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Jet Tagging with a Graph-Based Quantum Neural Network: First Study on the Feasibility and Outlook

Yi-An Chen, Kai-Feng Chen

Published Oct 7, 2025• 0 citations
Publisher

Abstract

Machine learning, particularly deep neural networks, has seen widespread application in high-energy physics. Recently, quantum machine learning has emerged by integrating quantum computing with classical ML techniques. In this work, we propose the Quantum Complete Graph Neural Network (QCGNN), tailored for fully connected graphs and reducing computational complexity from O(N2) to O(N). We apply QCGNN to the jet tagging task in the datasets Top and JetNet for showing the feasibility, achieving AUCs of 0.932 and 0.822 via simulators, respectively, comparable to the leading classical models. On real quantum hardware via the IBM Quantum Platform (IBMQ), however, the AUC drops to around 0.5 due to quantum noise, indicating current devices remain impractical. The scaling behavior of QCGNN on computational complexity is further examined through runtime measurements on actual IBMQ machines.

DOI
10.1051/epjconf/202533701285/pdf

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Machine learning, particularly deep neural networks, has seen widespread application in high-energy physics. Recently, quantum machine learning has emerged by integrating quantum computing with classical ML techniques. In this work, we propose the Quantum Complete Graph Neural Network (QCGNN), tailored for fully connected graphs and reducing computational complexity from O(N2) to O(N). We apply QCGNN to the jet tagging task in the datasets Top and JetNet for showing the feasibility, achieving AUCs of 0.932 and 0.822 via simulators, respectively, comparable to the leading classical models. On real quantum hardware via the IBM Quantum Platform (IBMQ), however, the AUC drops to around 0.5 due to quantum noise, indicating current devices remain impractical. The scaling behavior of QCGNN on computational complexity is further examined through runtime measurements on actual IBMQ machines.
Machine learning, particularly deep neural networks, has seen widespread application in high-energy physics. Recently, quantum machine learning has emerged by integrating quantum computing with classical ML techniques. In this work, we propose the Quantum Complete Graph Neural Network (QCGNN), tailored for fully connected graphs and reducing computational complexity from O(N2) to O(N). We apply QCGNN to the jet tagging task in the datasets Top and JetNet for showing the feasibility, achieving AUCs of 0.932 and 0.822 via simulators, respectively, comparable to the leading classical models. On real quantum hardware via the IBM Quantum Platform (IBMQ), however, the AUC drops to around 0.5 due to quantum noise, indicating current devices remain impractical. The scaling behavior of QCGNN on computational complexity is further examined through runtime measurements on actual IBMQ machines.
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emerging field of quantum machine learning. In this paper, we propose the quantum complete graph neural network (QCGNN), which is a variational quantum algorithm-based model designed for learning on complete graphs. The QCGNN with deep parametrized operators offers a polynomial speedup over its classical and quantum counterparts, leveraging the property of quantum parallelism. We investigate the application of the QCGNN with the challenging task of jet discrimination, where the jets are represented as complete graphs. Additionally, we conduct a comparative analysis with classical models to establish a performance benchmark.
Graph Neural Networks (GNNs) are effective for analyzing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. Quantum Computing (QC) may be used to overcome these problems. For instance, Quantum Graph Neural Networks (QGNNs) have already been implemented in the literature. However, current quantum hardware limits the dimensionality of the datasets that can be encoded. For this reason, previous works simplify graph datasets manually or use small synthetic datasets. This work introduces the Guided Graph Compression (GGC) framework. It uses a graph autoencoder to reduce the size of graphs in terms of number of nodes and node feature dimensionality. The compression is guided towards enhancing the performance of a classification task by coupling the loss functions of the autoencoder and the classifier. The framework is applied to the Jet Tagging task that is relevant at CERN's LHC. It involves classifying jets as being originated from quarks or gluons. It is tested on three different training paradigms. First, a classical GNN is trained using the uncompressed data. Then, graph autoencoders are trained as a separate preprocessing step and used for classical and quantum classifiers. Lastly, the GGC framework is applied to both types of classifiers. Results show that the GGC framework outperforms the other two paradigms. Moreover, the classical and quantum classifiers achieve competitive performance in terms of ROC-AUC score.
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.
Abstract Graph neural networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. Quantum computing offers a promising avenue to address these issues and inspires new algorithmic approaches. In particular, quantum GNNs (QGNNs) have been explored in recent literature. However, current quantum hardware limits the dimension of the data that can be effectively encoded. Existing approaches either simplify datasets manually or use artificial graph datasets. This work introduces the guided graph compression (GGC) framework, which uses a graph autoencoder to reduce both the number of nodes and the dimensionality of node features. The compression is guided to enhance the performance of a downstream classification task, which can be applied either with a quantum or a classical classifier. The framework is evaluated on the Jet Tagging task, a classification problem of fundamental importance in high energy physics that involves distinguishing particle jets initiated by quarks from those by gluons. We compare GGC to a model that uses the autoencoder as a preprocessing step and to a baseline classical GNN classifier. Our numerical results demonstrate that GGC outperforms both alternatives, while also facilitating the testing of novel QGNN ansatzes on realistic datasets.
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).