  Experimental High-Energy Physics (HEP), especially the Large Hadron Collider
(LHC) programme at the European Organization for Nuclear Research (CERN), is
one of the most computationally intensive activities in the world. This demand
is set to increase significantly with the upcoming High-Luminosity LHC
(HL-LHC), and even more in future machines, such as the Future Circular
Collider (FCC). As a consequence, event reconstruction, and in particular jet
clustering, is bound to become an even more daunting problem, thus challenging
present day computing resources. In this work, we present the first digital
quantum algorithm to tackle jet clustering, opening the way for digital quantum
processors to address this challenging problem. Furthermore, we show that, at
present and future collider energies, our algorithm has comparable, yet
generally lower complexity relative to the classical state-of-the-art $k_t$
clustering algorithm.
