Image Compression Using Quantum Wavelet Transform and Quantum Convolutional Networks

arXiv:2609.28387 · quant-ph · Submitted 2026-09-23 · Read on arXiv

quant-ph

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Wavelet Transform — 21× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Quantum Convolutional Neural Network — 8× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Quantum Wavelet Transform — 7× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• Entanglement — 6× in this paper · explained in 3 episodes
• Quantum Circuits — 6× in this paper · explained in 2 episodes
• Quantum Daubechies Wavelet Transform — 5× in this paper
• Redundancy — 5× in this paper · explained in 2 episodes
• Super-Resolution Generative Adversarial Network — 5× in this paper
• Discrete Wavelet Transform — 4× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Normal Arbitrary Superposition State — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Flexible Representation of Quantum Images — 2× in this paper
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Data Compression Ratio — 1× in this paper
• Edge Devices — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Inverse Quantum Daubechies Wavelet Transform — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Peak Signal-to-Noise Ratio — 1× in this paper
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes

Sources

• A Survey: Various Techniques of Image Compression
• End-to-end Optimized Image Compression
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Adam: A Method for Stochastic Optimization
• PennyLane: Automatic differentiation of hybrid quantum-classical computations

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