In plain words: A free, open-source toolkit for building tensor networks—sparse grids of numbers that shrink huge quantum and machine-learning calculations into manageable pieces. It uses one shared set of commands, demonstrated on problems in both physics and machine learning.

Abstract

TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.

Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, Stefan Leichenauer
arXiv:1905.01330 · physics.comp-ph, cond-mat.str-el, cs.LG, hep-th, stat.ML · submitted May 3, 2019
abstract · pdf · html · The TensorNetwork library can be found at https://github.com/google/tensornetwork