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Quantum-inspired tensor networks offer parameter-efficient image inpainting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed quantum-inspired tensor-network circuits for image inpainting tasks. A key innovation is the diagonal quantum Fourier transform (QFT) relaxation, which is invertible and maintains coherence during training, thus avoiding the need for explicit coherence penalties. This approach allows for efficient learning from randomly sampled data and generalizes well to new images, outperforming fixed transforms and larger unitary architectures with significantly fewer parameters. AI

IMPACT This research could lead to more efficient and effective image inpainting models by leveraging quantum principles.

RANK_REASON The cluster contains a research paper detailing a novel method for image inpainting using quantum-inspired tensor networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum-inspired tensor networks offer parameter-efficient image inpainting

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The cluster contains a research paper detailing a novel method for image inpainting using quantum-inspired tensor networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shiwen An, Konstantinos Slavakis ·

    Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting

    arXiv:2609.17298v1 Announce Type: cross Abstract: 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…

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