Hybrid Quantum-Classical Graph Convolutional Network

TL;DR

This paper introduces a hybrid quantum-classical graph convolutional network tailored for high energy physics data, showing parameter efficiency and competitive accuracy, thus advancing quantum machine learning applications in scientific research.

Contribution

The paper presents a novel hybrid quantum-classical GCN model that outperforms classical models in parameter efficiency and matches quantum CNN accuracy on HEP data.

Findings

QGCNN has fewer parameters than classical models.

QGCNN achieves comparable accuracy to quantum CNN.

Numerical simulations suggest potential of QML in HEP.

Abstract

The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than $50\%$ of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields.