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2020

Entanglement and Tensor Networks for Supervised Image Classification

Martyn, John, Vidal, Guifre, Roberts, Chase et al.

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Tensor networks, originally designed to address computational problems in quantum many-body physics, have recently been applied to machine learning tasks.

However, compared to quantum physics, where the reasons for the success of tensor network approaches over the last 30 years is well understood, very little is yet known about why these techniques work for machine learning.
The goal of this paper is to investigate entanglement properties of tensor network models in a current machine learning application, in order to uncover general principles that may guide future developments.
We revisit the use of tensor networks for supervised image classification using the MNIST data set of handwritten digits, as pioneered by Stoudenmire and Schwab [Adv.

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