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Quantum Machine Intelligence

Q-CMAPO: A quantum-classical framework for balancing exploration and exploitation in multi-agent reinforcement learning

Mohammad Taghavi, Javad Vahidi

Published Feb 3, 2026• 3 citations
Publisher

Abstract

Abstract In this paper, we propose a novel approach, Q-CMAPO (Quantum-Classical Multi-agent Policy Optimization), for tackling complex decision-making problems in multi-agent systems. By leveraging quantum-inspired optimization techniques, Q-CMAPO efficiently addresses the exploration-exploitation tradeoff, enhancing the scalability and performance of reinforcement learning (RL) algorithms in partially observable, non-stationary environments. We introduce an innovative framework that combines centralized training with decentralized execution (CTDE), enabling seamless cooperation between agents while preserving their autonomy during execution. Through extensive empirical evaluation, including various UAV deployment scenarios, we demonstrate that Q-CMAPO consistently outperforms existing baselines in both computational efficiency and classification accuracy. Our experiments show significant improvements in performance metrics, as well as substantial gains in runtime efficiency and memory utilization. Furthermore, we provide a comprehensive theoretical analysis, proving the convergence and stability of the proposed method in non-stationary environments. We also conduct an ablation study, shedding light on the importance of different components of Q-CMAPO in optimizing agent cooperation. While promising, the proposed approach faces several challenges, including its sensitivity to hyperparameters and scalability in large-scale systems, suggesting opportunities for future refinement and expansion. The integration of Q-CMAPO into real-world applications, such as autonomous robotics, and UAV-based surveillance, opens new avenues for research, bridging the gap between quantum-inspired optimization and practical deployment in multi-agent systems.

