Quantum Graph Neural Networks

Guillaume Verdon, Trevor McCourt, Enxhell Luzhinca, Vikash Singh, Stefan Leichenauer, Jack Hidary

TL;DR: Introducing a new class of quantum neural networks for learning graph-based representations on quantum computers.

Abstract: We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network. Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN). We provide four example applications of QGNN's: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.

Keywords: quantum neural networks, quantum machine learning, quantum deep learning, graph neural networks, graph convolutional neural networks