image: IMAGE: THE APPLICATION OF THE QUANTUM APPROXIMATE OPTIMIZATION ALGORITHM (QAOA) TO JET CLUSTERING. (A) ELECTRON-POSITRON COLLISIONS: WHEN ELECTRONS AND POSITRONS COLLIDE, THE RESULTING QUARKS AND GLUONS UNDERGO HADRONIZATION, FORMING COLLIMATED STREAMS OF PARTICLES KNOWN AS JETS. (B) GRAPH REPRESENTATION OF COLLISION EVENTS: EACH COLLISION EVENT CAN BE MAPPED ONTO A GRAPH, WHERE THE NODES REPRESENT PARTICLES, AND THE EDGES ENCODE THEIR KINEMATIC RELATIONSHIPS. (C) QAOA FOR JET CLUSTERING: JET CLUSTERING IS ANALOGOUS TO SOLVING A MAX-CUT PROBLEM, MAKING QAOA A SUITABLE ALGORITHM TO OPTIMIZE THIS PROCESS. (D) QUANTUM CIRCUIT COMPILATION: THE QAOA QUANTUM CIRCUIT IS DESIGNED AND COMPILED TO MATCH THE TOPOLOGY OF SUPERCONDUCTING QUBITS AND THE SET OF AVAILABLE QUANTUM GATES ON THE QUANTUM PROCESSOR. (E) PERFORMANCE EVALUATION: THE PERFORMANCE OF QAOA-BASED JET CLUSTERING IS ASSESSED USING THE ANGULAR DISTANCE BETWEEN THE RECONSTRUCTED JET AND THE CORRESPONDING QUARK, SERVING AS THE EVALUATION METRIC.
Credit: ©Science China Press
Bridging the gap between quantum technologies and fundamental sciences opens pathways for groundbreaking innovations in both domains. In high-energy particle collisions, quarks and gluons are produced and rapidly transform into collimated sprays of particles called jets. Accurate jet clustering is essential, preserving the information of the originating quark or gluon and forming the basis for multiple physics measuerments at high energy collider, especially for probing the properties of the Higgs boson, the particle responsible for the mechanism of mass generation.
The rapid advancement of quantum algorithms and hardware has enabled small-scale yet representative applications on quantum computers. The Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical framework, is well-suited for addressing classical combinatorial optimization problems. As high-energy physics experiments evolve with higher collision energies and luminosities, the demand for innovative computational tools grows. Jet clustering, pivotal for studies involving quarks and gluons, can be modeled as a combinatorial optimization problem, making it an ideal candidate for quantum computing applications.
Recently, a research team led by Prof. Chen Zhou from Peking University, Prof. Dong E. Liu from Tsinghua University, and Prof. Manqi Ruan from the Institute of High Energy Physics made a significant breakthrough. Published in Science Bulletin, their study, for the first time, applies QAOA to jet clustering. By mapping collision events onto graphs—representing particles as nodes and angular separations as edges—the team utilized QAOA to address the clustering problem with available quantum resources. Using simulations of up to 30 qubits and quantum hardware with 6 qubits, the study demonstrated that QAOA achieves jet clustering performance comparable with classical algorithms for small-scale problems. This achievement underscores the potential of quantum computing in improving jet clustering, paving the way for its practical application in high-energy physics experiments.
See the article:
A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments
https://doi: 10.1016/j.scib.2024.12.020
Journal
Science Bulletin