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Application of quantum machine learning using variational quantum classifier in accelerator physics
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DOI：10.1007/s41365-026-02016-y.png)
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Abstract
En 中文
Quantum machine learning algorithms aim to take advantage of quantum computing to improve classical machine learning algorithms. In this study, we apply a quantum machine learning algorithm and a variational quantum classifier to accelerator physics for the first time. Specifically, we utilize a variational quantum classifier to evaluate the dynamic aperture of a diffraction-limited storage ring. We demonstrate that the variational quantum classifier can achieve good accuracy much faster than the classical artificial neural network, with the statistics of the training samples increasing. The accuracy of the variational quantum classifier is always higher than that of an artificial neural network, although it is very close when the statistics of the training samples are high. Furthermore, we investigate the impact of noise on the variational quantum classifier and show that it maintains robust performance even in the presence of noise.
Keywords:
Accelerator physics
Quantum machine learning
Variational quantum classifier
Noisy intermediate-scale quantum computers
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