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.

翻译：随着量子机器学习架构的日趋成熟，当前的核心挑战已不再是构建这些架构，而是识别其在哪些实际应用场景中能提供超越经典方法的优势。本研究针对数据驱动的强子物理问题，通过开发一套以定量量子限定器为核心的诊断工具，提出了一种解决该问题的框架。该工具能够依据数据的内在特性，指导在经典深度神经网络与量子深度神经网络之间进行模型选择。通过受控分类与回归研究，我们展示了模型相对性能如何随数据复杂度、噪声及维度的系统性变化而改变，并阐明了如何将这些变化趋势提炼为一种预测性准则。随后，我们通过将方法应用于深度虚康普顿散射中的康普顿形状因子提取，验证了该方法的实用性：量子限定器成功识别出量子模型更具优势的运动学区域。综上所述，这些研究成果为在精密强子物理中部署量子机器学习工具建立了一个原则性框架。