تقرير
TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
| العنوان: | TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models |
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| المؤلفون: | Ganahl, Martin, Milsted, Ashley, Leichenauer, Stefan, Hidary, Jack, Vidal, Guifre |
| سنة النشر: | 2019 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics |
| الوصف: | 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. Comment: 8 pages, 10 figures; code can be downloaded from https://github.com/google/TensorNetwork |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1906.12030 |
| رقم الأكسشن: | edsarx.1906.12030 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |