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Found 7 papers, 1 papers with code
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era.
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs).
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL).
Multi-agent Reinforcement Learning
Quantum Machine Learning
+2
Slimmable Quantum Federated Learning
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL).
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency.
