Resource Letter AFHEP-1: Accelerators for the Future of High-Energy Physics

2012 ◽  
Vol 80 (2) ◽  
pp. 102-112
Author(s):  
William A. Barletta
Science ◽  
1983 ◽  
Vol 220 (4599) ◽  
pp. 809-811 ◽  
Author(s):  
M. M. WALDROP

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.


2009 ◽  
Vol 60 (1) ◽  
pp. 150-160 ◽  
Author(s):  
Anne Gentil-Beccot ◽  
Salvatore Mele ◽  
Annette Holtkamp ◽  
Heath B. O'Connell ◽  
Travis C. Brooks

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