Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

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ISSN Print: 2150-4091
ISSN Online: 2150-4105
www.scirp.org/journal/ns
E-mail: ns@scirp.org

"Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem"
written by Wei Hu, James Hu,
published by Natural Science, Vol.11 No.1, 2019

has been cited by the following article(s):

[1]	Abrupt moving target tracking based on quantum enhanced particle filter
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[2]	Quantum contextual bandits and recommender systems for quantum data
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[3]	On UAVs for Wireless Networks: Resource Management, Performance Analysis and Trajectory Optimization
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[4]	A Survey on Quantum Reinforcement Learning
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[5]	Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM
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[6]	Deep Reinforcement Learning With Quantum-Inspired Experience Replay
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[7]	Reinforcement Learning with Deep Quantum Neural Networks
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[8]	Distributional Reinforcement Learning with Quantum Neural Networks
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[9]	Q Learning with Quantum Neural Networks
	2019

[1]	Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM Mechanical Systems and Signal Processing, 2022 DOI:10.1016/j.ymssp.2021.108153

[2]	Deep Reinforcement Learning With Quantum-Inspired Experience Replay IEEE Transactions on Cybernetics, 2022 DOI:10.1109/TCYB.2021.3053414

[3]	Deep Reinforcement Learning With Quantum-Inspired Experience Replay IEEE Transactions on Cybernetics, 2022 DOI:10.1109/TCYB.2021.3053414

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