Supervised Learning with Quantum-Inspired Tensor Networks
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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 form imparts additional structure to the learned model and suggest a possible generative interpretation.
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