  Exploring the application of quantum technologies to fundamental sciences
holds the key to fostering innovation for both sides. In high-energy particle
collisions, quarks and gluons are produced and immediately form collimated
particle sprays known as jets. Accurate jet clustering is crucial as it retains
the information of the originating quark or gluon and forms the basis for
studying properties of the Higgs boson, which underlies teh mechanism of mass
generation for subatomic particles. For the first time, by mapping collision
events into graphs--with particles as nodes and their angular separations as
edges--we realize jet clustering using the Quantum Approximate Optimization
Algorithm (QAOA), a hybrid quantum-classical algorithm for addressing classical
combinatorial optimization problems with available quantum resources. Our
results, derived from 30 qubits on quantum computer simulator and 6 qubits on
quantum computer hardware, demonstrate that jet clustering performance with
QAOA is comparable with or even better than classical algorithms for a
small-sized problem. This study highlights the feasibility of quantum computing
to revolutionize jet clustering, bringing the practical application of quantum
computing in high-energy physics experiments one step closer.
