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arXiv · 1801.05417

Quantum Walk Inspired Neural Networks for Graph-Structured Data

Stefan Dernbach·Arman Mohseni-Kabir·Siddharth Pal·Don Towsley·Miles Gepner
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Abstract

In recent years, new neural network architectures designed to operate on graph-structured data have pushed the state-of-the-art in the field. A large set of these architectures utilize a form of classical random walks to diffuse information. We propose quantum walk neural networks (QWNN), a novel graph neural network architecture based on quantum random walks, the quantum parallel to classical random walks. A QWNN learns a quantum walk on a graph to construct a diffusion operator which can then be applied to graph-structured data. We demonstrate the use of QWNNs on a variety of prediction tasks on graphs involving temperature, biological, and molecular datasets.

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Stefan Dernbach, Arman Mohseni-Kabir, Siddharth Pal, Don Towsley, Miles Gepner. 2018-01-16. Quantum Walk Inspired Neural Networks for Graph-Structured Data. https://arxiv.org/abs/1801.05417

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