We implement reference renewal to reconsider retained physical quantum-service results under a prespecified protocol. Twelve transitions retained six results each; ten completed renewal, eight met the prespecified endpoint, and five succeeded using retained results alone. The exact one-sided p=0.0546875 does not establish the recovery hypothesis. Incomplete references, binding failures and sequential waits expose operational limits. Offline reconstruction recovers decisions from preserved counts. Retrospective binding-plus-oracle checking accepts all 60 usable-transition results without renewal. With the exact oracle also supplying calibration scores, the study does not demonstrate incremental numerical-correctness utility of renewal.
Roberto Fernández-Barrios, Iker Pastor-López, Asier González-Santocildes, Pablo García Bringas
原文摘要
Agreement labels cancel from an accuracy comparison but can change an F1 ranking. We specialize partial identification to an explicit audit after labeling disagreements: two agreement counts give exact bounds, certifying a ranking or a choice within a specified F1 tolerance under every completion. Across 3,200 frozen model pairs, shared completions tighten bounds and a 0.01 tolerance reduces labeling costs. A zero-loss (weak-winner) control attributes most savings to positive tolerance, not ties. Guarantees concern the evaluated batch, not future data.
Roberto Fernández-Barrios, Iker Pastor-López, Asier González-Santocildes, Pablo García Bringas
原文摘要
Published results in quantum machine learning for intrusion detection (QML-IDS) require joint interpretation of false-positive rate (FPR), attack true-positive rate (TPR), and evaluation provenance. We reconstruct operating points in the 25-entry frame selected by Kaissar and colleagues: 21 included articles, 20 identified documentary families, substantive methodological material consulted for 15, and 24 descriptive points from five. In one test-labelled comparison, a quantum support vector machine reduces FPR from 6.875% for random forest to 3.329%, while TPR decreases from 93.955% to 93.682%; both rates improve against three other comparators. In another study, a complete benign row identifies FPR as 12/6000 (0.200%), despite incomplete attack-side allocation that requires additional assumptions for TPR. Normalized matrices identify detection ranges rather than a unique aggregate rate. A uniform provenance rule separates training-described and test-adjusted results from six conditional illustrations whose independence remains unverified. The contribution is a reproducible interpretation of favorable comparisons and detection trade-offs, without converting descriptive results or hypothetical calculations into operational guarantees.