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

TL;DR

This paper demonstrates that quantum kernel methods can match classical machine learning performance in high energy physics analysis using quantum simulators and hardware, highlighting potential quantum advantages.

Contribution

It applies quantum kernel support vector machines to LHC physics data, showing comparable performance to classical methods and feasibility on current quantum hardware.

Findings

Quantum kernel SVM performs as well as classical methods on LHC data.

Quantum hardware approaches simulator performance with limited qubits.

Large quantum Hilbert space can effectively replace classical feature space.

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.