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Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning

arXiv 2505.15836 ↗First posted 16 May 2025cs.NE cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 16 May 2025.

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12 of 23 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  3. 2017Population Based Training of Neural Networkscited by 367
  4. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  5. 2017Evolving Deep Neural Networkscited by 95
  6. 2018Continuous-variable quantum neural networkscited by 74
  7. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  8. 2022Communication-Efficient Federated Learning With Data and Client Heterogeneitycited by 9
  9. 2022Optimising Communication Overhead in Federated Learning Using NSGA-IIcited by 7
  10. 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learningcited by 6
  11. 2022Evolution strategies: Application in hybrid quantum-classical neural networkscited by 5
  12. 2024Coherent Feed Forward Quantum Neural Networkcited by 3

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  3. 2024Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization
  4. 2025Quantum Decision Transformers (QDT): Synergistic Entanglement and Interference for Offline Reinforcement Learning
  5. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  6. 2024Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning
  7. 2024Federated learning with distributed fixed design quantum chips and quantum channels
  8. 2025Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning