AI RESEARCH
End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
arXiv CS.CV
•
ArXi:2603.18554v1 Announce Type: cross Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying on classical post-processing or patch-based methods. These approaches dilute the quantum generator's role and struggle to capture global image semantics. To address this, we propose ReQGAN, an end-to-end framework that synthesizes an entire N=2^D-pixel image using a single D-qubit quantum circuit.