Quantum Feature Accessibility as a Mechanism for Shortcut Learning under Distribution Shift.

Which features do quantum machine learning (QML) models learn when stable and shortcut signals compete? We show that statistical predictiveness alone does not explain feature reliance: quantum processing makes some predictive signals more accessible than others. We develop a learning-theoretic account of quantum feature accessibility and prove, under explicit assumptions, that population-risk minimizers of local quantum models with affine heads rely only on a competing cue, even when joint features encode label information. Controlled interventions and encoding ablations reveal how the same processing bias can favor stable signals or shortcuts. Experiments on realistic shifts show task-dependent generalization, while MonarQ evaluations reveal hardware-dependent changes in stable-signal prediction. Together, these results link quantum processing to learned reliance and show why out-of-distribution (OOD) generalization depends on the stability of the signals used.