Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs
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arXiv
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| Natura: | Preprint |
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2026
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| author | Fernando, I. P. Keller, D. |
| author_facet | Fernando, I. P. Keller, D. |
| contents | We recast the case for quantum advantage in hadronic physics as an observable-by-observable question rather than a blanket claim about Quantum Chromo-Dynamics (QCD). Focusing on hadronic tomography, we analyze why Compton form factors (CFF), generalized parton distributions (GPDs), Transverse Momentum-dependent Distributions (TMDs), and Generalized Transverse Momentum-dependent Distributions (GTMDs) are natural quantum targets: they are defined by light-front, off-forward, or real-time correlation functions whose extraction from Euclidean calculations or sparse experimental data is often an ill-posed inverse problem. We separate three notions of advantage -- algorithmic, computational, and representational -- and connect each to explicit formal objects. At the algorithmic level, Hamiltonian simulation, linear-response algorithms, and amplitude-estimation primitives motivate gains for real-time and sign-problematic observables. At the computational level, direct quantum evaluation of matrix elements and correlators becomes plausible for PDFs, GPDs, timelike response, and high-energy evolution. At the inference level, recent Quantum Deep Neural Network (QDNN) studies of CFF extraction indicate improved performance in noisy and sparse regimes and motivate hybrid fits in which a quantum simulator supplies a physics prior while a classical network models detector and nuisance effects. We discuss why real-device execution is scientifically necessary, summarize current hardware milestones, and propose benchmark criteria for credible claims of quantum advantage in hadronic tomography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs Fernando, I. P. Keller, D. High Energy Physics - Phenomenology We recast the case for quantum advantage in hadronic physics as an observable-by-observable question rather than a blanket claim about Quantum Chromo-Dynamics (QCD). Focusing on hadronic tomography, we analyze why Compton form factors (CFF), generalized parton distributions (GPDs), Transverse Momentum-dependent Distributions (TMDs), and Generalized Transverse Momentum-dependent Distributions (GTMDs) are natural quantum targets: they are defined by light-front, off-forward, or real-time correlation functions whose extraction from Euclidean calculations or sparse experimental data is often an ill-posed inverse problem. We separate three notions of advantage -- algorithmic, computational, and representational -- and connect each to explicit formal objects. At the algorithmic level, Hamiltonian simulation, linear-response algorithms, and amplitude-estimation primitives motivate gains for real-time and sign-problematic observables. At the computational level, direct quantum evaluation of matrix elements and correlators becomes plausible for PDFs, GPDs, timelike response, and high-energy evolution. At the inference level, recent Quantum Deep Neural Network (QDNN) studies of CFF extraction indicate improved performance in noisy and sparse regimes and motivate hybrid fits in which a quantum simulator supplies a physics prior while a classical network models detector and nuisance effects. We discuss why real-device execution is scientifically necessary, summarize current hardware milestones, and propose benchmark criteria for credible claims of quantum advantage in hadronic tomography. |
| title | Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2604.10025 |