PoS - Proceedings of Science
Volume 390 - 40th International Conference on High Energy physics (ICHEP2020) - Parallel: Computing and Data Handling
Quantum-inspired Machine Learning on high-energy physics data
D. Zuliani,* T. Felser, M. Trenti, L. Sestini, A. Gianelle, D. Lucchesi, S. Montangero
*corresponding author
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Pre-published on: January 27, 2021
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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, being very effective but hard to interpret. Here we study a possible application of a quantum-inspired algorithm based on tree tensor networks to study simulated data at LHCb, in order to properly classify $b\bar{b}$ di-jet events and to interpret the classification result.
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