QuCon-GAN: A Parameter-Efficient Quantum-Classical Conditional GAN for Handwritten Digit Generation

Abstract

The development of compact and controllable generative models under resource constraints remains a critical challenge for the practical deployment of conditional generative adversarial networks (cGANs). In this work, QuCon-GAN, a hybrid quantum-classical conditional GAN, is proposed to enhance generation expressiveness while drastically reducing parameter counts. Within the generator, dynamic data re-upload, parameterized unitary transformations, and adaptive entanglement have been incorporated into a variational quantum circuit, thereby enabling continuous semantic guidance and feature refinement across layers. When compared to classical counterparts, QuCon-GAN achieves significant improvements in parameter efficiency and conditional control, supporting full-category generation on the MNIST dataset at a resolution of 32×32 pixels. Extensive experiments have demonstrated that QuCon-GAN attains a Fréchet Inception Distance (FID) of 52.42, while maintaining stable adversarial training and avoiding mode collapse even under limited quantum resources (8 qubits). Furthermore, systematic comparisons against classical baselines and recent quantum GANs have validated the effectiveness of QuCon-GAN in balancing resource efficiency, stability, and semantic controllability. This work demonstrates the potential of hybrid quantum-classical architectures for advancing lightweight generative modeling in the Noisy Intermediate-Scale Quantum (NISQ) era.