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ФИЗИКА ЭЛЕМЕНТАРНЫХ ЧАСТИЦ И АТОМНОГО ЯДРА

Гибридная квантово-классическая генеративно-состязательная сеть с прогрессивным обучением для генерации изображений: Аннотации

N. V. Ryabov, Н. В. Рябов

Published Oct 19, 2026• 0 citations
OpenAlex

Abstract

We propose a progressive training strategy for quantum generative adversarial networks (QGANs) that enables stable image generation from $4 × 4$ to $28 × 28$ pixels via alpha-blended transitions. The method is built on the QINR-QGAN architecture with parameterized quantum circuits and data re-uploading. Experimental validation on MNIST demonstrates approximately two-fold faster convergence to targetresolution quality while maintaining FID, SSIM, and PSNR metrics.

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We propose a progressive training strategy for quantum generative adversarial networks (QGANs) that enables stable image generation from 4×44 × 44×4 to 28×2828 × 2828×28 pixels via alpha-blended transitions. The method is built on the QINR-QGAN architecture with parameterized quantum circuits and data re-uploading. Experimental validation on MNIST demonstrates approximately two-fold faster convergence to targetresolution quality while maintaining FID, SSIM, and PSNR metrics.
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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 quantum statistics and the desired pixel-intensity space. We introduce a learnable Neural Noise Encoder for adaptive state preparation and a differentiable Intensity Calibration module to map measurements to a stable, visually meaningful pixel domain. Experiments on MNIST and Fashion-MNIST demonstrate that ReQGAN achieves stable training and effective image synthesis under stringent qubit budgets, with ablation studies verifying the contribution of each component.