork. We use a standard energy minimization procedure over a TTN ansatz with bond dimension $χ$, with a computational cost that scales as $O(χ^4)$. Using bond dimension $χ\in [32,256]$ we compare the use of CPUs with GPUs and observe significant computational speed-ups, up to a factor of $100$, using a GPU and the TensorNetwork library.

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Code Implementations(1)

google/TensorNetworkOfficial100%
Apache-2.0

A library for easy and efficient manipulation of tensor networks.

1,871362Python, DockerfileApr 25, 20193 years agoApache-2.0
matrix-product-statestensor-networks

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Cite this paper

@article{milsted2019tensornetwork,
  title  = {TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks},
  author = {Ashley Milsted and Martin Ganahl and Stefan Leichenauer and Jack Hidary and Guifre Vidal},
  year   = {2019},
  eprint = {1905.01331},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1905.01331}
}

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