Parameter-Efficient Quantum and Hybrid Autoencoders for One-Class Anomaly Detection
Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.
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Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.
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.
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