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Neuro-Symbolic Reasoning with Quantum-Enhanced Associative Memory

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel approach to neuro-symbolic reasoning by integrating quantum associative memory (QAM). Traditional neuro-symbolic systems struggle with the scalability and efficiency of knowledge representation and retrieval, particularly when dealing with complex, partially known information. Our approach utilizes quantum states to represent symbolic concepts, allowing for exponentially larger and more nuanced knowledge representations compared to classical associative memory. Quantum interference is then leveraged to rapidly and efficiently retrieve relevant knowledge based on partial or noisy inputs. The core mechanism relies on the superposition and entanglement properties of quantum states, enabling the system to explore multiple potential interpretations simultaneously. We demonstrate how this QAM-enhanced neuro-symbolic framework can significantly improve reasoning performance, particularly in scenarios requiring robust inference with incomplete or uncertain data. We introduce a theoretical framework outlining the key components and operational principles, and explore the potential advantages and challenges of this hybrid approach. The core claim is that combining neural networks with symbolic reasoning systems can be significantly enhanced by leveraging quantum associative memory to represent and retrieve complex knowledge structures. The core mechanism utilizes quantum states to represent symbolic concepts and utilizing quantum interference to quickly and efficiently retrieve relevant knowledge based on partial or noisy inputs. This research represents a significant step towards unlocking new levels of reasoning capabilities within artificial intelligence.

DOI
10.5281/zenodo.22317060

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