TL;DR
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 via alpha-blended transitions is proposed.
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 glob...
Simulated experiments show that this reformulate quantum image generation as coordinate-conditioned implicit function learning outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.
Synthetic biological data play a crucial role in bioinformatics research, particularly in scenarios involving limited data availability and strict privacy constraints. However, conventional generative adversarial networks often face challenges in simultaneously preserving local statistical characteristics and global da...
This paper presents a hybrid quantum-classical framework for grayscale image compression and decompression, leveraging the strengths of quantum computing and deep learning. The compression pipeline integrates a Variational Quantum Daubechies Wavelet Transform (V-QDWT) and a trainable Quantum Convolutional Neural Networ...
Medical images are highly susceptible to noise during the image acquisition process that will adversely affect the diagnosis accuracy. There are lots of classical methods available for denoising but they often struggle with the complex and high noise and over smooth the anatomical structures cause loss of fine details...
StyleGANCA is proposed, the first lightweight general-purpose NCA-based generative adversarial network, enabling latent-controlled image generation through iterative local interactions and achieving competitive image quality with substantially fewer parameters than baseline architectures.
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