Lie-Equivariant Quantum Graph Neural Networks
This provides a symmetry-preserving quantum alternative for binary classification in high-energy physics, though it appears incremental as it matches rather than surpasses existing methods.
The authors tackled the problem of quark-gluon jet discrimination at the Large Hadron Collider by developing a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), achieving performance on par with the classical state-of-the-art LorentzNet.
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.