  Quantum computing holds the promise of substantially speeding up
computationally expensive tasks, such as solving optimization problems over a
large number of elements. In high-energy collider physics, quantum-assisted
algorithms might accelerate the clustering of particles into jets. In this
study, we benchmark quantum annealing strategies for jet clustering based on
optimizing a quantity called "thrust" in electron-positron collision events. We
find that quantum annealing yields similar performance to exact classical
approaches and classical heuristics, but only after tuning the annealing
parameters. Without tuning, comparable performance can be obtained through a
hybrid quantum/classical approach.
