Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC

TL;DR

This paper demonstrates the application of quantum machine learning, specifically QSVM-Kernel, to classify Higgs boson events at the CEPC, showing comparable performance to classical methods and validating on real quantum hardware.

Contribution

It pioneers using quantum machine learning for Higgs physics at the CEPC, validating quantum algorithms on actual hardware for particle classification tasks.

Findings

Quantum SVM achieved similar accuracy to classical SVM.

Quantum hardware results approached noiseless simulator performance.

Origin and IBM quantum hardware yielded consistent results within uncertainties.

Abstract

Machine learning has blossomed in recent decades and has become essential in many fields. It significantly solved some problems in particle physics -- particle reconstruction, event classification, etc. However, it is now time to break the limitation of conventional machine learning with quantum computing. A support-vector machine algorithm with a quantum kernel estimator (QSVM-Kernel) leverages high-dimensional quantum state space to identify a signal from backgrounds. In this study, we have pioneered employing this quantum machine learning algorithm to study the $e^{+}e^{-} \rightarrow ZH$ process at the Circular Electron-Positron Collider (CEPC), a proposed Higgs factory to study electroweak symmetry breaking of particle physics. Using 6 qubits on quantum computer simulators, we optimised the QSVM-Kernel algorithm and obtained a classification performance similar to the classical support-vector machine algorithm. Furthermore, we have validated the QSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBM and Origin Quantum: the classification performances of both are approaching noiseless quantum computer simulators. In addition, the Origin Quantum hardware results are similar to the IBM Quantum hardware within the uncertainties in our study. Our study shows that state-of-the-art quantum computing technologies could be utilised by particle physics, a branch of fundamental science that relies on big experimental data.