A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses the Deutsch–Jozsa quantum circuit to generate quantum populations synergistically working as haploid recombination and mutation operators that take advantage of quantum entanglement, providing exploitative and explorative features to produce new individuals. In place of the conventional lookup table or mathematical equation, we introduced a fuzzy heuristic to adapt the rotation angle employed in quantum gates. The hybrid nature of this algorithm becomes evident through its utilization of both classical and quantum computing components. Experimental evaluations were conducted using two distinct test sets. The first set, termed the “best case”, represents conditions that are the most favorable for determining the UAV positions, while the second set, the “worst-case”, simulates highly challenging conditions for locating the UAV positions, thereby posing a significant test for the proposed algorithm. We carried out statistical comparative analyses, assessing the HQGAFARA against other hybrid quantum algorithms that employ different rotation angles and against the classical genetic algorithm. The experimental results demonstrated that the HQGAFARA performed comparably, if not better, to the classical genetic algorithm regarding precision. Furthermore, quantum algorithms showcased their computational prowess in experiments related to the convergence time.
A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses the Deutsch–Jozsa quantum circuit to generate quantum populations synergistically working as haploid recombination and mutation operators that take advantage of quantum entanglement, providing exploitative and explorative features to produce new individuals. In place of the conventional lookup table or mathematical equation, we introduced a fuzzy heuristic to adapt the rotation angle employed in quantum gates. The hybrid nature of this algorithm becomes evident through its utilization of both classical and quantum computing components. Experimental evaluations were conducted using two distinct test sets. The first set, termed the “best case”, represents conditions that are the most favorable for determining the UAV positions, while the second set, the “worst-case”, simulates highly challenging conditions for locating the UAV positions, thereby posing a significant test for the proposed algorithm. We carried out statistical comparative analyses, assessing the HQGAFARA against other hybrid quantum algorithms that employ different rotation angles and against the classical genetic algorithm. The experimental results demonstrated that the HQGAFARA performed comparably, if not better, to the classical genetic algorithm regarding precision. Furthermore, quantum algorithms showcased their computational prowess in experiments related to the convergence time.
Quantum Computing (QC) is the next step in processing power by using principles like superposition and entanglement to solve problems that are otherwise intractable for classical computers. Within this realm, Quantum Genetic Algorithms (QGAs) are one of the most powerful tools that combine the evolutionary strategies of genetic algorithms with the unparalleled efficiency of QC. These algorithms are specifically suited to the exploration of large, complex problem spaces and would, therefore, make for a fantastic fit for enhancing the capabilities of Unmanned Aerial Vehicles (UAVs). Quantum genetics can be coupled with UAV technology to open new avenues in the advancement of aerial systems. QGAs may be used to allow UAVs to perform better in critical areas such as real-time obstacle avoidance, dynamic mission planning, energy-efficient routing, and swarm intelligence. These aerial vehicles can work more effectively in complicated and dynamic environments because they can work on multiple possibilities at once. This chapter will dig into the groundbreaking integration of QGAs with UAV systems, providing a revolutionary approach to enhancing UAV efficiency and autonomy, with in-depth examination of the conceptual framework behind QGAs, their applications within UAV systems, and essential hardware and software requirements that integrate quantum processors into UAV architectures. This further opens up avenues for ascertaining potential applications of QGAs in areas such as military operations, environmental monitoring, and disaster management in order to showcase the wide impact these technologies might carry across different sectors. This chapter would serve as a roadmap for researchers and discuss the hurdles in adapting quantum algorithms for large fleets of UAVs, technological barriers existing today, and the progress required to fully unleash the power of QC in aerial systems.
With the developing autonomous driving technologies today, unmanned vehicles - unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV) and unmanned marine vehicles (UMV) - autonomously reach the desired target without human intervention. However, effective and efficient methods for the coordination of these vehicles in multiples need to be developed. Quantum computing has much more potential than classical computers due to its nature. In this study, a quantum-classical hybrid system is proposed to ensure that autonomous transportation unmanned vehicles can assign tasks in a collaborative and fully autonomous manner. Since the constraints of land vehicles navigating on roads and marine vehicles navigating at sea are taken into account in the study, the problem is approached from a different perspective compared to common studies in the literature. First, the QEA algorithm used in the ranking operations was adapted to the problem. Improvements were made to the adapted method to increase its performance. Then, a framework was proposed to operate this method as a quantum-classical hybrid. Significant contributions have been made to the proposed quantum-classical hybrid method by offering advantages in learning the parameters and solution update equations. The problem was solved using classical genetic algorithms (GA), QEA and the proposed method, and their solution performances were compared. It was observed that the proposed method was superior to QEA and produced results very close to those produced by GA. Experimental results confirmed the effectiveness of the proposed method. Implementing the method as a quantum-classical hybrid brings one step closer to the goal of realizing the solution in a quantum environment.
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.
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 genetic algorithms(QGA) typically construct traditional quantum chromosomes as independent quantum bit systems, which has facilitated the enhancement of search speed and precision in genetic algorithms, demonstrating powerful problem-solving capabilities in optimization issues. Although traditional QGA have enriched population diversity, they still tend to fall into local optima, and the evolutionary update operations require rotation directions to be determined by lookup tables, a process involving multiple conditional judgments that affect algorithm efficiency. To address these issues, an improved method has been proposed to further enhance the performance of QGA. In the initial population selection, a population grouping evolution method based on microhabitat coevolution strategy is used to prevent the algorithm from getting trapped in local optima when solving complex function problems. Additionally, quantum entanglement is introduced on top of quantum super-position by implementing quantum entanglement using quantum registers, and higher-order quantum rotation gates are employed that do not involve multiple conditional judgments of lookup tables, thus improving the efficiency of quantum chromosome measurement and evolutionary updates. Finally, traditional QGA and higher-order quantum genetic algorithms were applied to test functions in multiple experiments. The results indicate that, compared to traditional QGA, the higher-order quantum genetic algorithms have achieved an increase in convergence speed, an approximate 14.3% improvement in convergence precision, and possess enhanced global search capabilities.