Quantum-annealing-inspired algorithms for multijet clustering

TL;DR

This paper introduces quantum-annealing-inspired algorithms for multijet clustering in high energy physics, formulating the problem as a QUBO and demonstrating improved performance over previous quantum approaches.

Contribution

It presents novel quantum-annealing-inspired algorithms, including ballistic simulated bifurcation, for efficient multijet clustering directly in a single step.

Findings

Ballistic simulated bifurcation improves multijet clustering performance.

QUBO matrix distance and solver prediction power are crucial for success.

New approach enables global multijet reconstruction beyond traditional methods.

Abstract

Jet clustering or reconstruction is a crucial component at high energy colliders, a procedure to identify sprays of collimated particles originating from the fragmentation and hadronization of quarks and gluons. It is a complicated combinatorial optimization problem and requires intensive computing resources. In this study, we formulate jet reconstruction as a quadratic unconstrained binary optimization (QUBO) problem and introduce novel quantum-annealing-inspired algorithms for clustering multiple jets in electron-positron collision events. One of these quantum-annealing-inspired algorithms, ballistic simulated bifurcation, overcomes problems previously observed in multijet clustering with quantum-annealing approaches. We find that both the distance defined in the QUBO matrix and the prediction power of the QUBO solvers have crucial impacts on the multijet clustering performance. This study opens up a new approach to globally reconstructing multijet beyond dijet in one go, in contrast to the traditional iterative method.