tensor-networks

TT-Net introduces a quantum-inspired tensor network denoising block for conditional GANs that accesses cross-channel information, outperforming SVD-Net and other methods on PSNR and SSIM metrics across various noise types.

Introduces iterative tensor network transformations (ITNTs) for efficient element-wise evaluation of nonlinear functions on tensor trains, enabling data processing and optimization on large-scale datasets with applications in reactive flow fields and Max-SAT problems.

This arXiv paper introduces TEMPER, a tensorized parameterization for hyper-connection residual routing that reduces parameter growth while preserving expressive, manifold-constrained routing. Experiments show it matches or outperforms existing methods with substantially fewer additional parameters.

This paper introduces a tensor network model to capture the influence of emotional valence on the order-dependent structure of children's recognition memory, achieving 77.98% accuracy and demonstrating the value of quantum-inspired methods for modelling cognitive phenomena.

Introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for compressing deep neural network layers via small core tensors, achieving high compression ratios while maintaining accuracy.