Reconstructing Particle Decay Trees with Quantum Graph Neural Networks in High Energy Physics
Year: 2026
Type: conference-paper
Abstract: Quantum Computing and Machine Learning are both significant and appealing research fields. In particular, the combination of both has led to the emergence of the research field quantum machine learning which has recently taken enormous popularity. We investigate in the potential advantages of this synergy for the application in high energy physics, more precisely in reconstructing particle decay trees in particle collision experiments. Due to the larger computational space of quantum computers, this highly complex combinatorical problem is well suited for investigating in a potential quantum advantage compared to the classical scenario. However, current quantum devices are subject to noise and provide only a limited number of qubits. We therefore propose the utilization of a variational quantum circuit within a classical graph neural network which has been shown to be feasible for reconstruction of particle decay trees. We evaluate our approach on a quantum simulator and a real quantum computer by IBM Quantum for artificially generated decay trees and compare our results to the purely classical approach. Our proposed approach does not only enable hardware efficient utilization of nowadays quantum devices, but also shows competitive results even in the presence of noise.
Institution: Karlsruhe Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7161112104