Parameter-Efficient Quantum and Hybrid Autoencoders for One-Class Anomaly Detection

• Murilo Salem UFPel
• Daniel Pontes UFPel
• João Carrett UFPel
• Luísa Böhm UFPel
• Henrique dos Reis UFPel
• Anderson Priebe Ferrugem UFPel

DOI: https://doi.org/10.5753/sbccq.2026.23343

Resumo

This work investigates variational quantum autoencoders (QAE) for one-class anomaly detection under strict parametric constraints, shifting the evaluation focus from absolute performance to performance–capacity tradeoffs. We implement a pure QAE baseline and a Hybrid QAE with classical compression/decoding around a quantum latent block, comparing both against classical baselines - AE, VAE, Isolation Forest, and One-Class SVM - on the NSL-KDD and ECG5000 benchmarks. The central contribution is a fairnessoriented evaluation protocol combining standard detection metrics (AUC-ROC, AUC-PR, and F1) with a performance-density measure (AUC-ROC per 1,000 trainable parameters) and a fair-budget comparison against a compact classical AE. On NSL-KDD, the QAE baseline achieves 0.9612 AUC-ROC with only 476 parameters versus 0.9679 for a classical AE with 5,580 parameters, while attaining 2.0193 AUC-ROC/1k parameters against 0.1735 for the classical AE - an order-of-magnitude improvement in parametric efficiency. Under a fairbudget setting, the QAE baseline also surpasses the compact classical AE on NSL-KDD (0.9612 vs. 0.9405). In contrast, ECG5000 favors classical methods, indicating domain dependence rather than universal quantum advantage. Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.

Referências

Abreu, D., Moura, D., Rothenberg, C., and Abelém, A. (2025). Rede generativa adversarial quântica semi-supervisionada (sqgan) para detecção de ataques. In Anais do XLIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 252–265. Sociedade Brasileira de Computação.

Abreu, D., Rothenberg, C. R. E., and Abelém, A. (2024). qids: Sistema de detecção de ataques baseado em aprendizado de máquina quântico híbrido. In Anais do XLII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 295–308. Sociedade Brasileira de Computação.

Benedetti, M., Lloyd, E., Sack, S., and Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001.

Bowles, J., Ahmed, S., and Schuld, M. (2024). Better than classical? the subtle art of benchmarking quantum machine learning models. arXiv preprint arXiv:2403.07059.

Cambiucci, W., Silveira, R. M., and Ruggiero, W. V. (2024). Arquitetura para sistema de computação quântica multicore nisq com compilação sensível ao hardware. In Anais do I Workshop de Redes Quânticas, pages 7–12. Sociedade Brasileira de Computação.

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.

Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3):15:1–15:58.

Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., and Batista, G. (2015). The UCR time series classification archive. University of California, Riverside. Available at: [link].

Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874.

Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Kingma, D. P. and Welling, M. (2014). Auto-encoding variational Bayes. In International Conference on Learning Representations (ICLR).

Kingma, D. P. and Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends in Machine Learning, 12(4):307–392.

Liu, F. T., Ting, K. M., and Zhou, Z.-H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining, pages 413–422. IEEE.

Pimentel, M. A. F., Clifton, D. A., Clifton, L., and Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99:215–249.

Pinheiro, G. and Kowada, L. A. B. (2024). Automation of the quantum algorithm hhl for implementing two-dimensional svms. In Anais do I Workshop de Redes Quânticas, pages 13–18. Sociedade Brasileira de Computação.

Powers, D. M. W. (2011). Evaluation: From precision, recall and f-measure to roc, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1):37–63.

Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2:79.

Romero, J., Olson, J. P., and Aspuru-Guzik, A. (2017). Quantum autoencoders for efficient compression of quantum data. Quantum Science and Technology, 2(4):045001.

Saito, T. and Rehmsmeier, M. (2015). The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3):e0118432.

Sakurada, M. and Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis, MLSDA ’14, pages 4–11, New York, NY, USA. Association for Computing Machinery.

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443–1471.

Schuld, M. and Killoran, N. (2019). Quantum machine learning in feature hilbert spaces. Physical Review Letters, 122(4):040504.

Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the NSL-KDD data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1–6. IEEE.

Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Zheng, D., and Chen, H. (2018). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International Conference on Learning Representations (ICLR).