Guided Graph Compression for Quantum Graph Neural Networks

TL;DR

This paper introduces a Guided Graph Compression framework that uses a graph autoencoder to reduce graph size and feature dimensions, improving quantum and classical GNN performance on high energy physics classification tasks.

Contribution

The work presents a novel graph autoencoder-based compression method that enhances quantum and classical GNNs, enabling effective processing of larger, realistic datasets in quantum computing contexts.

Findings

GGC outperforms standalone autoencoder preprocessing.

GGC surpasses baseline classical GNN classifiers.

Facilitates testing of new QGNN architectures on realistic data.

Abstract

Graph Neural Networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. Quantum Computing (QC) offers a promising avenue to address these issues and inspires new algorithmic approaches. In particular, Quantum Graph Neural Networks (QGNNs) have been explored in recent literature. However, current quantum hardware limits the dimension of the data that can be effectively encoded. Existing approaches either simplify datasets manually or use artificial graph datasets. This work introduces the Guided Graph Compression (GGC) framework, which uses a graph autoencoder to reduce both the number of nodes and the dimensionality of node features. The compression is guided to enhance the performance of a downstream classification task, which can be applied either with a quantum or a classical classifier. The framework is evaluated on the Jet Tagging task, a classification problem of fundamental importance in high energy physics that involves distinguishing particle jets initiated by quarks from those by gluons. The GGC is compared against using the autoencoder as a standalone preprocessing step and against a baseline classical GNN classifier. Our numerical results demonstrate that GGC outperforms both alternatives, while also facilitating the testing of novel QGNN ansatzes on realistic datasets.