@article {1759,
	title = {Adaptive and Efficient Qubit Allocation Using Reinforcement Learning in Quantum Networks},
	journal = {IEEE Network},
	volume = {36},
	year = {2022},
	month = {09/2022},
	pages = {48-54},
	abstract = {<p>Quantum entanglement brings high-speed and inherently privacy-preserving transmission for information communication in quantum networks. The qubit scarcity is an important issue that cannot be ignored in quantum networks due to the limited storage capacity of quantum device, the short lifespan of qubits, etc. In this article, we first formulate the qubit competition problem as the Cooperative-Qubit-Allocation-Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set. We then model the CQAP as a Markov Decision Process (MDP) and adopt Reinforcement Learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters. Further, we introduce Active Learning (AL) algorithm to improve the efficiency of RL algorithm by reducing its trialerror times. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithms, with 23.5 ms reduction on average waiting time and 19.2 improvement on average path maturity degree, respectively.</p>
},
	keywords = {Quantum entanglement, quantum networks, qubit allocation, reinforcement learning},
	author = {Yanan Gao and Song Yang and Fan Li and Xiaoming Fu}
}
@conference {1732,
	title = {A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV},
	booktitle = {The 29th IEEE/ACM International Symposium on Quality of Service (IWQoS 2021)},
	year = {2021},
	month = {06/2021},
	author = {Nan He and Song Yang and Fan Li and Stojan Trajanovski and Fernando A. Kuipers and Xiaoming Fu}
}
