Quantum-Inspired Hardware Architectures for Graph Processing

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of quantum-inspired hardware architectures for significantly enhancing graph processing capabilities. Traditional graph processing methods often struggle with the computational complexity arising from intricate relationships and patterns within graphs, particularly in domains like social network analysis, drug discovery, and financial modeling. We propose a novel approach that draws inspiration from quantum computation principles, specifically entanglement and superposition, to achieve unprecedented levels of parallelism. The core idea involves designing specialized hardware components that mimic these quantum phenomena to facilitate the concurrent processing of graph nodes and edges. This architecture aims to overcome the limitations of conventional graph processing systems by offering a fundamentally different paradigm. We detail the design principles, key components, and anticipated performance improvements, focusing on the theoretical framework and outlining potential avenues for future research and development. The primary focus is on developing architectures that can efficiently handle high-dimensional graph data and complex algorithms, ultimately leading to faster and more accurate graph processing results.

DOI: 10.5281/zenodo.22154684

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