Quantum algorithm for the classification of Supersymmetric top quark events

TL;DR

This paper introduces a quantum computing approach using QAML-Z and quantum annealing to classify supersymmetric top quark events, potentially outperforming classical methods in high-luminosity collider environments.

Contribution

It presents a novel application of quantum annealing and the QAML-Z algorithm for particle event classification in high-energy physics.

Findings

Quantum annealing with QAML-Z improves classification accuracy.

Pre-processing with PCA enhances quantum classification performance.

Potential for quantum methods to outperform classical techniques in collider data analysis.

Abstract

The search for supersymmetric particles is one of the major goals in the next high luminosity phase of the Large Hadron Collider. Supersymmmetric top (stop) searches play a very important role in this respect, but the unprecedented collision rate that will be attained at this phase poses new challenges for the separation between any new signal and the Standard Model background. While classical multivariate techniques might be insufficient in this new environment, the massive parallelism provided by quantum computing techniques may yield an efficient solution for the problem. In this paper we make a novel application of the QAML-Z approach to classify the stop signal versus the background, and implement it in a quantum annealer machine. We show that this approach together with the pre-processing of the data with Principal Component Analysis may yield better results than conventional multivariate approaches.