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

2026-03-09

AI Summary

This work addresses the challenges of efficiency and precision in charged particle track reconstruction under the high-pileup conditions of the High-Luminosity LHC. The authors propose a hybrid quantum graph neural network (QGNN) that integrates classical feedforward layers with parameterized variational quantum circuits in an alternating stack, enabling end-to-end trainable classification of hit associations across detector layers. The architecture systematically elucidates the synergistic interplay between classical and quantum components and introduces key enhancements to the original QGNN framework, substantially improving training convergence and stability. Experimental results on high-luminosity simulation data demonstrate that the upgraded model achieves more reliable convergence, offering compelling new evidence for the applicability of quantum machine learning in high-energy physics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• charged particle tracking
• high luminosity LHC
• track reconstruction
• quantum machine learning
• event complexity

Innovation

• quantum graph neural network
• charged particle tracking
• hybrid quantum-classical architecture
• high energy physics
• parameterized quantum circuits