We present an innovative perspective of usage of intelligent agents and multi-agent systems in quantum computing, and summarize preliminary results of our initial research in this direction.
This keynote speech covers quantum games, quantum models, intelligent systems and simulation for business and management. In particular, the following research work will be presented. \n-\tA survey of the current status of research on quantum games (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for competition (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for cooperation (Dingxuan Huang & Shuliang Li)\n-\tSocial media for brand virality and open innovation\n-\tIntelligent systems and multi-agent-based systems for strategic decision making (Shuliang Li & Jim Zheng Li)\n-\tModelling & computer simulation of enterprise carbon intangible asset evolution; and a resilient multi-factor elasticity framework for low carbon cities (Yin Zeng & Shuliang Li)
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.