Quantum-Assisted Generative Anomaly Detection: A Dimension-Controlled Study of Latent Refinement

arXiv:2611.13671v1 Announce Type: new Abstract: Anomaly-based malware detectors learn what benign files look like and flag deviations. Generative approaches to this problem can make use of a "latent" or "prior" data representation, which is often chosen to be a Gaussian distribution. We ask whether the choice of prior distribution matters, and whether one produced by quantum hardware behaves differently from a classical one. Building on SAGEGAN, a style-based detector trained only on benign data, we compare three latent distributions: the original 512-dimensional Gaussian, a 48-dimensional Gaussian, and a 48-dimensional distribution sampled from an ORCA Computing PT-2 photonic quantum processor.