Higgs analysis with quantum classifiers

TL;DR

This paper develops and tests hybrid quantum-classical classifiers for Higgs boson analysis, demonstrating potential advantages of quantum machine learning methods over classical ones in low-data, limited-qubit scenarios.

Contribution

Introduces two quantum classifiers for Higgs analysis, exploring feature reduction and comparing quantum and classical models on NISQ devices.

Findings

Quantum classifiers perform comparably or better with limited training data.

Feature reduction impacts model performance significantly.

Quantum models show promise in low-qubit, NISQ hardware environments.

Abstract

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