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.
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.
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.
This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation
ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.
In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.
This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep-layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: 1) unmanned aerial vehicle (UAV) trajectory planning; 2) user grouping; and 3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its nonconvexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station (BS) while maintaining Quality of Service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV BS in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.