Quantum-Inspired Hardware Acceleration for Graph Convolution

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Graph Convolution (GC) is a fundamental operation in numerous machine learning tasks, including node classification, link prediction, and graph classification. However, the computational complexity of traditional GC algorithms, particularly when applied to large-scale graphs, presents a significant bottleneck. This paper proposes a novel approach to accelerate GC operations by designing specialized hardware accelerators inspired by quantum computing principles. The core idea is to exploit superposition and entanglement-like behavior to enable fundamentally parallel computations. We present a detailed framework for such accelerators, focusing on the mathematical underpinnings of the GC operation and translating them into hardware-executable instructions. The design emphasizes efficient data representation, parallel processing, and minimizing redundant computations. The proposed architecture aims to drastically reduce the time complexity of GC, paving the way for more efficient and scalable graph-based machine learning applications. Specifically, we explore approaches for representing graph adjacency matrices and node features in a manner conducive to parallel processing, and introduce a novel dataflow model optimized for the inherent parallelism of GC. The theoretical analysis demonstrates a potential reduction in computational complexity, and we outline a design strategy for implementing this acceleration on custom hardware.

DOI: 10.5281/zenodo.22314298

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