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arXiv · 1906.12030

TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models

Martin Ganahl·Ashley Milsted·Stefan Leichenauer·Jack Hidary·Guifre Vidal
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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.

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Martin Ganahl, Ashley Milsted, Stefan Leichenauer, Jack Hidary, Guifre Vidal. 2019-06-28. TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models. https://arxiv.org/abs/1906.12030

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