TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
Computational Physics
2019-07-01 v1
Abstract
We use TensorNetwork [C. Roberts et al., arXiv: 1905.01330], a recently developed API for performing tensor network contractions using accelerated backends such as TensorFlow, to implement an optimization algorithm for the Multi-scale Entanglement Renormalization Ansatz (MERA). We use the MERA to approximate the ground state wave function of the infinite, one-dimensional transverse field Ising model at criticality, and extract conformal data from the optimized ansatz. Comparing run times of the optimization on CPUs vs. GPU, we report a very significant speed-up, up to a factor of 200, of the optimization algorithm when run on a GPU.
Cite
@article{arxiv.1906.12030,
title = {TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models},
author = {Martin Ganahl and Ashley Milsted and Stefan Leichenauer and Jack Hidary and Guifre Vidal},
journal= {arXiv preprint arXiv:1906.12030},
year = {2019}
}
Comments
8 pages, 10 figures; code can be downloaded from https://github.com/google/TensorNetwork