Speaker
Description
The Beijing Spectrometer (BESIII) is a detector for hadron and tau-charm physics studies. It’s located at the Beijing Electron Positron Collider (BEPCII), which runs at center-of-mass energy 2.0-5.0GeV. Zc(3900) was firstly discovered by BESIII collaboration in 2013. This exotic resonance structure is considered to be a tetra-quark state, which scientists have been looking for a long time. In recent years, the quantum computing technology evolves rapidly and shows attractive prospect. Meanwhile, the potential ability of Quantum Machine Learning (QML) has been proved in many areas.
In this presentation, we will investigate the Quantum Support Vector Machine (QSVM) method in the Zc(3900) observation at BESIII. SVM is a supervised machine learning algorithm used for data classification. For comparison, both the classical SVM and QSVM will be studied. At present, the IBM qiskit simulator is used to construct the QSVM circuit. At BESIII, more than 828$pb^{-1}$ data are collected at center-of-mass energy 4.26GeV. For the training data set, we use a mixture of Zc(3900) signals from Monte Carlo (MC) simulation and background events from the collision data. The impacts of training set volumes and input features are being carefully studied, as well as the quantum circuit optimization. By this research, we hope to reveal the feasibility of applying QML in future physics data analysis.
| Consider for long presentation | No |
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