Simulability-Aware Quantum Reservoir Computing for Image Classification under Matched Readout Fairness

Quantum reservoir computing for vision tasks is often discussed in terms of empirical gains without an equally explicit separation between predictive improvement and computational-advantage claims. We study this issue in a controlled image-classification setting with PCA-compressed inputs, matched preprocessing, and identical linear readout training across classical and quantum reservoirs. The manuscript contributes a hybrid theory-plus-experiment framing: we formalize the readout stage as a strongly convex regularized problem with a unique closed-form optimum under positive regularization, derive concentration-calibrated conditions for reporting positive entanglement regimes, and define a compute-constrained Pareto boundary that filters performance gains by runtime and simulability costs. Experiments on MNIST, Fashion-MNIST, and a grayscale CIFAR-10 variant show that entangling quantum reservoirs can improve average macro-F1 against strong baselines in several configurations, but confidence-qualified positive-regime gates remain unmet under the current proxy-data execution. At the same time, symbolic and numerical checks validate the formal claims, and fixed-universe frontier audits satisfy monotonicity and nondominance requirements. The resulting evidence supports a simulability-safe conclusion: current results are strongest for formal guarantees and boundary/null characterization, while positive advantage claims remain conditional and require canonical-data reruns.