Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics
This provides a principled framework for deploying quantum machine-learning tools in precision hadronic physics, addressing a domain-specific problem for researchers in that field.
The paper tackles the challenge of determining when quantum machine-learning models outperform classical ones in hadronic physics by developing diagnostic tools, including a quantitative quantum qualifier, to guide model selection based on data properties, and demonstrates its utility in Compton form factor extraction.
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.