Quantum jet clustering with LHC simulated data

TL;DR

This paper explores quantum algorithms to enhance jet clustering in high-energy physics, demonstrating potential exponential speed-ups and comparable efficiencies to classical methods through simulated data analysis.

Contribution

Introduces two novel quantum algorithms for jet clustering that can potentially accelerate classical clustering methods and achieve exponential speed-ups in certain scenarios.

Findings

Quantum algorithms can match classical clustering efficiency.

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

Quantum version of $k_T$ algorithm comparable to FastJet.

Abstract

We study the case where quantum computing could improve jet clustering by considering two new quantum algorithms that might speed up classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, while the second one consists of a quantum circuit to track the rough maximum into a list of unsorted data. When one or both algorithms are implemented in 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. Furthermore, in the first two algorithms, an exponential speed up in dimensionality and data length can be achieved when applying the distance or the maximum search algorithm. In the $k_T$ algorithm, a quantum version of the same order as FastJet is achieved.