Publication Date
2019
Journal or Book Title
Applied Network Science
Abstract
Recent neural networks designed to operate on graph-structured data have proven effective in many domains. These graph neural networks often diffuse information using the spatial structure of the graph. We propose a quantum walk neural network that learns a diffusion operation that is not only dependent on the geometry of the graph but also on the features of the nodes and the learning task. A quantum walk neural network is based on learning the coin operators that determine the behavior of quantum random walks, the quantum parallel to classical random walks. We demonstrate the effectiveness of our method on multiple classification and regression tasks at both node and graph levels.
DOI
https://doi.org/10.1007/s41109-019-0188-2
Volume
4
Special Issue
Special Issue of the 7th International Conference on Complex Networks and Their Applications
License
UMass Amherst Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Dernbach, Stefan; Mohseni-Kabir, Arman; Pal, Siddarth; Gepner, Miles; and Towsley, Don, "Quantum walk neural networks with feature dependent coins" (2019). Applied Network Science. 1356.
https://doi.org/10.1007/s41109-019-0188-2