Where a Domain-Specific Quantum Feature Map Pays: A Cross-Domain Replication on Three Cybersecurity Datasets
Abstract
Abstract—Claims about quantum machine learning are routinely
established on a single dataset and generalised informally.
This paper tests one such claim directly. Earlier work in this
series found, under a leakage-controlled predeclared protocol,
that a financial-transaction-specific quantum feature map does
not outperform matched classical baselines on the Kaggle Credit-
Card Fraud dataset. We freeze source-column contracts and
an experimental protocol in advance, inherit every predicted
direction from that published result, and replicate on CICIDS-
2017 (2.8 million flows) and UNSW-NB15 (257,673 records) —
six paired comparisons under one Holm–Bonferroni correction.
The replication is partial, and the failure is informative. The
map’s advantage over naive amplitude encoding replicates on
both datasets and is now established across three datasets and
two domains. But the prediction that a matched RBF-SVM would
win is significantly reversed on UNSW-NB15, where the quantum
kernel wins by a median 0.021 F1 — identifying a regime in
which the approach pays. That result is bounded: a random
forest on identical cohorts outperforms the quantum kernel on
both datasets, so no general claim of quantum advantage is
supportable. Two further findings weaken the premise of domainspecific
encoding itself. The advantage over generic quantum
encodings differs in sign between the two network datasets, and
the unanimous entanglement strength selected on financial data
does not reproduce on either. We conclude that quantum kernel
results reported on one dataset should not be assumed to transfer,
in either direction, and that predeclared cross-domain replication
is cheap enough to be routine. Balanced-cohort metrics are
benchmark values, not deployment-prevalence estimates.
Keywords—Quantum Machine Learning; Quantum Kernel
Methods; Cross-Domain Replication; Network Intrusion Detection;
Financial Fraud Detection; Predeclared Protocols; Reproducibility
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