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Quantum Walk Neural Networks for Graph-Structured Data

  • Stefan Dernbach
  • Arman Mohseni-Kabir
  • Siddharth Pal
  • Don Towsley
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

In recent years, 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 throughout the graph. 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 this model on a variety of prediction tasks on graphs involving temperature, biological, and molecular datasets.

Keywords

Graph neural networks Quantum walks Graph classification Graph regression 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stefan Dernbach
    • 1
  • Arman Mohseni-Kabir
    • 1
  • Siddharth Pal
    • 2
  • Don Towsley
    • 1
  1. 1.University of Massachusetts AmherstAmherstUSA
  2. 2.Raytheon BBN TechnologiesCambridgeUSA

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