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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]
Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 176223992
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Application of quantum machine learning in a Higgs physics study at the CEPC.
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 1/10/2024
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