DOI
10.1007/s42484-026-00361-0

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Abstract In this paper, we propose a novel approach, Q-CMAPO (Quantum-Classical Multi-agent Policy Optimization), for tackling complex decision-making problems in multi-agent systems. By leveraging quantum-inspired optimization techniques, Q-CMAPO efficiently addresses the exploration-exploitation tradeoff, enhancing the scalability and performance of reinforcement learning (RL) algorithms in partially observable, non-stationary environments. We introduce an innovative framework that combines centralized training with decentralized execution (CTDE), enabling seamless cooperation between agents while preserving their autonomy during execution. Through extensive empirical evaluation, including various UAV deployment scenarios, we demonstrate that Q-CMAPO consistently outperforms existing baselines in both computational efficiency and classification accuracy. Our experiments show significant improvements in performance metrics, as well as substantial gains in runtime efficiency and memory utilization. Furthermore, we provide a comprehensive theoretical analysis, proving the convergence and stability of the proposed method in non-stationary environments. We also conduct an ablation study, shedding light on the importance of different components of Q-CMAPO in optimizing agent cooperation. While promising, the proposed approach faces several challenges, including its sensitivity to hyperparameters and scalability in large-scale systems, suggesting opportunities for future refinement and expansion. The integration of Q-CMAPO into real-world applications, such as autonomous robotics, and UAV-based surveillance, opens new avenues for research, bridging the gap between quantum-inspired optimization and practical deployment in multi-agent systems.
In the last decade, the field of reinforcement learning has evolved from single-agent paradigms to embrace multi-agent settings. However, as the number of agents increases, especially in intricate or stochastic environments, the efficacy of individual learning models tends to diminish. Moreover, applying experience replay techniques in multi-agent scenarios presents considerable challenges. To address these pressing issues, this paper introduces a straightforward yet highly effective approach known as Quantum-Based Multi-Agent Reinforcement Learning (QMARL). This approach revolves around the quantization of states and actions within the multi-agent reinforcement learning system. Leveraging the power of the Grover algorithm for action decision-making, we also introduce a novel quantum-based prioritized experience replay method. Our proposed approach has been rigorously validated through experiments conducted in the cooperative navigation environment provided by OpenAI. The results demonstrate its capacity to enhance multi-agent learning in complex settings. This research opens promising avenues for harnessing quantum computing techniques in the realm of reinforcement learning, paving the way for more robust and scalable solutions in multi-agent systems.
This paper investigates the potential of quantum-inspired algorithms to improve reinforcement learning (RL) policy optimization. Traditional RL methods often struggle with slow convergence and exploration challenges, particularly in complex environments. We propose a novel approach that draws inspiration from quantum mechanics, specifically superposition and entanglement, to address these limitations. Our algorithms aim to exploit superposition to simultaneously evaluate multiple action possibilities and utilize entanglement to model correlations between states, thereby accelerating the learning process. We present a framework for incorporating these concepts into RL, focusing on algorithmic design rather than a direct quantum simulation. The theoretical analysis suggests that our approach could significantly enhance exploration efficiency and, consequently, policy convergence rates. This work represents a direct application of quantum-inspired ideas to RL policy optimization, offering a potentially transformative pathway for developing more efficient and robust RL agents.
Reinforcement learning (RL) policy gradient methods, while effective, frequently encounter challenges related to instability and the sensitivity of their performance to the learning rate hyperparameter. This work introduces a novel approach leveraging quantum-inspired optimization techniques to address these issues. Specifically, we propose a quantum-inspired learning rate scheduler designed to dynamically adapt the update step size during policy gradient training. This scheduler, rooted in principles of quantum measurement and superposition, aims to provide a more robust and efficient learning process. The core claim of this paper is that the integration of this quantum-inspired learning rate scheduler significantly improves the stability and convergence speed of policy gradient methods. We present a theoretical framework and explore the potential of this approach, demonstrating its promise for enhancing RL performance. The proposed method offers a new direction in RL optimization, potentially bridging the gap between classical and quantum-inspired approaches.
The current natural evolution of the world of real-time decision-making algorithms is unable to keep up with the fast changing complexity and dynamics of today’s world. Particularly in automotive, smart grids, robotics, or other applying fields, such ultra efficient decision making is demanded in a situation of rapidly changing conditions with high requirements on computation resources. As the first contribution, Quantum Inspired Reinforcement Learning (QIRLF) proposes a new method of addressing these challenges via marrying the fundamental elements of quantum theory to reinforcement learning. At the same time, QIRLF exploits quantum inspired state representation to explore many potential states concomitantly, thereby improving the efficiency in its decisions making process. Besides quantum tunneling and probabilistic action choice mechanisms faster convergence to optimal solutions are possible in values of state spaces, and multi-agent entanglement improves cooperation between the agents. The framework is adaptable in being able to make real time decisions in dynamic environments and is efficient by having quantum feedback loops that continuously refine decision policies. QIRLF reduces significantly the decision-making time and computational load compared to traditional, by using quantum inspired techniques. By applying this approach we not only enhance decision speed and reliability but also help in developing effective, energy efficient solutions for the complex real time systems, making it possible for more advanced, environment friendly technologies in systems needing fast, accurate and quick decisions.
This paper investigates the application of quantum-inspired algorithms to the field of reinforcement learning (RL). Traditional RL methods often struggle with exploration challenges and can be computationally expensive, particularly in complex environments. We propose a novel RL algorithm that leverages concepts from quantum mechanics, specifically superposition and entanglement, to address these limitations. The core idea is to represent states, actions, and rewards using quantum-inspired representations, allowing the agent to explore the environment more efficiently and potentially converge to optimal policies faster. This work presents a detailed formulation of the algorithm, including the mathematical framework and key considerations for implementation. The potential benefits of this approach, including improved exploration and reduced computational complexity, are discussed. The algorithm is designed to iteratively refine its policy based on simulated quantum measurements, aiming to minimize the expected return. The exploration strategy is guided by the inherent probabilistic nature of quantum states, leading to a more diverse and potentially faster learning process. Further research is warranted to fully explore the capabilities and limitations of this approach across various RL benchmark problems.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35%\mathbf{2 0}-\mathbf{3 5 \%}20−35% faster convergence and 15%\mathbf{1 5 \%}15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.