期刊
ADVANCED QUANTUM TECHNOLOGIES
卷 -, 期 -, 页码 -出版社
WILEY
DOI: 10.1002/qute.202300130
关键词
boosting; ensemble learning; flavor tagging; machine learning; particle physics; quantum computing; quantum support vector machine
The recent realization of physical quantum computers with hundreds of noisy qubits has sparked a search for useful applications of their unique capabilities. Quantum machine learning (QML) has received particular attention as the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, a crucial component in experiments probing CP violation. Despite working with classically simulable QSVM architectures, the results demonstrate the potential for even higher performance when sufficiently powerful quantum hardware is developed.
The recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter-antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit-based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.
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