Characterization and upgrade of a quantum graph neural network for charged particle tracking

TL;DR

This paper evaluates and enhances a quantum graph neural network for charged particle tracking in high luminosity collider experiments, demonstrating improved training convergence and hybrid classical-quantum architecture.

Contribution

It introduces key upgrades to a quantum graph neural network architecture, improving training convergence for particle track reconstruction in high energy physics.

Findings

Improved training convergence with the upgraded QGNN.

Effective hybrid classical-quantum architecture for HEP tasks.

Enhanced characterization of classical-quantum interplay.

Abstract

In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in reconstructing charged particle tracks, motivating frontier research in new technologies. Quantum machine learning models are being investigated as potential new approaches to high energy physics (HEP) tasks. We characterize and upgrade a quantum graph neural network (QGNN) architecture for charged particle track reconstruction on a simulated high luminosity dataset. The model operates on a set of event graphs, each built from the hits generated in tracking detector layers by particles produced in proton collisions, performing a classification of the possible hit connections between adjacent layers. In this approach the QGNN is designed as a hybrid architecture, interleaving classical feedforward networks with parametrized quantum circuits. We characterize the interplay between the classical and quantum components. We report on the principal upgrades to the original design, and present new evidence of improved training behavior, specifically in terms of convergence toward the final trained configuration.