Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier

TL;DR

This paper introduces a hybrid variational quantum classifier tailored for near-term quantum devices, demonstrating improved performance over classical neural networks in particle physics classification tasks.

Contribution

It presents a novel hybrid quantum-classical training algorithm combining quantum gradient descent with classical methods, optimized for current quantum hardware.

Findings

Outperforms classical neural networks in resonance search tasks

Effective training on small datasets

Shows promise for data-driven classification in particle physics

Abstract

Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.