Work

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

Year: 2026

Type: article

Abstract: In this paper, we propose a hybrid quantum machine learning framework to improve the sensitivity of double Higgs boson searches in the $HH \rightarrow b\bar{b}\gamma \gamma$ final state at $\sqrt{s} = 13.6~\text{TeV}$. 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. In our benchmark setup, the hybrid model outperforms the pure quantum implementation and improves over the XGBoost baseline while performing similarly to the graph neural network baseline, achieving an expected 95% CL upper limit on the nonresonant double Higgs boson production cross section of $1.9\times \sigma _{\text{SM}}$ and $2.1\times \sigma _{\text{SM}}$ under background normalization uncertainties of 10% and 50%, respectively. In addition, expected constraints on the Higgs boson self-coupling $\kappa _{\lambda }$ and quartic vector-boson–Higgs coupling $\kappa _{2V}$ are improved relative to the pure quantum model and competitive with the classical baselines considered in this work.

Source: Progress of Theoretical and Experimental Physics

DOI: 10.1093/ptep/ptag079

Authors: Marwan Ait Haddou, Mohamed Belfkir, Salah Eddine Elharrauss

Institutions: University of Hassan II Casablanca, United Arab Emirates University

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $979

Funder: United Arab Emirates University