基于量子神经网络的复杂系统建模

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of Quantum Neural Networks (QNNs) for modeling and simulating complex systems. Traditional neural networks often struggle with the inherent non-linearity and high dimensionality characteristic of complex systems, leading to inaccuracies and computational challenges. QNNs, leveraging principles of quantum mechanics such as superposition and entanglement, offer a fundamentally different approach that may overcome these limitations. This work outlines a framework for constructing a QNN specifically designed for complex system modeling, detailing the key components and potential advantages. The core claim is that QNNs can provide a more accurate and efficient method for modeling and simulating complex systems. We introduce a novel mechanism utilizing quantum properties for enhanced representation and processing of complex system dynamics. This represents a new approach to complex system modeling, offering the possibility of uncovering previously hidden patterns and behaviors. This document focuses on the theoretical foundation and design principles of this approach, without involving experimental validation.

DOI: 10.5281/zenodo.22315458

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