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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.10902 |
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Table of Contents:
- The Higgs boson, discovered back in 2012 through collision data at the Large Hadron Collider (LHC) by ATLAS and CMS experiments, marked a significant inflection point in High Energy Physics (HEP). Today, it's crucial to precisely measure Higgs production processes with LHC experiments in order to gain insights into the universe and find any invisible physics. To analyze the vast data that LHC experiments generate, classical machine learning has become an invaluable tool. However, classical classifiers often struggle with detecting higgs production processes, leading to incorrect labeling of Higgs Bosons. This paper aims to tackle this classification problem by investigating the use of quantum machine learning (QML).