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Quantum-inspired multi-agent reinforcement learning for exploration–exploitation optimization in UAV-assisted 6G network deployment

Mohammad Taghavi, Javad Vahidi

arXiv:2512.20624Quantum Machine IntelligenceNov 24, 20250 citations

Quantum-Enhanced Edge Computing for Optimized Resource Allocation in Heterogeneous IoT Networks: A Deep Reinforcement Learning Approach

Arun Kumar Arigela, A. Brahmareddy, Mercy Paul Selvan +3 more

Lecture notes in networks and systemsOct 1, 20252 citations

Quantum computing and neuromorphic computing for safe, reliable, and explainable multi-agent reinforcement learning: optimal control in autonomous robotics

Mohammad Taghavi, Rahman Farnoosh

arXiv:2408.03884Iran Journal of Computer ScienceJul 16, 202512 citations

Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization

Michael Kölle, Karola Schneider, Sabrina Egger +5 more

arXiv:2407.20739Lecture notes in computer scienceJan 1, 20251 citation

Dual-Agent-based Resource Allocation for Synergistic Transmission of Classical and Quantum Signals in Multi-Core Fiber Networks

Fengkai Sun, Yongmei Sun, Yaoxian Gao +1 more

To address the challenge of synergistic transmission for classical and quantum signals in multi-core fiber networks, this paper proposes a synergistic dual-agent reinforcement learning resource allocation scheme (S-DARL) based on multi-agent reinforcement learning framework. Two cooperating agents adopted in this scheme allocate the spectrum and core resources for quantum and classical signals, which are enabled to propagate together under the constraints of physical layer noise to achieve efficient encrypted data transmission. Simulation results demonstrate that the proposed scheme significantly reduces the blocking probability and improves the overall network synergistic degree compared to previous state- of-the-art heuristic methods.To address the challenge of synergistic transmission for classical and quantum signals in multi-core fiber networks, this paper proposes a synergistic dual-agent reinforcement learning resource allocation scheme (S-DARL) based on multi-agent reinforcement learning framework. Two cooperating agents adopted in this scheme allocate the spectrum and core resources for quantum and classical signals, which are enabled to propagate together under the constraints of physical layer noise to achieve efficient encrypted data transmission. Simulation results demonstrate that the proposed scheme significantly reduces the blocking probability and improves the overall network synergistic degree compared to previous state- of-the-art heuristic methods.

Asia Communications and Photonics ConferenceNov 5, 20250 citations

Quantum Reinforcement Learning for Large-Scale Multi-Agent Decision-Making in Autonomous Aerial Networks

Soohyun Park, Joongheon Kim

This paper addresses a new quantum computing-based multi-agent reinforcement learning (QMARL) algorithm which is inspired by the quantum neural network (QNN)-based centralized critic and multiple actor networks. The benefit of the proposed QMARL-based algorithm is in the action control dimension reduction where it can reduce the size into a logarithmic-scale when project value measure (PVM) is utilized. Therefore, our proposed QMARL-based algorithm is beneficial for massive-agent MARL training convergence. Moreover, the various applications of QMARL-based algorithms are presented in massive-scale unmanned aerial vehicle (UAV) networks. Lastly, our performance evaluation results verify that the proposed QMARL-based algorithm can successfully converge when massive-action dimensions should be utilized.This paper addresses a new quantum computing-based multi-agent reinforcement learning (QMARL) algorithm which is inspired by the quantum neural network (QNN)-based centralized critic and multiple actor networks. The benefit of the proposed QMARL-based algorithm is in the action control dimension reduction where it can reduce the size into a logarithmic-scale when project value measure (PVM) is utilized. Therefore, our proposed QMARL-based algorithm is beneficial for massive-agent MARL training convergence. Moreover, the various applications of QMARL-based algorithms are presented in massive-scale unmanned aerial vehicle (UAV) networks. Lastly, our performance evaluation results verify that the proposed QMARL-based algorithm can successfully converge when massive-action dimensions should be utilized.

IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)Aug 1, 20235 citations

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