arXiv (OAI Expanded)
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Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting
This work introduces quantum-inspired tensor-network circuits as trainable transforms for image inpainting. Among the proposed architectures, the diagonal quantum Fourier transform (QFT) relaxation is invertible with $O(N^2 \log N)$ computational cost for $N\times N$ images, inherently preserving mi
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