Adiabatic Quantum Algorithm for Multijet Clustering in High Energy Physics

TL;DR

This paper introduces a quantum annealing algorithm for multijet clustering in high energy physics, demonstrating high efficiency and potential for handling complex combinatorial problems in collider data analysis.

Contribution

The paper presents a novel quantum annealing binary clustering algorithm and extends it to multijet events, improving efficiency over existing quantum methods.

Findings

Achieved approximately 96% efficiency in dijet event clustering.

Generalized the algorithm for multijet clustering.

Demonstrated potential for quantum approaches in high energy physics data analysis.

Abstract

The currently predicted increase in computational demand for the upcoming High-Luminosity Large Hadron Collider (HL-LHC) event reconstruction, and in particular jet clustering, is bound to challenge present day computing resources, becoming an even more complex combinatorial problem. In this paper, we show that quantum annealing can tackle dijet event clustering by introducing a novel quantum annealing binary clustering algorithm. The benchmarked efficiency is of the order of $96\%$, thus yielding substantial improvements over the current quantum state-of-the-art. Additionally, we also show how to generalize the proposed objective function into a more versatile form, capable of solving the clustering problem in multijet events.