Quantum Reservoir Computing
Assess whether quantum reservoirs (with PCA‑encoded inputs) can outperform classical ones and how entanglement changes feature mapping.
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Papers
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, rea…
Quantum Reservoir Computing Under Comparator Parity: Regime-Conditioned Advantage, Entanglement Effects, and Kernel-Null Boundaries
Quantum reservoir computing (QRC) is often evaluated with heterogeneous comparator strength, making it difficult to determine whether reported gains are genuinely quantum-mechanist…
Parity-Constrained Quantum Reservoir Computing for Image Classification: Formal Guarantees and Staged Simulation Evidence
Quantum reservoir computing has recently reported encouraging image-classification performance, yet many claims remain sensitive to fairness controls, measurement-policy confounds,…
Certified Regime Mapping for Quantum Reservoir Computing Under Parity-Constrained Evaluation
Quantum reservoir computing is frequently evaluated with point-estimate accuracy gains that confound representation effects, readout parity, and computational cost. We present a hy…
Calibrated Hybrid Evaluation of Quantum Reservoir Classification Under Finite-Shot and Simulability Constraints
Quantum reservoir computing has shown repeated empirical promise for representation learning, but the evidence base for robust quantum advantage in image classification remains fra…
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-…