No extra CNOT needed: trainable single-qubit rotations before readout lift Iris three-class peak from 60% to 83.33%
Variational quantum classifiers usually buy accuracy with more entangling gates, yet CNOTs err and run far slower than single-qubit gates. OQMD folds the readout map into training, using trainable single-qubit rotations to choose the measurement basis without adding any CNOT. On a 30-point stratified Iris test split, the best 0-CNOT OQMD configuration reaches 83.33% against 56.67% for the best 18-CNOT control; at 18 CNOTs OQMD raises the peak from 56.67% to 66.67%, though the pooled mean gap is small.