Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits

@article{Wu2021ApplicationOQ,
  title={Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits},
  author={Sau Lan Wu and Jay Chan and Wen Guan and Shaojun Sun and Alex Zeng Wang and Chengda Zhou and Miron Livny and Federico Carminati and Alberto Di Meglio and Andy C. Y. Li and J. Lykken and Panagiotis Spentzouris and Samuel Yen-Chi Chen and Shinjae Yoo and Tzu-Chieh Wei},
  journal={Journal of Physics G: Nuclear and Particle Physics},
  year={2021}
}
  • S. Wu, J. Chan, T. Wei
  • Published 21 December 2020
  • Physics
  • Journal of Physics G: Nuclear and Particle Physics
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gatemodel quantum computing systems, we employ the quantum variational… 

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