Speaker
Mr
Yongfeng Zhu
(PKU)
Description
The rapid development of Deep Learning and Quantum Computing has benefited or potentially will benefit high-energy physics experiments. To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet-origin identification, which categorizes jets into five quark species (b, c, s, u, d), their corresponding antiquarks, and the gluon. We uniquely solve jet clustering using the Quantum Approximate Optimization Algorithm (QAOA). For small-scale jet clustering problems, the QAOA has achieved performance similar to the classical jet clustering algorithm, ee_kt.
Primary author
Mr
Yongfeng Zhu
(PKU)
