量子进化神经网络提升多智能体联邦学习的实时决策与隐私保护能力
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
- 结合量子计算与进化算法优化多智能体决策
- 实现实时自适应，提升动态环境下的决策准确率
- 适合自动驾驶、智慧城市等隐私敏感场景
随着人工智能在复杂去中心化环境中的持续创新，对可扩展、自适应且隐私保护的决策系统的需求日益迫切。本文提出一种新型框架，将量子启发式神经网络与进化算法相结合，以优化多智能体系统（MAS）中的实时决策。所提出的量子-进化神经网络（QE-NN）利用量子计算原理——如量子叠加和纠缠——提升学习速度与决策精度，并通过进化优化在动态不确定环境中持续改进智能体行为。借助联邦学习，QE-NN实现隐私保护，使去中心化智能体无需共享敏感数据即可协作。该框架支持智能体实时适应环境，优化决策流程，适用于自动驾驶、智慧城市和医疗等领域。本研究实现了量子计算、进化优化与隐私保护技术的融合，推动了真实世界隐私敏感场景中复杂多智能体决策系统的边界。
原文摘要 · Abstract (English)
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces a novel framework combining quantum-inspired neural networks with evolutionary algorithms to optimize real-time decision-making in multi-agent systems (MAS). The proposed Quantum-Evolutionary Neural Network (QE-NN) leverages quantum computing principles -- such as quantum superposition and entanglement -- to enhance learning speed and decision accuracy, while integrating evolutionary optimization to continually refine agent behaviors in dynamic, uncertain environments. By utilizing federated learning, QE-NN ensures privacy preservation, enabling decentralized agents to collaborate without sharing sensitive data. The framework is designed to allow agents to adapt in real-time to their environments, optimizing decision-making processes for applications in areas such as autonomous systems, smart cities, and healthcare. This research represents a breakthrough in merging quantum computing, evolutionary optimization, and privacy-preserving techniques to solve complex problems in multi-agent decision-making systems, pushing the boundaries of AI in real-world, privacy-sensitive applications.
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