Quantum Neural Networks for Symbolic Reasoning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel approach to symbolic reasoning utilizing quantum neural networks (QNNs). Traditional symbolic reasoning systems face limitations due to the computational bottlenecks inherent in classical logic processing. This work introduces a framework where logical operations are translated into quantum operations, leveraging the principles of quantum mechanics to perform reasoning. The core mechanism involves utilizing a QNN to process symbolic data, exploiting quantum entanglement to facilitate knowledge representation and propagation. We demonstrate the potential for significantly enhanced efficiency in symbolic inference compared to conventional methods. This research contributes to the burgeoning field of quantum computing by exploring a practical application of QNNs in a domain with well-defined logical structures – symbolic reasoning. The proposed system offers a pathway towards scalable and potentially faster solutions for complex reasoning tasks.

DOI: 10.5281/zenodo.22152385

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