Quantum Generative Adversarial Networks for Intrusion Detection: An Experimentation on Real Quantum Devices

Intrusion Detection Systems (IDS) are vital for securing modern networks, but traditional machine learning-based anomaly detection methods often struggle with high-dimensional and imbalanced data. Although classical GANs help improve data generation, they are computationally intensive and limited in scalability. Quantum GANs (QGANs) present a promising alternative, but existing approaches are tested only in simulations. Instead, using real devices presents challenges not only because of the impact of quantum noise but, more significantly, due to usage restrictions imposed by their high cost. To cope with this, we propose a lightweight but effective hybrid QGAN for network intrusion detection, adopting a hybrid training approach by switching between an IBM real quantum device and its noisy simulator version, balancing computational feasibility and relevance in the real world. The results show that our model performs consistently, with minimal performance loss on real hardware.