July 30, 2026 to August 5, 2026
Natal Convention Center
America/Sao_Paulo timezone

Quantum Computing for Parton Shower and Hadronization

Jul 30, 2026, 9:45 AM
15m
Room #1 (Praiamar Natal Hotel & Convention)

Room #1

Praiamar Natal Hotel & Convention

R. Francisco Gurgel, 33 - Ponta Negra, Natal - RN, 59090-050
Talk Artificial Intelligence, Machine Learning and Quantum Computing in HEP Artificial Intelligence, Machine Learning and Quantum Computing in HEP

Speaker

Prof. Ying-Ying Li (Institute of High Energy Physics, CAS)

Description

Spin correlations provide a powerful probe of the Standard Model (SM), physics beyond the SM (BSM), and the quantum nature of high-energy interactions. While such effects have been extensively studied for heavy particles, accessing spin correlations of light partons that hadronize into jets remains a longstanding challenge. In this work, we study parton spin correlations in hadronic Higgs decays, focusing on the channel (H \to gg). We investigate two spin-sensitive observables: a Lund-plane--based jet variable and the four-point energy--energy correlator (E4C) constructed from particles inside jets. This represents the first application of E4C to probe parton spin correlations in Higgs decays and BSM scenarios. Our analysis is performed in the clean environment of (ZH \to \nu\bar{\nu}H) production at future lepton colliders, where jet reclustering effects are minimized. In parallel, we develop a quantum-computing framework for simulating non-Abelian gauge dynamics relevant to parton evolution, introducing a block-encoding method for discrete subgroups of (SU(2)) and (SU(3)). Resource estimates and hardware benchmarks demonstrate both the feasibility and current limitations of this approach. Together, these results advance the study of quantum correlations in QCD and their potential as probes of new physics.

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Authors

Yi-Lin Wang Prof. Ying-Ying Li (Institute of High Energy Physics, CAS) Ying-Ying Li (Institute of High Energy Physics, Chinese Academy of Sciences)

Co-authors

Huaxing Zhu (Peking University) Jun Gao (Shanghai Jiao Tong University)

Presentation materials