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Machine learning has blossomed in recent decades and has become essential inmany fields. It significantly solved some problems for particle physics --particle reconstruction, event classification, etc. However, it is now time tobreak the limitation of conventional machine learning with quantum computing. Asupport-vector machine algorithm with a quantum kernel estimator (QSVM-Kernel)leverages high-dimensional quantum state space to identify a signal frombackgrounds. In this study, we have employed this quantum machine learningalgorithm to study the $e^{+}e^{-} \rightarrow ZH$ process at the CircularElectron-Positron Collider (CEPC), a Higgs factory proposed by Chinesescientists. Using 6-qubits on quantum computer simulators, we have optimizedthe QSVM-Kernel algorithm and obtained a similar classification performance tothe classical support-vector machine algorithm. We have also validated theQSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBMand Origin Quantum (a quantum technology company in China), where theclassification performance is approaching noiseless quantum computersimulators. Our study is one of the few examples that apply state-of-the-artquantum computing technologies to particle physics, a branch of fundamentalscience that relies on big experimental data.

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