Application of Quantum Machine Learning using the Quantum Kernel
  Algorithm on High Energy Physics Analysis at the LHC
Quantum machine learning could possibly become a valuable alternative to
classical machine learning for applications in High Energy Physics by offering
computational speed-ups. In this study, we employ a support vector machine with
a quantum kernel estimator (QSVM-Kernel method) to a recent LHC flagship
physics analysis: $t\bar{t}H$ (Higgs boson production in association with a top
quark pair). In our quantum simulation study using up to 20 qubits and up to
50000 events, the QSVM-Kernel method performs as well as its classical
counterparts in three different platforms from Google Tensorflow Quantum, IBM
Quantum and Amazon Braket. Additionally, using 15 qubits and 100 events, the
application of the QSVM-Kernel method on the IBM superconducting quantum
hardware approaches the performance of a noiseless quantum simulator. Our study
confirms that the QSVM-Kernel method can use the large dimensionality of the
quantum Hilbert space to replace the classical feature space in realistic
physics datasets.
Sau Lan Wu Shaojun Sun Wen Guan Chen Zhou Jay Chan Chi Lung Cheng Tuan Pham Yan Qian Alex Zeng Wang Rui Zhang Miron Livny Jennifer Glick Panagiotis Kl. Barkoutsos Stefan Woerner Ivano Tavernelli Federico Carminati Alberto Di Meglio Andy C. Y. Li Joseph Lykken Panagiotis Spentzouris Samuel Yen-Chi Chen Shinjae Yoo Tzu-Chieh Wei