Lie-Equivariant Quantum Graph Neural Networks

TL;DR

This paper introduces a Lie-Equivariant Quantum Graph Neural Network designed for particle physics data analysis, demonstrating comparable performance to classical models while preserving Lorentz symmetry, thus offering a quantum alternative for efficient, symmetry-aware classification tasks.

Contribution

The paper presents a novel Lie-Equivariant Quantum GNN that maintains Lorentz symmetry and achieves performance comparable to classical models in jet discrimination tasks.

Findings

Quantum model achieves performance on par with classical LorentzNet.

The quantum GNN is data-efficient and symmetry-preserving.

Potential for quantum models to replace classical approaches in physics analysis.

Abstract

Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that is not only data efficient, but also has symmetry-preserving properties. Since Lorentz group equivariance has been shown to be beneficial for jet tagging, we build a Lorentz-equivariant quantum GNN for quark-gluon jet discrimination and show that its performance is on par with its classical state-of-the-art counterpart LorentzNet, making it a viable alternative to the conventional computing paradigm.