量子编码提升生成图像质量，比传统方法更清晰。
Quantum Down Sampling Filter for Variational Auto-encoder
- 用量子编码替代传统卷积，增强特征提取能力。
- 在MNIST和USPS上弗雷谢特距离更低，重建更保真。
- 适合对图像质量要求高的生成模型研究者。
变分自编码器（VAEs）是生成建模与图像重建的基础，但其性能常难以维持高保真度。本研究提出一种混合模型——量子变分自编码器（Q-VAE），在编码器中引入量子编码，利用全连接层提取有意义表征，解码器则采用转置卷积层进行上采样。在MNIST和USPS数据集上的实验表明，Q-VAE持续优于经典VAE及使用窗口池化滤波器的直接传递VAE，表现出更低的弗雷谢特起始距离（FID）得分，说明其具有更优的图像保真度与重建质量。结果验证了Q-VAE在高质量合成数据生成和图像重建中的潜力。
原文摘要 · Abstract (English)
Variational autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, quantum variational autoencoder (Q-VAE), which integrates quantum encoding within the encoder while utilizing fully connected layers to extract meaningful representations. The decoder uses transposed convolution layers for up-sampling. The Q-VAE is evaluated against the classical VAE and the classical direct-passing VAE, which utilizes windowed pooling filters. Results on the MNIST and USPS datasets demonstrate that Q-VAE consistently outperforms classical approaches, achieving lower Fréchet inception distance scores, thereby indicating superior image fidelity and enhanced reconstruction quality. These findings highlight the potential of Q-VAE for high-quality synthetic data generation and improved image reconstruction in generative models.
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