Quantum enhanced identification of boosted jets with quantum graph neural networks

TL;DR

This paper introduces a quantum-enhanced method using a quantum graph neural network to identify highly boosted jets from Z boson decays at colliders, demonstrating potential comparable to classical algorithms.

Contribution

It is the first to design a convolutional quantum graph neural network for jet identification, utilizing a quantum autoencoder for data reduction and showing promising results.

Findings

Quantum graph neural networks can match classical performance in jet tagging.

Autoencoder-based data reduction improves quantum network training.

Quantum methods show potential for collider data analysis.

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.