Finding Key Nodes in Complex Networks Through Quantum Deep Reinforcement Learning

Finding Key Nodes in Complex Networks Through Quantum Deep Reinforcement Learning

Identifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively identifying distributed influential nodes whil...

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Bibliographic Details
Main Authors: Juechan Xiong, Xiao-Long Ren, Linyuan Lü
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Entropy
Subjects:
vital node identification
quantum algorithm
reinforcement learning
complex networks
Online Access:https://www.mdpi.com/1099-4300/27/4/382
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