• Corpus ID: 252531308

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

@inproceedings{Fadol2022ApplicationOQ,
  title={Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC},
  author={Abdualazem Fadol and Qiyu Sha and Yaquan Fang and Zhanghai Li and Sitian Qian and Yuyang Xiao and Yu Zhang and Chen Zhou},
  year={2022}
}

• Abdualazem Fadol, Qiyu Sha, +5 authors Chen Zhou
• Published 26 September 2022
• Computer Science, Physics

Machine learning has blossomed in recent decades and has become essential in many fields. It sig- nificantly solved some problems for 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 employed this quantum machine learning algorithm to study the 𝑒 + 𝑒 − → 𝑍𝐻 process at the Circular Electron-Positron Collider (CEPC), a Higgs factory proposed by Chinese scientists. Us- ing 6-qubits on quantum computer simulators, we have optimized the QSVM-Kernel algorithm and obtained a similar classification performance to the classical support-vector machine algorithm. We have also validated the QSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBM and Origin Quantum (a quantum technology company in China), where the classification performance is approaching noiseless quantum computer simulators. Our study is one of the few examples that apply state-of-the-art quantum computing technologies to particle physics, a branch of fundamental science that relies on big