In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment.
In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment.
In this era of automation, drones have considered to be a key element with its wide applicability in various situations. Additionally, the efficiency of drones can be further improved via collaborated learning techniques such as federated learning (FL). However, the drone-based FL requires a significant amount of communications because the drones share their FL data over wireless channels. Therefore, the unstable channel condition of wireless transmission introduces the low success probability in large-scale parameter transmission. According to quantum computing (QC) advantages, this problem can be tackled due to less parameter utilization in quantum neural network (QNN) training. Therefore, a novel QC-based FL known as theadaptive quantum federated learning (AQFL)is proposed for drone license plate recognition in autonomous surveillance applications. In our proposed AQFL, adaptive QNNs (AQNNs) are used as local models which can control the depths of QNNs by adding measurement computations in the middles of QNN architectures. Thus, our proposed AQFL is robust even in training data non-iidness and large local model variances while maintaining sufficient learning performance. Finally, the evaluation results verify that our proposed AQFL achieves the desired performance improvements.
With the rise of autonomous vehicles (AVs), the number of self-driving vehicles has increased in recent years and vehicular computing (VC) was actively used to ensure road safety by controlling AVs. However, the recent escalation in demand for a massive scale vehicle network has prevented VC from achieving its purpose. To overcome this problem, a framework which combined VC and federated learning (FL) was proposed. Although the vehicular FL was initially effective, it also faced challenges as the excessive increase in the amount and size of data generated by AVs and the serious threat of data leakage have severely degraded the performance of vehicular FL. Thus, quantum computing was exploited to further improve on the pre-existing FL and proposed dynamic quantum federated learning (DQFL). This work proposes the application of DQFL to public safety scenarios to investigate the effectiveness of the model in controlling the AVs under real-life constraints.
This paper studies a novel energy-efficient quantum federated learning (QFL) framework designed for low-altitude quantum-enabled unmanned aerial vehicle (UAV) networks. We develop a novel energy minimization problem by jointly considering UAV’s quantum computing power, computational frequency, transmit power, number of quantum noise measurements, and trajectory. The energy minimization problem we formulate is inherently non-convex and cannot be efficiently solved using conventional convex optimization methods in its original form. To overcome this challenge, we develop an iterative optimization approach that integrates block coordinate descent (BCD) with successive convex approximation (SCA) techniques to effectively find optimal solutions for QFL’s energy optimization. Moreover, we conduct a holistic convergence analysis of federated quantum model training, which reveals the important trade-off between the convergence rate and number of quantum noise measurements. Evaluations under different data settings demonstrate that our theoretical results are consistent with QFL model training simulations, thereby facilitating future QFL algorithm design. Extensive simulation results also show that our optimization methodology significantly improves energy efficiency over baseline methods.
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.
The utilization of unmanned aerial vehicles (UAVs) in defense for functions, such as delivery and surveillance, has surged due to the fast progress of technology. UAV tracking and navigation system optimization is critical as UAV operations become more complicated. Quantum computing (QC) can significantly improve optimization procedures for real-time tracking, overcoming computational challenges and enhancing efficiency in UAV operations by handling large amounts of data. The study’s objective is to establish how QC performs in the optimization of UAV tracking. A novel Dynamic Kookaburra-tuned Malleable Convolutional Neural Network (DK-MCNN) is proposed to improve the tracking performance of UAVs. The data collected from video feeds from cameras mounted on UAVs, readings from GPS and other sensors outlining the specific paths and maneuvers the UAV will perform, ensuring diverse scenarios, including various terrains and environments. The data preprocessed using data cleaning and normalization involve removing erroneous or redundant data to ensure high-quality datasets. The study specifically focuses on leveraging QC into UAV tracking systems and promises to enhance performance, including speed, optimization, data handling and robustness. This technology has the potential to transform UAV operations, making them more efficient, reliable and capable of handling complex tasks in dynamic environments. The study evaluates the tracking accuracy, computational efficiency and resilience of the proposed method compared to traditional approaches across a range of operating situations. Extensive simulations of several tracking scenarios, encompassing urban, rural and disaster response situations, are employed to assess the efficacy of UAVs in monitoring and navigating across dynamic terrain. The outcomes illustrate the potential for real-time applications in UAV systems, by showing that the suggested technique dramatically decreases processing time and improves tracking accuracy, when compared to conventional algorithms.
This article proposes a novel algorithm, named quantum multiagent actor–critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple UAVs, the application of multiagent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of QC, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this article validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.