Parity-Locked Quantum Reservoir Computing for PCA-Encoded Image Classification: Robust Advantage, Entanglement Frontiers, and Operator--Dynamics Attribution

Quantum reservoir computing for image classification is currently constrained by a reproducibility problem: many reported improvements can be explained by uneven preprocessing, readout mismatch, or benchmark saturation rather than by reservoir physics. We study this issue in a parity-locked protocol where quantum and classical reservoirs share fold-local PCA, feature budget, readout family, tuning budget, and split seeds, and where a transverse-Ising reservoir is evaluated with matched entangling and non-entangling branches. We formalize three audit quantities: a robust non-easy-tier gap functional \Delta_\mathrmrob, a matched-control entanglement effect \tau(\eta,S) over noise-shot strata, and an attribution ratio \rho(\eta) that separates observable-policy gains from dynamics gains with an explicit undefined-denominator guard. Symbolic checks verify the algebraic identities used by the audit quantities, including gap-error equivalence and ratio-domain conditions. Simulation evidence on tiered image regimes shows positive robust gaps on mid and hard tiers under protocol parity, regime-dependent entanglement effects with unresolved cells at high noise and low shots, and mixed operator-vs-dynamics dominance because ratio estimates become undefined near small dynamics denominators. The resulting conclusion is conditional rather than universal: parity-controlled quantum advantage is supported in identified robustness regimes, but attribution and frontier claims require explicit caveats where uncertainty or denominator instability dominates.