Computational Superconductivity for Ultra-Fast AI: A Graphene Inspired Dirac Approach for Sparse Vision

This research paper presents the Fractal Dirac-Highway (FDH), a novel non-Euclidean vision framework that breaks from traditional grid-based paradigms by treating images as potential fields extracted via a vibrating fractal lattice. Inspired by the tight-binding Hamiltonian of graphene and recent findings in the van der Waals magnet ErBr3, the FDH architecture utilizes a hierarchical honeycomb structure to maintain "massless" information transport through stable Dirac cones. By implementing a discrete-sign "Negative Symbol" encoding, the system mimics the symmetry restoration observed in ErBr3 dipolar lattices, effectively closing the magnon-like band gaps that typically restrict gradient flow in dense networks. Experimental results on Fashion-MNIST demonstrate that this phase-coherent topological approach achieves a milestone 76.0% accuracy while utilizing 85% fewer active connections than standard CNN kernels, offering a high-efficiency path for low-power autonomous vision at the edge.