[2512.05069] Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Computer Science > Machine Learning arXiv:2512.05069 (cs) This paper has been withdrawn by Mohammad Arif Rasyidi [Submitted on 4 Dec 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection Authors:Mohammad Arif Rasyidi, Omar Alhussein, Sami Muhaidat, Ernesto Damiani View a PDF of the paper titled Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection, by Mohammad Arif Rasyidi and 3 other authors No PDF available, click to view other formats Abstract:Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need f...