Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics

TL;DR

This paper develops diagnostic tools, including a quantum qualifier, to guide the selection between classical and quantum neural networks in hadronic physics, based on data properties like complexity, noise, and dimensionality.

Contribution

It introduces a systematic framework and predictive criterion for model selection in quantum machine learning applied to hadronic physics problems.

Findings

Quantum qualifier effectively predicts regimes where quantum models outperform classical ones.

Model performance trends correlate with data complexity, noise, and dimensionality.

Application to Compton form factor extraction demonstrates practical utility of the approach.

Abstract

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.