Quantum Technologies for Space and Aerial Vehicles

Sabia Hassan, Syed Naseer Ahmad Shah, Kaiser Javeed Giri

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.

Bongs, Kai

M. Krelina

Li Zhang, Jianbo Xu, Mohammad S. Obaidat +2 more

Abstract With the advancement of information technology and the reduction of costs, the application of unmanned aerial vehicle (UAV) has gradually expanded from the military field to the industrial field and civilian field. It brings great convenience to people in surveillance, detection, transportation, emergency rescue etc. However, UAVs usually work in harsh natural environments, and their communication security confronts various challenges. Due to UAVs' limited resources, such as computing capability, storage space, and energy, traditional security protection schemes based on complex cryptographic algorithms are not suitable for UAV systems directly. Therefore, a two‐stage lightweight identity authentication and key agreement protocol for UAV is proposed in this paper. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physical capture attack. In the paper, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, Real‐or‐Random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost, which shows that the proposed protocol has effectiveness and practicality.

Suresh Kumar Balakrishnan, Avudaiappan Tharmiah, Vidhya Ramalingam

This chapter explores the emerging field of quantum-enhanced predictive maintenance for unmanned aerial vehicles (UAVs). Traditional maintenance techniques, while valuable, have limitations that hinder optimal UAV performance. This chapter discusses these limitations and introduces quantum computing as a potential solution. Quantum algorithms can analyze vast datasets from UAV sensors, identifying subtle anomalies that predict failures before they occur. The chapter explores how this can improve battery health prediction, enable early detection of motor faults, and ensure the overall safety and reliability of UAV operations. Challenges associated with integrating quantum technology are addressed, along with the exciting future this holds for the UAV industry. By harnessing the power of quantum computing, UAV maintenance can become more proactive, efficient, and cost-effective, paving the way for a new era of reliable and safe UAV operations.

Rui A. P. Perdigão