Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC

Abstract

Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In this study, we employ a support vector machine with a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production in association with a top quark pair). In our quantum simulation study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum and Amazon Braket. Additionally, using 15 qubits and 100 events, the application of the QSVM-Kernel method on the IBM superconducting quantum hardware approaches the performance of a noiseless quantum simulator. Our study confirms that the QSVM-Kernel method can use the large dimensionality of the quantum Hilbert space to replace the classical feature space in realistic physics datasets.

References in corpus (19)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Quantum Machine Learning
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Supervised learning with quantum enhanced feature spaces
• Machine learning and the physical sciences
• Quantum machine learning in feature Hilbert spaces
• Power of data in quantum machine learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Deep Learning and its Application to LHC Physics
• Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector
• Observation of $\mathrm{t}\overline{\mathrm{t}}$H production
• Machine and Deep Learning Applications in Particle Physics
• Quantum Machine Learning in High Energy Physics
• Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
• Event Classification with Quantum Machine Learning in High-Energy Physics

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