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arXiv:2403.04990 · Physical review. D/Physical review. D.

Jet discrimination with a quantum complete graph neural network

Yi An Chen, K. F. Chen

Published Jan 21, 2025• 11 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1103/physrevd.111.016020

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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.
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.
Abstract Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic jet images. In this paper, we use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN using a classical noiseless simulator. We compare various setups for the QCNN, varying the convolutional circuit, type of encoding, loss function, and batch sizes. For every quantum setup, we design a similar setup to the corresponding classical model for a fair comparison. Our results indicate that, using a classical simulator, QCNN with proper setups tend to perform better than their CNN counterparts, especially when the convolution block has a lower number of parameters. For the higher parameter regime, the QCNN circuit was adjusted according to the dimensional expressivity analysis (DEA) to lower the parameter count while preserving its optimal structure. The DEA circuit demonstrated improved results over the comparable classical CNN model.
Machine Learning algorithms are playing a fundamental role in solving High Energy Physics tasks. In particular, the classification of hadronic jets at the Large Hadron Collider is suited for such types of algorithms, and despite the great effort that has been put in place to tackle such a classification task, there is room for improvement. In this context, Quantum Machine Learning is a new methodology that takes advantage of the intrinsic properties of quantum computation (e.g. entanglement between qubits) to possibly improve the performance of a classification task. In this contribution, a new study of Quantum Machine Learning applied to jet identification is presented. Namely, a Variational Quantum Classifier is trained and evaluated on fully simulated data of the LHCb experiment, in order to identify jets containing a hadron formed by a bbb or bˉ\bar{b}bˉ quark at the moment of production. The jet identification performance of the quantum classifier is compared with a Deep Neural Network using the same input features.
The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than 50%50\%50% of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields.
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.