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Application of quantum machine learning in a Higgs physics study at the CEPC.

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Title: Application of quantum machine learning in a Higgs physics study at the CEPC.
Authors: Fadol, Abdualazem1,2 (AUTHOR), Sha, Qiyu1,3 (AUTHOR), Fang, Yaquan1,3 (AUTHOR), Li, Zhan1,3 (AUTHOR), Qian, Sitian4 (AUTHOR), Xiao, Yuyang4 (AUTHOR), Zhang, Yu5 (AUTHOR), Zhou, Chen4 (AUTHOR) czhouphy@pku.edu.cn
Source: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 1/10/2024, Vol. 39 Issue 1, p1-15. 15p.
Subjects: International Business Machines Corp., Electroweak interactions, Quantum computers, Machine learning, Symmetry breaking, Quantum computing, Particle physics, Computers
Abstract: 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 − → Z H 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 optimized 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 utilized by particle physics, a branch of fundamental science that relies on big experimental data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract: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 − → Z H 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 optimized 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 utilized by particle physics, a branch of fundamental science that relies on big experimental data. [ABSTRACT FROM AUTHOR]
ISSN:0217751X
DOI:10.1142/S0217751X24500076