scholarly journals Information resources in High-Energy Physics: Surveying the present landscape and charting the future course

2009 ◽  
Vol 60 (1) ◽  
pp. 150-160 ◽  
Author(s):  
Anne Gentil-Beccot ◽  
Salvatore Mele ◽  
Annette Holtkamp ◽  
Heath B. O'Connell ◽  
Travis C. Brooks
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Information Resources ◽  
The Future ◽  
Energy Physics

High Energy Physics Looks to the Future

Science ◽  
1983 ◽  
Vol 220 (4599) ◽  
pp. 809-811 ◽  
Author(s):  
M. M. WALDROP
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
The Future ◽  
Energy Physics

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

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.

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