From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics

2025/11/19 by Haddou, Marwan Ait, Belfkir, Mohamed, Harrauss, Salah Eddine El · 1 citation
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Particle physics theoretical and experimental studies #Quantum Computing Algorithms and Architecture

paper · doi:10.48550/arxiv.2511.15672

openalex publication_date 2025/11/19 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the HH → bbγγ final state at √(s) = 13.6 TeV. The proposed model combines parameterized quantum circuits with a classical neural network meta-model, enabling event-level features to be embedded in a quantum feature space while maintaining the optimization stability of classical learning. The hybrid model outperforms both a state-of-the-art XGBoost model and a purely quantum implementation by a factor of two, achieving an expected 95% CL upper limit on the non-resonant double Higgs boson production cross-section of 1.9×σ_SM and 2.1×σ_SM under background normalization uncertainties of 10% and 50%, respectively. In addition, expected constraints on the Higgs boson self-coupling κ_λ and quartic vector-boson-Higgs coupling κ_2V are found to be improved compared to the classical and purely quantum models.

Citations

• Sculpting Quantum Landscapes: Fubini-Study Metric Conditioning for Geometry Aware Learning in Parameterized Quantum Circuits
• Transforming jet flavour tagging at ATLAS
• Novel machine learning applications at the LHC
• A comprehensive guide to the physics and usage of PYTHIA 8.3
• A Living Review of Machine Learning for Particle Physics
• Quantum circuits of CNOT gates
• Meta-Learning in Neural Networks: A Survey
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Theory and phenomenology of two-Higgs-doublet models

Cited by

• Probing new physics in the Boosted HH → bbγγ channel at the LHC

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• Quantum Metric Learning for New Physics Searches at the LHC
• Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning