AN ESTIMAND-SEPARATED AUDIT OF SIMULATED VARIATIONAL QUANTUM FEATURE MAPS ON TABULAR DATA

Variational quantum circuits are widely studied as feature maps for classical data, yet benchmark conclusions disagree, in part because they depend on the comparator, the objective, and the resources charged, and because evaluations pool three distinct questions: whether circuit features improve a task model, whether a classical student can reproduce a trained map, and how circuits and classical maps compare when trained directly on the task. We audit the three separately for one angle-encoded circuit family, primarily at q=6 under analytic-statevector simulation, on tabular benchmarks, binding each result to a stated cohort, comparator, and decision rule, and find that for this family and regime the observed gains trace to the classical front end and its nonlinear expansion, with no isolated gate-angle contribution detected. As augmentation, none of 28 circuit-versus-classical transform contrasts clears the benchmark-derived rule, and the one margin over raw features that does is not confirmed on an untouched cohort. Under distillation, four of 28 classical students reproduce a cached trained map, but none remains cheaper once generating and using the teacher's outputs are both charged, so reproduction is not replacement. In head-to-head training behind the same front end, screening rejects seven of 56 contrasts, none placing a circuit above a width-matched classical map. A paired optimizer control isolates the training gain at +1.1 pp, below the 2.0 pp criterion, and training the classical compression alone recovers most of it. The record supports no circuit-free replacement, hardware or end-to-end saving, or practical advantage.