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Quantum-enhanced hyperdimensional computing for graph representation
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DOI：10.1016/j.knosys.2026.116550.png)
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Abstract
En 中文
Representing complex structured data often involves a tradeoff between computational cost and representational quality, largely determined by the underlying encoding framework. Hyperdimensional Computing (HDC), also known as Vector Symbolic Architecture (VSA), has been recognized for its ability to represent complex structures through high-dimensional encoding. Conventional representation techniques in HDC, which in many cases rely on the cognitive operation of bundling for data memorization, suffer from quality and capacity limitations. Association through the binding operation offers a potential alternative, but its use is complicated by the difficult decomposition process, where classical factorization methods, such as resonator networks, can become challenging as the number of candidate factors and bound components increases. In this work, we explore a novel approach based on a quantum factorization algorithm inspired by Grover's quantum search algorithm within HDC. This approach not only addresses the complexity challenges inherent in the decomposition of bound HDC representations, but also significantly mitigates noise and improves representational fidelity. Focusing on graph data structures, our framework Quark is analyzed through theoretical derivations and classical simulations of the quantum decoding process, demonstrating improved graph reconstruction quality across different graph sizes and dimensionalities. This integration of quantum computing and HDC provides a promising direction for scalable and high-fidelity graph representation.
Keywords:
Graph representation
Hyperdimensional Computing
Quantum computing
Vector Symbolic Architecture
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