This project serves to implement a common framework for applying quantum machine learning algorithms to high energy physics analysis. A major focus will be placed on classifier algorithms that are important for distinguishing signal and background events in high energy physics experiments. The framework will use the quantum variational method as a basic quantum machine learning classifier algorithm.The framework will feature a modular and extensible architecture where its components can be reused to design different machine learning algorithms such as quantum neural networks and a user may contribute to the framework by providing new algorithms and components.



Chi Lung Cheng


  • Wen Guan
  • Chen Zhou
  • shaojun sun
  • Sergei Gleyzer