Supervised Learning with Quantum-Inspired Tensor Networks

E. Miles Stoudenmire, David J. Schwab
5/18/2016
stat.MLcond-mat.str-elcs.LG

Official code: emstoudenmire/TNML (165 stars, MIT, last commit 2019-04-09).

Abstract

Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications. We demonstrate how algorithms for optimizing such networks can be adapted to supervised learning tasks by using matrix product states (tensor trains) to parameterize models for classifying images. For the MNIST data set we obtain less than 1% test set classification error. We discuss how the tensor network form imparts additional structure to the learned model and suggest a possible generative interpretation.

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Code Implementations(1)

emstoudenmire/TNMLOfficial100%
MIT

Tensor network machine learning. Based on the paper "Supervised Learning with Quantum Inspired Tensor Networks" http://arxiv.org/abs/1605.05775

16555C++Oct 25, 20167 years agoMIT
machine-learningmatrix-product-statesmpstensor-networks

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Cite this paper

@article{stoudenmire2016supervised,
  title  = {Supervised Learning with Quantum-Inspired Tensor Networks},
  author = {E. Miles Stoudenmire and David J. Schwab},
  year   = {2016},
  eprint = {1605.05775},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1605.05775}
}

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