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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16073 |
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| _version_ | 1866909935645753344 |
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| author | de Lejarza, Jorge J. Martínez Wu, Hsin-Yu Kyriienko, Oleksandr Rodrigo, Germán Grossi, Michele |
| author_facet | de Lejarza, Jorge J. Martínez Wu, Hsin-Yu Kyriienko, Oleksandr Rodrigo, Germán Grossi, Michele |
| contents | Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficiently learning and sampling these distributions remains challenging. We propose a quantum protocol for a bivariate probabilistic model based on shifted Chebyshev polynomials, trained as a circuit-based representation of two correlated variables, with sampling performed via quantum Chebyshev transforms. As a key application we study fragmentation functions (FFs) of charged pions and kaons from single-inclusive hadron production in electron-positron annihilation. We learn the joint distribution of momentum fraction $z$ and energy scale $Q$, and infer their correlations from the entanglement structure. Building on the generalization capabilities of the quantum model and extended register architecture, we perform fine-grid multivariate sampling for FF dataset augmentation. Our results highlight the growing potential of quantum generative modeling to advance data analysis and scientific discovery in high-energy physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Chebyshev Probabilistic Models for Fragmentation Functions de Lejarza, Jorge J. Martínez Wu, Hsin-Yu Kyriienko, Oleksandr Rodrigo, Germán Grossi, Michele Quantum Physics High Energy Physics - Phenomenology Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficiently learning and sampling these distributions remains challenging. We propose a quantum protocol for a bivariate probabilistic model based on shifted Chebyshev polynomials, trained as a circuit-based representation of two correlated variables, with sampling performed via quantum Chebyshev transforms. As a key application we study fragmentation functions (FFs) of charged pions and kaons from single-inclusive hadron production in electron-positron annihilation. We learn the joint distribution of momentum fraction $z$ and energy scale $Q$, and infer their correlations from the entanglement structure. Building on the generalization capabilities of the quantum model and extended register architecture, we perform fine-grid multivariate sampling for FF dataset augmentation. Our results highlight the growing potential of quantum generative modeling to advance data analysis and scientific discovery in high-energy physics. |
| title | Quantum Chebyshev Probabilistic Models for Fragmentation Functions |
| topic | Quantum Physics High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2503.16073 |