scholarly journals Quantum machine learning in high energy physics

Author(s):  
Wen Guan ◽  
Gabriel Perdue ◽  
Arthur Pesah ◽  
Maria Schuld ◽  
Koji Terashi ◽  
...  
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.


2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.


2019 ◽  
Vol 214 ◽  
pp. 06037
Author(s):  
Moritz Kiehn ◽  
Sabrina Amrouche ◽  
Paolo Calafiura ◽  
Victor Estrade ◽  
Steven Farrell ◽  
...  

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.


2021 ◽  
Vol 16 (08) ◽  
pp. P08016
Author(s):  
T.M. Hong ◽  
B.T. Carlson ◽  
B.R. Eubanks ◽  
S.T. Racz ◽  
S.T. Roche ◽  
...  

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Aishik Ghosh ◽  
Benjamin Nachman ◽  
Daniel Whiteson

Sign in / Sign up

Export Citation Format

Share Document