  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.
