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Full-Text Articles in Quantum Physics
Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides
Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides
The Graduate School Posters
Future particle accelerators will exceed by far the current data size (1015) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …
Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato
Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato
College of Sciences Posters
No abstract provided.