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Quantum Machine Learning

Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz

arXiv
Authors: Andrew Hallam, Edward Grant, Vid Stojevic, Simone Severini, Andrew G. Green

Year

2017

Paper ID

25206

Status

Preprint

Abstract Read

~2 min

Abstract Words

105

Citations

N/A

Abstract

This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for the fully connected layers in a convolutional neural network and test this implementation on the CIFAR-10 and CIFAR-100 datasets. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA layers with 14000 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers, scaling like O(N).

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  • This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1711.03357 [2] arXiv https://arxiv.org/abs/1711.03357 [3] Publisher https://arxiv.org/abs/1711.03357

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