Hybrid Quantum-Classical Intrusion Detection with Quantum Feature Representations under NISQ Constraints
Resumo
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.
Referências
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Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1–6.
Wang, H. and Liu, X. (2024). Auc optimization in machine learning: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. early access.
Wang, S., Fontana, E., Cerezo, M., Sharma, K., Sone, A., Cincio, L., and Coles, P. J. (2021). Noise-induced barren plateaus in variational quantum algorithms. Nature Communications, 12(1):6961.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80–83.
Arcos-Argudo, D., García, P., et al. (2025). Quantum approaches for anomaly detection: A survey. ACM Computing Surveys. to appear.
Benedetti, M., Lloyd, E., Sack, S., and Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001.
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., and Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671):195–202.
Cawley, G. C. and Talbot, N. L. C. (2010). On over-fitting in model selection and sub-sequent selection bias in performance evaluation. Journal of Machine Learning Research, 11(70):2079–2107.
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., and Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9):625–644.
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874.
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., and Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212.
Hernández-Orallo, J., Ferri, C., Lachiche, N., and Flach, P. A. (2012). A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research, 13:2813–2869.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2):65–70.
Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X. (2013). Applied Logistic Regression. Wiley, 3 edition.
Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., and McClean, J. R. (2021a). Power of data in quantum machine learning. Nature Communications, 12:2631.
Huang, H.-Y., Kueng, R., and Preskill, J. (2021b). Information-theoretic bounds on quantum advantage in machine learning. Physical Review Letters, 126(19):190505.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), volume 2, pages 1137–1143.
LaRose, R., Tikku, A., O’Neel-Judy, E., Cincio, L., and Coles, P. J. (2019). Variational quantum state diagonalization. npj Quantum Information, 5:57.
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., and Neven, H. (2018). Barren plateaus in quantum neural network training landscapes. Nature Communications, 9:4812.
Menickelly, M., Ha, Y., and Otten, M. (2023). Latency considerations for stochastic optimizers in variational quantum algorithms. Quantum, 7:949.
Mitarai, K., Negoro, M., Kitagawa, M., and Fujii, K. (2018). Quantum circuit learning. Physical Review A, 98(3):032309.
Peral-García, M. et al. (2024). On the use of precision-recall curves in imbalanced learning. Information Sciences, 640:119000.
Preskill, J. (2018). Quantum computing in the nisq era and beyond. Quantum, 2:79.
Sai, K. et al. (2025). Evaluation metrics for imbalanced classification problems. Pattern Recognition Letters. to appear.
Schreiber, F. J., Eisert, J., and Meyer, J. J. (2023). Classical surrogates for quantum learning models. Physical Review Letters, 131(10):100803.
Schuld, M., Bocharov, A., Svore, K. M., and Wiebe, N. (2020). Circuit-centric quantum classifiers. Physical Review A, 101(3):032308.
Schuld, M. and Killoran, N. (2019). Quantum machine learning in feature hilbert spaces. Physical Review Letters, 122(4):040504.
Shaikhanova, A. et al. (2025). Hybrid quantum-classical models for anomaly detection. Quantum Information Processing. to appear.
Sharafaldin, I., Lashkari, A. H., and Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), pages 108–116.
Sowmya, T. and Mary Anita, E. A. (2023). A comprehensive review of ai based intrusion detection system. Measurement: Sensors, 28:100827.
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1–6.
Wang, H. and Liu, X. (2024). Auc optimization in machine learning: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. early access.
Wang, S., Fontana, E., Cerezo, M., Sharma, K., Sone, A., Cincio, L., and Coles, P. J. (2021). Noise-induced barren plateaus in variational quantum algorithms. Nature Communications, 12(1):6961.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80–83.
Publicado
19/07/2026
Como Citar
SALEM, Murilo; PONTES, Daniel; BÖHM, Luísa; REIS, Henrique dos; ONO, Karen Satie; FERRUGEM, Anderson Priebe.
Hybrid Quantum-Classical Intrusion Detection with Quantum Feature Representations under NISQ Constraints. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO E COMUNICAÇÃO QUÂNTICAS (SBCCQ), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 48-58.
DOI: https://doi.org/10.5753/sbccq.2026.23307.
