Evaluating Modifications to Classifiers for Identification of Higgs Bosons

TL;DR

This paper explores the use of quantum machine learning to improve the classification of Higgs bosons in LHC data, addressing limitations of classical classifiers in high energy physics.

Contribution

It introduces modifications to classifiers utilizing quantum machine learning techniques to enhance Higgs boson identification accuracy.

Findings

Quantum classifiers show potential in better identifying Higgs bosons.

Modified classifiers outperform classical methods in preliminary tests.

Results suggest quantum approaches could advance particle physics data analysis.

Abstract

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).