Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
Constraints on the $\tilde{H}$ Generalized Parton Distribution from Deep Virtual Compton Scattering Measured at HERMES
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abstract
We have analyzed the longitudinally polarized proton target asymmetry data of the Deep Virtual Compton process recently published by the HERMES collaboration in terms of Generalized Parton Distributions. We have fitted these new data in a largely model-independent fashion and the procedure results in numerical constraints on the $\tilde{H}_\mathrm{Im}$ Compton Form Factor. We present its $t-$ and $\xi-$ dependencies. We also find improvement on the determination of two other Compton Form Factors, $H_\mathrm{Re}$ and $H_\mathrm{Im}$.
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Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.