AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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 minimum coherence thro...
A scheme to train a better transformation for a given image dataset is developed, using isometric tensor networks, inspired by quantum many-body theory, to parameterize the basis, and train it with Riemannian optimization.
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