Quantum-Inspired Neural Networks with Adaptive Graph Structures

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel approach to neural network design, termed Quantum-Inspired Neural Networks (QINNs) with Adaptive Graph Structures. The core idea is to leverage the fundamental principles of quantum mechanics – specifically superposition and entanglement – to create a network architecture that exhibits enhanced information processing capabilities. Traditional neural networks often struggle with complex, non-linear relationships and can suffer from vanishing gradients during training. QINNs address these limitations by employing dynamically evolving graph structures that mimic the interconnectedness and efficient information transfer observed in quantum systems. The network architecture is designed to represent data through a superposition of states, allowing for the exploration of multiple solution paths simultaneously. Furthermore, adaptive graph structures, inspired by entanglement, facilitate the correlation of information between nodes, significantly improving the network's ability to capture intricate patterns. This approach represents a significant departure from simply using quantum concepts as an inspirational element; it's a fundamental integration of quantum principles into the neural network design, leading to potentially superior performance in tasks demanding high computational efficiency and pattern recognition accuracy. The paper outlines the theoretical framework, the proposed architecture, and discusses the potential benefits and future research directions.

DOI: 10.5281/zenodo.22270296

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