Q-OPS: When Selective Quantum Machine Learning Fails—A Cost-Aware Quantum-Kernel Bandit Audit for Human–AI Allocation

Advanced computation creates economic value only when its decision contribution exceeds its resource cost. Quantum-assisted AI makes this urgent: benchmarks ask whether a quantum routine changes an output, but organizations must decide before invocation whether it is worth acquiring under partial feedback and hard constraints. We ask whether pre-invocation context can guide a learner to decline or acquire Quantum Approximate Optimization Algorithm (QAOA) evidence. In synthetic human–AI task-mode allocation, a classical procedure fixes allocations and routes inadmissible cases to review, while quantum samples remain evidence rather than decisions. We introduce Q-OPS (Quantum-Optional Policy Selection), an audit that casts decline, depth-one or depth-two QAOA as priced contextual-bandit actions and compares linear, radial-basis-function (RBF), and six-qubit fidelity-kernel representations using ridge upper-confidence bounds. Across 320 held-out instances (221 eligible), the warm-started depth-one distribution improves normalized score over uniform support sampling by 0.331; an oracle invokes on 55.7% and earns 4.245 cumulative expected proxy reward. Yet every learner invokes on 100% and earns $-2.398$, $-0.686$, or $-0.477$; aggregate losses include mandatory depth-two calls costing 0.66 although their largest archived gain is 0.494. The evidence separates reference-relative output quality, pre-invocation predictability, proxy net evidence value, and downstream decision value, which is not measured. Practitioners and policymakers should demand a decline option, resource price, pre-purchase information boundary, matched alternatives, and non-tradeable constraints. Limited to exact simulation on enumerable synthetic supports, Q-OPS motivates warm-state and classical-reference controls, fresh-instance mechanism tests, and participatory studies of costs and stakeholder-defined outcomes.