Post-measurement readout and class-resolved hardware sensitivity of a variational quantum classifier on two IBM Heron r2 backends

Variational quantum classifiers (VQCs) are increasingly evaluated on real quantum processors, yet their deployment behaviour can depend on post-measurement readout and variation across independently trained models. We study a four-qubit VQC for three-class traffic-state classification on two IBM Heron r2 backends. In a single-seed comparison, a four-channel linear-projection readout achieves higher clean balanced accuracy than a three-channel direct-argmax readout (94.4% versus 78.9%), whereas their hardware recoveries on ibm_kingston are 53.0% and 94.4%, respectively. A matched synthetic-noise control shows that prediction stability does not imply higher absolute noisy accuracy. Across five independently trained direct-argmax models, the clearest backend difference occurs for the moderate class and favours ibm_marrakesh ; the congested-class difference is weaker and shot-dependent, whereas free-flow recall remains stable. Finite-shot disagreement follows the inverse of the class-mean margin ordering and decreases with increasing shots, but the hardware differences do not exhibit the same monotonic behaviour. Register-level assignment-matrix indicators show directional alignment with these differences without establishing causality. Temperature scaling reduces ECE, although the residual near 0.17 is empirical rather than structural. These results demonstrate the complementary roles of readout strategy, class-resolved metrics, decision margins, and seed-level replication in hardware evaluation of VQCs.