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Compton Form Factor Extraction using Quantum Deep Neural Networks

arXiv 2504.15458 ↗First posted 21 Apr 2025cs.LG hep-ph nucl-thcited by 0

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Abstract (arXiv)

We extract Compton form factors (CFFs) from deeply virtual Compton scattering measurements at the Thomas Jefferson National Accelerator Facility (JLab) using quantum-inspired deep neural networks (QDNNs). The analysis implements the twist-2 Belitsky-Kirchner-M"uller formalism and employs a fitting strategy that emulates standard local fits. Using pseudodata, we benchmark QDNNs against classical deep neural networks (CDNNs) and find that QDNNs often deliver higher predictive accuracy and tighter uncertainties at comparable model complexity. Guided by these results, we introduce a quantitative selection metric that indicates when QDNNs or CDNNs are optimal for a given experimental fit. After obtaining local extractions from the JLab data, we perform a standard neural-network global CFF fit and compare with previous global analyses. The results support QDNNs as an efficient and complementary tool to CDNNs for CFF determination and for future multidimensional studies of parton distributions and hadronic structure.

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References · 14

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  12. 2024Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Imagescited by 5
2 more
  1. 20170.5 Petabyte Simulation of a 45-Qubit Quantum Circuitcited by 2
  2. 2021QFCNN: Quantum Fourier Convolutional Neural Networkcited by 2

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