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Found 7 papers, 1 papers with code
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.
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.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation
The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies.
Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation
In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links.
Quantum Neural Networks: Concepts, Applications, and Challenges
Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks.
