James Appenzeller
Hardware validation on IBM’s 133-qubit Heron processor demonstrates that a quantum neural architecture can reliably distinguish neural configurations with 99.3% discrimination quality. The A-Gate circuit encodes excitatory-inhibitory dynamics from EEG signals using paired qubits, achieving O(M) gate complexity for M-channel correlation encoding versus $$O(M^2)$$ classical computation on the symmetric positive definite (SPD) manifold. Transpilation to native CZ gates follows the predicted linear law over the implemented range ( $$14.1M - 17.5$$ , $$R^2 > 0.99$$ for $$M \le 8$$ ); extending to $$M = 32$$ reveals super-linear growth from topology-induced routing overhead, which we report as a hardware limitation distinct from the architecture’s O(M) logical complexity. Evaluation on the CHB-MIT Scalp EEG Database using leave-one-subject-out (LOSO) cross-validation across 22 subjects reveals a critical encoding bottleneck: the quantum fidelity classifier achieves 53.4% AUC compared to 62.5% for an 8-channel classical baseline. Notably, per-subject AUC ranges from 0.183 to 1.000, with sub-chance performance in some cases suggesting what we term manifold polarity, a hypothesized patient-specific geometric orientation that may cause cross-subject template mismatch. The gap between quantum and classical performance indicates that mapping EEG features to circuit parameters, not the A-Gate architecture itself, limits classification. We provide complexity analysis with corrections to common quantum-advantage overclaims, and we are explicit that the present results establish hardware feasibility and the location of the performance bottleneck rather than clinical utility.