Quantum-Inspired Hardware Acceleration for Graph Algorithms

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of quantum-inspired hardware acceleration for graph algorithms. Traditional hardware acceleration techniques often struggle to efficiently exploit the inherent parallelism within graph computations. We propose a novel approach leveraging concepts from quantum mechanics, specifically superposition and entanglement, to design specialized hardware architectures. This approach aims to achieve significant performance improvements by enabling parallel processing and optimized data movement within graph algorithms. The core claim is that specialized hardware, inspired by quantum principles, can dramatically speed up graph algorithms. This research investigates the feasibility and potential impact of such designs, paving the way for future advancements in graph processing. The presented methodology focuses on architectural design and performance estimation, without requiring experimental validation.

DOI: 10.5281/zenodo.22157000

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