kolmogorov-arnold-network

We propose hierarchical RBF-KAN and RBF-SKAN architectures for multidimensional function approximation and random field learning. The frameworks offer universal approximation properties and partially alleviate the curse of dimensionality, with empirical results showing improved accuracy over existing methods.

This paper introduces Gated QKAN-FWP, a scalable quantum-inspired sequence learning framework that combines Fast Weight Programmers with Kolmogorov-Arnold Networks using single-qubit data re-uploading circuits.