Hybrid Quantum Generative Adversarial Networks: A Technical Review of Implicit Quantum Models and Hybrid Optimization
Wael Maged Badawy
Hybrid Quantum Generative Adversarial Networks (HQ-GANs) have emerged as a promising class of models at the intersection of quantum computing and generative artificial intelligence. Unlike early quantum generative approaches that relied on fully quantum pipelines, hybrid QGANs combine parameterized quantum circuits with classical optimization and discrimination mechanisms, enabling practical training under the constraints of noisy intermediate-scale quantum (NISQ) hardware. This paper presents a comprehensive technical review of hybrid QGAN architectures, with a particular focus on implicit quantum representations and hybrid optimization strategies that enhance expressivity, stability, and scalability. We analyze how recent advances in variational circuit design, data re-encoding, Wasserstein-based objectives, and hybriddepth control have reshaped the feasibility of quantum generative learning. New mathematical formulations are introduced to describe implicit quantum mappings and hybrid adversarial convergence. Comparative analysis highlights performance trends, architectural trade-offs, and unresolved challenges related to noise, barren plateaus, and hardware–software mismatch. The paper concludes by outlining future research directions toward robust, application-ready quantum generative intelligence.
•Dec 14, 2025•2 citations