A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments

Abstract

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.

Figures (2)

• Figure 1: A typical physics analysis procedure involving jets in high-energy physics is outlined: Panel (a) CEPCStudyGroup:2018ghi depicts the layout of the Circular Electron-Positron Collider. Electrons and positrons are accelerated in the booster and collide at the Interaction Point (IP) within the detector, illustrated in panel (b) CEPCStudyGroup:2018ghi. Quarks and gluons resulting from these collisions transform into collimated streams of particles known as jets. Panel (c) illustrates an $e^+e^-\to ZH(Z\to \nu\bar{\nu}, H\to s\bar{s})$ event, where the two quarks form two jets. Jets are clustered using an algorithm that groups final-state particles into sets corresponding to individual gluons or quarks. During jet clustering, each event is represented as a graph, as shown in panel (d). The Quantum Approximate Optimization Algorithm (QAOA) can be applied to perform jet clustering, as demonstrated in panel (e). The compiled quantum circuit is shown in panel (f). The information from reconstructed jets can be utilized to achieve related objectives, such as the $H\to s\bar{s}$ measurement shown in panel (g) PhysRevLett.132.221802.
• Figure 2: Panels (a), (b), and (c) show the jet clustering performance for 4000 events with 30 particles. (a) The jet clustering performance with k set to 6 and QAOA depths of 1, 3, and 5. (b) The performance with QAOA depth set to 3 and k set to 2, 4, 6, 7, and 8. (c) Comparison of the QAOA (with depth = 5 and k = 7), the $e^+e^-k_t$ algorithm, and the k-Means algorithm. (d) Compiled quantum circuit for performing QAOA-based jet clustering on a 6-particle event, based on the Baihua quantum hardware. The circuit has 34 CNOT gates, 27 single-qubit gates, and a depth of 27. (e) Jet clustering performance for 1217 6-particle events with the QAOA (with depth = 1 and k = 2) run on a quantum computer and a quantum simulator, and with the classical algorithms.