Hybrid Quantum-Classical Intrusion Detection with Quantum Feature Representations under NISQ Constraints
TL;DR
This paper investigates whether quantum principal component analysis can provide useful features for IDS without relying on claims of end-to-end quantum superiority, and finds that QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.
Abstract
Intrusion detection systems must balance predictive quality, robustness, and computational cost, yet the role of quantum representations under NISQ constraints remains unclear. This paper investigates whether quantum principal component analysis (QPCA) can provide useful features for IDS without relying on claims of end-to-end quantum superiority. We evaluate PCA- and QPCA-based pipelines combined with Logistic Regression, SVM, and Random Forest, and include a QPCA→VQC branch as a comparative quantum arm. Experiments on CICIDS2017 and NSL-KDD under nisq preset and scaled preset use simulator-based quantum execution, multi-seed evaluation, Wilcoxon–Holm tests, bootstrap confidence intervals, and cost analysis. Results show no universal advantage of QPCA, but selective ranking gains: ROC-AUC improves from 0.5397 to 0.8772 (CICIDS2017) and from 0.7453 to 0.8063 (NSL-KDD), both with corrected significance under matched data budgets. Overall, QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.