Quantum clustering and jet reconstruction at the LHC

TL;DR

This paper explores how quantum algorithms can potentially accelerate jet clustering at the LHC, offering comparable efficiency to classical methods with prospects for exponential speed-up in high-dimensional data scenarios.

Contribution

It introduces two novel quantum algorithms for distance computation and maximum searching, integrated into classical clustering methods, with potential exponential speed-up in data dimensionality.

Findings

Quantum algorithms can match classical clustering efficiency.

Potential exponential speed-up in high-dimensional data processing.

Quantum methods applicable beyond particle physics.

Abstract

Clustering is one of the most frequent problems in many domains, in particular, in particle physics where jet reconstruction is central in experimental analyses. Jet clustering at the CERN's Large Hadron Collider (LHC) is computationally expensive and the difficulty of this task will increase with the upcoming High-Luminosity LHC (HL-LHC). In this paper, we study the case in which quantum computing algorithms might improve jet clustering by considering two novel quantum algorithms which may speed up the classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, whereas the second one consists of a quantum circuit to track the maximum into a list of unsorted data. The latter algorithm could be of value beyond particle physics, for instance in statistics. When one or both of these algorithms are implemented into the classical versions of well-known clustering algorithms (K-means, Affinity Propagation and $k_T$-jet) we obtain efficiencies comparable to those of their classical counterparts. Even more, exponential speed-up could be achieved, in the first two algorithms, in data dimensionality and data length when the distance algorithm or the maximum searching algorithm are applied.