mlsanityData to 7 Oct 2026

Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design

arXiv 2203.10443 ↗First posted 20 Mar 2022quant-ph cs.ET cs.LGcited by 25

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Abstract (arXiv)

In recent years, quantum computing (QC) has been getting a lot of attention from industry and academia. Especially, among various QC research topics, variational quantum circuit (VQC) enables quantum deep reinforcement learning (QRL). Many studies of QRL have shown that the QRL is superior to the classical reinforcement learning (RL) methods under the constraints of the number of training parameters. This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL). However, the extension of QRL to QMARL is not straightforward due to the challenge of the noise intermediate-scale quantum (NISQ) and the non-stationary properties in classical multi-agent RL (MARL). Therefore, this paper proposes the centralized training and decentralized execution (CTDE) QMARL framework by designing novel VQCs for the framework to cope with these issues. To corroborate the QMARL framework, this paper conducts the QMARL demonstration in a single-hop environment where edge agents offload packets to clouds. The extensive demonstration shows that the proposed QMARL framework enhances 57.7% of total reward than classical frameworks.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

6 of 12 references.

References · 6

  1. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  2. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  3. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  4. 2014Quantum Deep Learningcited by 31
  5. 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23
  6. 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 10; 2023: 4; 2024: 3; 2025: 6; 2026: 2.

25 papers.

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  1. 2022A Survey on Quantum Reinforcement Learningcited by 50
  2. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  3. 2022Quantum deep recurrent reinforcement learningcited by 23
  4. 2023Asynchronous training of quantum reinforcement learningcited by 20
  5. 2025Learning to Measure Quantum Neural Networkscited by 15
  6. 2022Reservoir Computing via Quantum Recurrent Neural Networkscited by 14
  7. 2023Efficient quantum recurrent reinforcement learning via quantum reservoir computingcited by 14
  8. 2024eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channelscited by 7
  9. 2022Scalable Quantum Convolutional Neural Networkscited by 5
  10. 2023Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systemscited by 5
  11. 20223D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applicationscited by 4
  12. 2022Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classificationcited by 4
13 more
  1. 2022Quantum Split Neural Network Learning using Cross-Channel Poolingcited by 4
  2. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  3. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4
  4. 2025Transfer Learning Analysis of Variational Quantum Circuitscited by 3
  5. 2023Quantum deep Q learning with distributed prioritized experience replaycited by 2
  6. 2025GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuitscited by 1
  7. 2025Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Deliverycited by 1
  8. 2025Neural Architecture Search for Quantum Autoencoderscited by 1
  9. 2026Learning to Coordinate via Quantum Entanglement in Multi-Agent Reinforcement Learningcited by 1
  10. 2022Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learningcited by 0
  11. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimizationcited by 0
  12. 2025HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuitscited by 0
  13. 2026Quantum Advantage in Multi Agent Reinforcement Learningcited by 0