From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910270576656384 |
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| author | Haddou, Marwan Ait Belfkir, Mohamed Harrauss, Salah Eddine El |
| author_facet | Haddou, Marwan Ait Belfkir, Mohamed Harrauss, Salah Eddine El |
| contents | In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the $HH \to b\bar{b}γγ$ final state at $\sqrt{s}$ = 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 $1.9\timesσ_{\text{SM}}$ and $2.1\timesσ_{\text{SM}}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Haddou, Marwan Ait Belfkir, Mohamed Harrauss, Salah Eddine El High Energy Physics - Experiment High Energy Physics - Phenomenology In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the $HH \to b\bar{b}γγ$ final state at $\sqrt{s}$ = 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 $1.9\timesσ_{\text{SM}}$ and $2.1\timesσ_{\text{SM}}$ 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. |
| title | From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.15672 |