Quantum Down Sampling Filter for Variational Auto-encoder

• URL: http://arxiv.org/abs/2501.06259v3
• Date: Thu, 06 Mar 2025 23:10:14 GMT
• Title: Quantum Down Sampling Filter for Variational Auto-encoder
• Authors: Farina Riaz, Fakhar Zaman, Hajime Suzuki, Sharif Abuadbba, David Nguyen,
• Abstract summary: Variational autoencoders (VAEs) are fundamental for generative modeling and image reconstruction.<n>This study introduces a hybrid model, quantum variational autoencoder (Q-VAE)<n>Q-VAE integrates quantum encoding within the encoder while utilizing fully connected layers to extract meaningful representations.
• Score: 0.504868948270058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Fr\'echet 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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