Quantum Algorithms for Graph Neural Networks

EasyChair Preprint 14869

Abstract

Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, but their computational complexity and scalability pose significant challenges, especially with large graphs. Recent advancements in quantum computing offer new avenues for addressing these challenges through quantum algorithms that could potentially enhance the performance of GNNs. This paper explores the intersection of quantum computing and GNNs, presenting a comprehensive overview of quantum algorithms designed to accelerate graph processing tasks and improve the efficiency of neural network operations on graph data. We discuss quantum versions of classical algorithms for graph-related problems, such as quantum algorithms for shortest path finding, graph isomorphism testing, and clustering. Additionally, we examine quantum-enhanced techniques for training GNNs, including variational quantum circuits and quantum annealing methods. Theoretical analyses and preliminary experimental results demonstrate the potential advantages of quantum approaches over classical counterparts. By integrating quantum algorithms into GNN architectures, we propose novel frameworks for more scalable and efficient graph-based learning. This paper aims to provide insights into the potential benefits and challenges of combining quantum computing with graph neural networks and sets the stage for future research in this promising interdisciplinary field.

Keyphrases: Graph Neural Networks, optimization algorithm, quantum algorithms

@booklet{EasyChair:14869,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Algorithms for Graph Neural Networks},
  howpublished = {EasyChair Preprint 14869},
  year      = {EasyChair, 2024}}