用量子启发神经网络从实验数据中提取强子结构关键参数
Compton Form Factor Extraction using Quantum Deep Neural Networks
- 用量子启发深度网络拟合杰斐逊实验室的散射数据
- 在相同复杂度下，量子网络预测更准、误差更小
- 适合需要高精度分析的粒子物理研究者
我们利用量子启发深度神经网络（QDNNs）从杰斐逊国家加速器实验室（JLab）的深度虚拟康普顿散射测量中提取康普顿形式因子（CFFs）。分析基于扭度-2的贝利茨基-基尔彻-穆勒形式理论，采用模拟标准局部拟合策略。通过伪数据对比，发现QDNNs在模型复杂度相当的情况下，通常具备更高的预测精度和更紧的不确定性。基于此结果，提出一个定量选择指标，用于判断在特定实验拟合中应使用QDNN还是经典深度神经网络（CDNN）。对JLab数据进行局部提取后，进一步执行标准神经网络全局拟合，并与以往全局分析结果对比。结果表明，QDNN是确定CFF的有效且互补工具，适用于未来多维部分子分布与强子结构的研究。
原文摘要 · Abstract (English)
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-Müller 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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