Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its ‘i) fast convergence’ and ‘ii) high scalability’, unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.
Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its ‘i) fast convergence’ and ‘ii) high scalability’, unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
Attitude control is vital for ensuring flight stability in aircraft operating under dynamic disturbances such as turbulence and vortices. While reinforcement learning (RL)-based controllers offer adaptivity to uncertain dynamics, their deployment on airborne platforms is often constrained by the large number of training parameters and associated computational overhead. To address this limitation, this article implements a quantum reinforcement learning (QRL)-based attitude controller in which a quantum neural network (QNN) replaces the classical neural network (NN) within an actor-critic framework. Exploiting quantum principles such as superposition and entanglement, the QRL controller reduces training parameters, thereby enhancing computational efficiency and enabling resilient, lightweight flight control for next-generation aircraft systems. The proposed controller is evaluated in a simulated flight-control environment that reflects the aerodynamic specifications of a Boeing B777-300X airframe and is subjected to stochastic vortical disturbances. The simulation results demonstrate that the QNN-based controller can reduce the number of training parameters by approximately 760x compared with a controller based on the classical NN. These results indicate that, within the realistic experimental setting, QRL can provide a parameter-efficient and computationally lightweight alternative for aircraft attitude control under uncertain aerodynamic conditions.
Recent research in quantum computing has gained significant momentum, promising transformative advances across various fields, including artificial intelligence (AI). This paper focuses on quantum reinforcement learning (QRL), which utilizes quantum neural network (QNN) to address the inherent chal-lenges in conventional reinforcement learning (RL) frameworks. For a comprehensive examination of QRL, in this paper, the structure of QNN is explored in-depth, including the functional roles of key quantum circuit gates such as the unitary, rotation, controlled-X, and Hadamard gates. This paper also discusses the primary components of QNN, namely state encoding, parameterized quantum circuits (PQC), and measurement processes. Furthermore, the application of IBM qiskit for the visualization of quantum states and circuits is highlighted, providing practical insights into the deployment of quantum principles in RL. Consequently, this paper encapsulates the potential of QRL to revolutionize AI, positioning it as a pivotal area of future research and development in quantum computing.
Abstract This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems. With the advances in quantum computing and deep learning technologies, various research works have focused on quantum deep learning and quantum machine learning. In this paper, quantum neural network (QNN)‐based reinforcement learning (RL) models are discussed and introduced. Moreover, the pros of the QNN‐based RL algorithms and models, such as fast training, high scalability, and efficient learning parameter utilization, are presented along with various research results. In addition, one of the well‐known multi‐agent extensions of QNN‐based RL models, the quantum centralized‐critic and multiple‐actor network, is also discussed and its applications to multi‐agent cooperation and coordination are introduced. Finally, the applications and future research directions are introduced and discussed in terms of federated learning, split learning, autonomous control, and quantum deep learning software testing.
Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL’s potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.
Quantum reinforcement learning (QRL) is a burgeoning area that lies at the convergence of quantum computing and machine learning. Based on the characteristics of quantum superposition and quantum entanglement, quantum reinforcement learning can achieve some quantum advantages on existing noisy intermediate-scale quantum devices compared to traditional reinforcement learning. Within the field of information systems, reinforcement learning techniques have been extensively explored for applications including intelligent decision support mechanisms, resource distribution strategies, and system enhancement under conditions of uncertainty. This paper analyzes the fundamental principles of quantum reinforcement learning and introduces research work and progress in related areas. This study examines the foundational concepts underlying quantum reinforcement learning and provides an overview of notable algorithmic strategies documented in contemporary research.