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.
Figure 3 shows representative his- tograms of theℜeEextraction from 1000 noisy repli- cas of the cross-section pseudodata for a fixed kinematic setting
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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.