<ul><li>A novel framework called Quantum-Evolutionary Neural Network (QE-NN) is introduced to optimize real-time decision-making in multi-agent systems.</li><li>QE-NN combines quantum-inspired neural networks with evolutionary algorithms, leveraging quantum computing principles for enhanced learning speed and decision accuracy.</li><li>The framework ensures privacy preservation through federated learning, enabling decentralized agents to collaborate without sharing sensitive data.</li><li>This research merges quantum computing, evolutionary optimization, and privacy-preserving techniques to address complex problems in multi-agent decision-making systems for applications in autonomous systems, smart cities, and healthcare.</li></ul>

Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning

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