用量子指标判断何时该用量子模型，提升强子物理精度
Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics
- 基于数据特性设计量子优势判定指标，指导模型选择
- 在复杂度、噪声、维度变化下，量子模型表现呈可预测趋势
- 已应用于深度虚拟康普顿散射的形因子提取，验证有效性
随着量子机器学习架构日益成熟，核心挑战已从构建转向识别其相对于经典方法的实际优势场景。本文提出一套针对数据驱动型强子物理问题的框架，通过开发以量化量子指标为核心的诊断工具，依据数据内在属性指导经典与量子深度神经网络的模型选择。通过受控的分类与回归实验，揭示了模型相对性能随数据复杂度、噪声水平和维度变化的系统性规律，并将其提炼为可预测的判别准则。进一步将该方法应用于深度虚拟康普顿散射中的康普顿形因子提取，量子指标成功识别出量子模型占优的运动学区域。上述结果建立了一个在高精度强子物理中部署量子机器学习工具的系统性方法。
原文摘要 · Abstract (English)
As quantum machine-learning architectures mature, a central challenge is no longer their construction, but identifying the regimes in which they offer practical advantages over classical approaches. In this work, we introduce a framework for addressing this question in data-driven hadronic physics problems by developing diagnostic tools - centered on a quantitative quantum qualifier - that guide model selection between classical and quantum deep neural networks based on intrinsic properties of the data. Using controlled classification and regression studies, we show how relative model performance follows systematic trends in complexity, noise, and dimensionality, and how these trends can be distilled into a predictive criterion. We then demonstrate the utility of this approach through an application to Compton form factor extraction from deeply virtual Compton scattering, where the quantum qualifier identifies kinematic regimes favorable to quantum models. Together, these results establish a principled framework for deploying quantum machine-learning tools in precision hadronic physics.
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