Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation
Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing attention in quantum machine learning. However, most existing QGAN methods rely on patch-based decomposition strategies, which weaken the global consistency of generated images and increase quantum resource overhead. In this work, we investigate a simpler approach: pixel-level, end-to-end image generation using a single-quantum-circuit QGAN. By analyzing the structural matching relationship between the quantum prior and the target data distribution in Hilbert space, we provide a new theoretical perspective for und...
| Authors | Xue Yang, Rigui Zhou, Dax Enshan Koh et al. |
|---|---|
| Primary category | quant-ph |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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