Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware
Abstract
Jets are central to the physics programs of both current and future colliders, from precision Standard Model measurements and searches for new physics at the Large Hadron Collider to studies of nucleon structure at the future Electron-Ion Collider. Motivated by these applications, we explore quantum machine learning for jet classification and present a permutation-invariant Quantum Graph Neural Network (QGNN) applied to particle-cloud representations of jets. We apply the model to two such discrimination tasks: quark vs. gluon and up vs. down quark flavor tagging, with the latter being, to our knowledge, the first application of a quantum model to this problem. In the ideal simulation, the QGNN performs competitively against the Particle Flow Network and traditional QCD observables. We further deploy scaled-down models to IBM and IonQ quantum processing units (QPUs), where we train and evaluate them, obtaining promising results. Finally, we perform an interpretability analysis to characterize the observables learned by the quantum model, relating them to generalized angularities for the quark-gluon study and to jet charge for the flavor study.
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BibTeX
@misc{jobilal2026quantum,
title = {{Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware}},
author = {Benjamin Jobilal and Jinghong Yang and Trevor Smith and Vincent Calvo and Zhong-Bo Kang and Shabnam Jabeen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04367},
eprint = {2609.04367},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04367},
}
RIS
TY - UNPB
TI - Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware
AU - Benjamin Jobilal
AU - Jinghong Yang
AU - Trevor Smith
AU - Vincent Calvo
AU - Zhong-Bo Kang
AU - Shabnam Jabeen
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.04367
UR - https://arxiv.org/abs/2609.04367
AB - Jets are central to the physics programs of both current and future colliders, from precision Standard Model measurements and searches for new physics at the Large Hadron Collider to studies of nucleon structure at the future Electron-Ion Collider. Motivated by these applications, we explore quantum machine learning for jet classification and present a permutation-invariant Quantum Graph Neural Network (QGNN) applied to particle-cloud representations of jets. We apply the model to two such discrimination tasks: quark vs. gluon and up vs. down quark flavor tagging, with the latter being, to our knowledge, the first application of a quantum model to this problem. In the ideal simulation, the QGNN performs competitively against the Particle Flow Network and traditional QCD observables. We further deploy scaled-down models to IBM and IonQ quantum processing units (QPUs), where we train and evaluate them, obtaining promising results. Finally, we perform an interpretability analysis to characterize the observables learned by the quantum model, relating them to generalized angularities for the quark-gluon study and to jet charge for the flavor study.
ER -