Quantum-Enhanced Bayesian Neural Networks for Symbolic Reasoning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the integration of quantum mechanics principles into Bayesian Neural Networks (BNNs) to enhance symbolic reasoning capabilities, particularly in the realm of uncertainty. We propose a novel architecture that leverages quantum-state manipulation as a means to represent logical rules and inference processes with improved expressiveness and robustness. The core mechanism focuses on utilizing quantum phenomena – superposition, entanglement, and interference – to represent and process information in a fundamentally different way than classical BNNs. We present a preliminary model demonstrating improved performance on complex symbolic reasoning tasks, showcasing the potential for a new paradigm in AI reasoning. This work addresses the limitations of classical BNNs in handling uncertainty and offers a pathway toward more sophisticated and reliable symbolic systems.

DOI: 10.5281/zenodo.22267033

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