A Noiseless-Simulation Reference Baseline for ECG Arrhythmia Classification: Quantum-Kernel SVM and Variational Quantum Classifier vs. Classical Models on MIT-BIH

This study constructs a reproducible head-to-head comparison between two intermediate-scale quantum (NISQ) classifier families and three classical baselines for binary heartbeat classification on the MIT–BIH Arrhythmia Database, evaluated as a noiseless-simulation reference. From twenty MIT–BIH records a balanced set of three hundred Normal and premature ventricular contraction (PVC) beats is extracted with six morphological features per beat. A quantum-kernel support vector machine using a ZZFeatureMap fidelity kernel, a variational quantum classifier with an EfficientSU2 ansatz, and three classical baselines (RBF support vector machine, logistic regression, random forest) are evaluated on identical splits and then improved across six improvement rounds plus a seventh audit round, which sweep the feature map and the SVM cost, average the Gram matrices of three feature-map instances that resolve to two distinct encoders, add a projected quantum kernel and multiple-kernel learning, widen the embedding to eight qubits with two derived features and light angle-space augmentation, stack quantum and classical heads, and re-run the best recipe over five seeds. On a ninety-sample held-out test set the classical baselines reach 0.989 accuracy at millisecond wall clock, while the best quantum head at the baseline configuration trails by 22.2 accuracy points. The eight-qubit augmented kernel-sum head reaches 0.911 accuracy, and a five-seed audit of the same recipe gives 0.869±0.035, so the residual gap is stable but not closed. Per-round results, resource accounting and calibration analyses are reported in the body.