End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
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. ReQGAN overcomes two fundamental bottlenecks hindering direct pixel generation: (1) the rigid classical-to-quantum noise interface and (2) the output mismatch between normalized...
| Authors | Xue Yang, Rigui Zhou, Shizheng Jia et al. |
|---|---|
| Primary category | quant-ph |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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