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Quantum Machine Learning for Track Reconstruction

Laura Cappelli, Matteo Argenton, C. Bozzi, Enrico Calore, Sebastiano Fabio Schifano

Published Oct 7, 2025• 0 citations
Publisher

Abstract

Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase significantly. As a consequence, local pattern recognition algorithms suffer from scaling problems. In this work, we investigate the possibility of using machine learning techniques in combination with quantum computing. In particular, we represent particle trajectories as a graph data structure and train a quantum graph neural network to perform global pattern recognition. We show recent results on the application of this method, with scalability tests for increasing pileup values. We also provide insights into various aspects of code development in different quantum programming frameworks such as Pennylane and IBM Qiskit. Finally, we discuss the critical points and give an outlook of potential improvements and alternative approaches.

DOI
10.1051/epjconf/202533701246/pdf

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Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase significantly. As a consequence, local pattern recognition algorithms suffer from scaling problems. In this work, we investigate the possibility of using machine learning techniques in combination with quantum computing. In particular, we represent particle trajectories as a graph data structure and train a quantum graph neural network to perform global pattern recognition. We show recent results on the application of this method, with scalability tests for increasing pileup values. We also provide insights into various aspects of code development in different quantum programming frameworks such as Pennylane and IBM Qiskit. Finally, we discuss the critical points and give an outlook of potential improvements and alternative approaches.
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase significantly. As a consequence, local pattern recognition algorithms suffer from scaling problems. In this work, we investigate the possibility of using machine learning techniques in combination with quantum computing. In particular, we represent particle trajectories as a graph data structure and train a quantum graph neural network to perform global pattern recognition. We show recent results on the application of this method, with scalability tests for increasing pileup values. We also provide insights into various aspects of code development in different quantum programming frameworks such as Pennylane and IBM Qiskit. Finally, we discuss the critical points and give an outlook of potential improvements and alternative approaches.
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase dramatically. Traditional algorithms suffer from scaling problems. We are investigating the possibility of using machine learning techniques in combination with quantum computing. In our research, we represent particle trajectories as a graph data structure and train a hybrid graph neural network with classical and quantum layers. We present recent results on the application of these methods, focusing on the computational aspects of code development in different programming frameworks such as Jax, Pennylane and IBM Qiskit. We also provide insights into expected performance and explore the role of GPUs as computational accelerators in the simulation of quantum computing resources.
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.
The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search in a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.
The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms) are used, they are expected to scale worse than quadratic and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces. In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights.
High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue 'Quantum technologies in particle physics'.