Speaker
Description
The $Z_c(3900)$ was first discovered by the Beijing Spectrometer (BESIII) detector in 2013. As one of the most attractive discoveries of the BESIII experiment, $Z_c(3900)$ itself has inspired extensive theoretical and experimental research on its properties. In recent years, the rapid growth of massive experimental data at high energy physics (HEP) experiments have driven a lot of novel data-intensive data analysis techniques, with the Quantum Machine Learning (QML) being one of them. However, for data analysis of HEP experiments, the practical viability of QML still remains a topic of debate, requiring more examples of real data analysis with quantum hardware for its further verification.
Based on this idea, this research focuses on the application of QML in the re-discovery of $Z_c(3900)$ using the same data sample at $\sqrt{s} = 4.26 \ \mathrm{GeV}$. We developed a quantum support vector machine to distinguish the $Z_c(3900)$ signals from backgrounds, with classical cut-based and ML-based analysis strategy as references. To evaluate the impact of realistic hardware environment, the analysis will also be conducted on the Origin Quantum system based on superconducting quantum bit techniques. We carefully studied the impacts of different input features, encoding circuit structures as well as hardware noises for a better understanding of the application of QML to realistic data analysis for HEP experiments.
| Experiment context, if any | BESIII |
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