Q-RAD: A Gated Residual Architecture for Quantum-Assisted Fraud Detection, and Why Its Quantum Branch Does Not Pay
Abstract
Abstract—Hybrid quantum–classical detectors are usually
evaluated as monolithic alternatives to classical models, which
conflates two separable questions: whether the hard cases can be
identified, and whether a quantum model can resolve them. We
introduce Q-RAD, a gated residual architecture that separates
these questions by construction. A quantum-blind, quantumcost-
free gating function routes only ambiguous transactions to
a financial-transaction-specific quantum-kernel branch, whose
scores are fused residually with a classical trunk under an
explicit finite-shot budget. We predeclare the architecture, an
eight-comparison confirmatory family with Holm–Bonferroni
correction, a cost metric in quantum circuit executions, and
a refutation posture, then evaluate on the Kaggle Credit-Card
Fraud dataset over ten repeated case-control splits under three
blocking validity gates, including exact-equality replay of previously
published results. The routing half of the architecture
succeeds: the gate attains 0.889 AUROC against realized classical
errors and captures 77% of them within a 22% routing budget.
The quantum half fails: handed exactly those hard cases, the
quantum residual produced zero net corrections in nine of ten
seeds and one net regression in the tenth. Q-RAD attains mean
case-control test F1 0.916 against 0.918 for the classical trunk
and 0.922 for an otherwise identical classical second opinion; the
hypotheses of improvement over the classical trunk, over random
routing, and over a classical second opinion are all refuted after
correction. We report this refutation as predeclared, and localise
it: ambiguity in this task is identifiable from classical signals
alone, but the quantum kernel cannot exploit that signal at
any tested shot budget. Balanced-cohort metrics are benchmark
values, not deployment-prevalence estimates.
Keywords—Quantum Machine Learning; Quantum Kernel
Methods; Selective Prediction; Learning to Defer; Hybrid
Quantum-Classical Models; Financial Fraud Detection; Shot
Noise; Negative Results
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