  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.
