PoS - Proceedings of Science
Volume 390 - 40th International Conference on High Energy physics (ICHEP2020) - Parallel: Computing and Data Handling
Towards quantum-inspired Machine Learning on high-energy physics data at LHCb
D. Zuliani*, T. Felser, M. Trenti, L. Sestini, A. Gianelle, D. Lucchesi and S. Montangero
Full text: pdf
Pre-published on: January 27, 2021
Published on: April 15, 2021
Abstract
The analysis of data produced in proton-proton collisions at the Large Hadron Collider (LHC) is very challenging and it will require a huge amount of resources when High Luminosity LHC will be operational. Recently, Machine Learning methods have been employed to tackle this task, with high efficiency but low interpretability. In this study [1] a possible application of a quantum-inspired algorithm based on tree tensor networks to study simulated data at LHCb is
shown, in order to properly classify 𝑏 𝑏 ¯ di-jet events and to interpret the classification result.
DOI: https://doi.org/10.22323/1.390.0931
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