Conditional validity of quantum event classifiers under collider systematics and quantum estimation uncertainty

Abstract:Claims about a deployed quantum machine-learning classifier can fail when target data shift or when finite-shot quantum evaluation randomizes the model itself. We develop an information-conditional, fail-closed auditing framework that returns supported, refuted or unresolved verdicts with anytime-valid per-claim error control under a declared sampling protocol. On a Higgs-to-tau-tau collider benchmark, stable classifier metrics do not guarantee valid signal-strength inference: at the studied finite-template statistics, the fixed-template profile can lose coverage, even in shift-free controls, when its templates are estimated independently and template-statistical uncertainty is not modeled explicitly. Across 30 frozen finite-shot quantum-kernel deployments, every realized Gram matrix is propagated through refitting, calibration and threshold selection; in the primary raw pipeline these perturbations leave ranking nearly unchanged yet move thresholded target metrics by about 0.02, flipping ideal-anchored claims, and the diagonal-loading sensitivity decomposes differently. Matched classical controls remove apparent quantum-specific nominal-performance and sensing effects. We claim no quantum advantage.

31 pages, 11 figures. Supplementary Information is provided as ancillary material. Submitted to npj Quantum Information

arXiv:2609.02781 [quant-ph]

(or arXiv:2609.02781v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.02781 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

figS16_estimation_diagnostics.pdf

npjqi_supplementary_information.pdf

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