Higgs analysis with quantum classifiers

We have developed two quantum classifier models for the ttH classification problem, both of which fall into category hybrid quantumclassical algorithms Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof concept that Machine Learning (QML) methods can similar or better performance, in specific cases low number training samples, respect to conventional ML even limited qubits available current hardware. To utilise — accommodate limitations simulation hardware and real we investigated different feature reduction methods. Their impact on performance classical was assessed. addressed implementations QML models, representative main approaches supervised machine learning today: Support Vector (QSVM), kernel-based method, Variational Circuit (VQC), variational approach.