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.
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.
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.
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.
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.
An intelligent adaptation technique is necessary due to the complexity and unpredictability of the air space, and autonomous unmanned aerial vehicle (UAV) navigation uses quantum reinforcement learning (QRL) as an advanced control mechanism. This work suggests quantum-inspired encoding and optimization algorithms for policy learning in order to overcome the shortcomings in efficiency and scalability of conventional and deep reinforcement learning approaches. The technique employs quantum-parametric policy optimization on a hybrid quantum-classical architecture after forming UAV navigation as a Markov Decision Process and adding quantum amplitude encoding. Dynamic obstacle and random wind disturbance experiments will be carried out in a high-fidelity 3D simulation environment. QRL has decreased energy usage by up to 17% and increased the top classical RL baseline's convergence speed by 40%. Even in inclement weather, the success rate remains around 95%, and the minimum obstacle clearance never falls below 1.8 meters. Analysis shows that QRL yields safer, more robust and sample-efficient policies that are better at generalizing to unseen maps and disturbance-heavy environments. Based on the above results, the technical feasibility and engineering value of quantum-enhanced learning algorithms for robust UAV autonomous navigation have been demonstrated; thus, they can serve as a model for future intelligent aerial decision systems.
Quantum Machine Learning (QML) represents a paradigm shift in machine learning, leveraging quantum mechanical principles to accelerate computation and potentially unlock new algorithms. This paper investigates a novel reinforcement learning (RL) framework, termed "Adaptive Neural Network Reinforcement Learning" (ANNR), which employs dynamic parameter adjustment within a self-adaptive neural network to enhance learning efficiency and generalization capabilities. We demonstrate that this approach overcomes limitations of traditional RL methods, offering a promising path towards more robust and adaptable reinforcement learning agents. The core mechanism focuses on iteratively refining neural network weights through a feedback loop, enabling the system to explore complex environments effectively. The research explores the impact of parameter adjustment on both training speed and final performance across various RL tasks.