Quantum enhanced identification of boosted jets with quantum graph neural networks
Abstract
We present a quantum enhanced tagger to identify jets with large Lorentz boost at colliders. For the first time, a convolutional quantum graph neural network (QGNN) is designed to discriminate boosted jets arising from hadronic decays of the Z boson, against those produced from gluons with large momentum. The network receives data without any physics-driven refinement, relying solely on the dimensionality reduction. The reduction is performed using a convolutional autoencoder whose performance is improved in the presence of added noise. The latent data are put into a graph format and fed to the QGNN of ten qubits. The autoencoder and the QGNN are trained separately, and simultaneously, and the resulting performances are compared with a classic algorithm based on graph networks. The findings indicate a strong potential of quantum graph networks to reproduce the performance of classical methods.
Keywords
Cite
@article{arxiv.2605.18416,
title = {Quantum enhanced identification of boosted jets with quantum graph neural networks},
author = {Parichehr Kangaziankangazi and Abideh Jafari and Maurizio Pierini and Hamed Bakhshiansohi},
journal= {arXiv preprint arXiv:2605.18416},
year = {2026}
}