# Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation

arXiv: https://arxiv.org/abs/2308.01519 · HTML: https://mlsanity.com/paper/2308.01519/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management](https://mlsanity.com/paper/2301.04012/) (2023)
- [Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems](https://mlsanity.com/paper/2302.04445/) (2023)
- [Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification](https://mlsanity.com/paper/2210.16731/) (2022)

## Built on by (most cited first, 6 of 6)

- [eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels](https://mlsanity.com/paper/2405.17486/) (2024)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2406.16994/) (2024)
- [How Can Quantum Deep Learning Improve Large Language Models?](https://mlsanity.com/paper/2509.16244/) (2025)
- [Quantum Circuit Structure Optimization for Quantum Reinforcement Learning](https://mlsanity.com/paper/2507.00589/) (2025)
- [Distributed Quantum Gaussian Processes for Multi-Agent Systems](https://mlsanity.com/paper/2602.15006/) (2026)

## Abstract (arXiv)

For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward, by reducing the action dimension into a logarithmic-scale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.