Quantum computing is a promising technology which is able to speed up computation, by several orders of magnitude, with respect to classical systems. In particular, quantum optimization can be employed to solve complex problems in many engineering fields. In this work, it is proposed to use Quantum Annealing (QA) to solve a binary optimization problem to derive an optimal scheduling plan in an Unmanned Aerial Vehicle (UAV)-enabled Internet of Things (IoT) network. Based on specific design criteria, specific problem formulation can be cast, thus providing real-time solutions.
Quantum computing is a promising technology which is able to speed up computation, by several orders of magnitude, with respect to classical systems. In particular, quantum optimization can be employed to solve complex problems in many engineering fields. In this work, it is proposed to use Quantum Annealing (QA) to solve a binary optimization problem to derive an optimal scheduling plan in an Unmanned Aerial Vehicle (UAV)-enabled Internet of Things (IoT) network. Based on specific design criteria, specific problem formulation can be cast, thus providing real-time solutions.
In recent years, with the development of Unmanned Aerial Vehicle (UAV) technology and its widespread application in military, logistics, agriculture, and other fields, the problem of UAV swarm trajectory planning has received extensive attention. Traditional optimization algorithms such as simulated annealing, genetic algorithms, and particle swarm optimization can achieve good results in some cases. However, when dealing with larger and more complex UAV swarm tasks, they often face issues such as low computational efficiency and getting stuck in local optima. Quantum annealing, which has the unique advantage of quantum tunneling, can effectively avoid local optima. Therefore, this study proposes a UAV swarm trajectory planning algorithm based on quantum annealing. The trajectory planning problem is converted into a Quadratic Unconstrained Binary Optimization (QUBO) problem. Using a two-stage processing strategy, the quantum annealing method clusters the task points and simulates the trajectory for each category, effectively reducing time complexity. Results show that quantum annealing has a higher probability of finding better paths than simulated annealing, demonstrating a better ability to escape the local optima problem. Additionally, the study considers four common scenarios that UAV swarms encounter during missions, designs corresponding dynamic task allocation schemes and modifies the objective function and constraints of quantum annealing. Results indicate that the UAV swarm trajectory planning algorithm can handle common scenarios effectively, ensuring that the UAV swarm can flexibly respond and efficiently complete tasks collaboratively.
We present annealing solutions for route optimization problems for unmanned aerial vehicles (UAVs), The route planning problem is a generalised version of the Travelling Salesman Problem which has many applications in logistics planning. Our annealing solutions combine classical algorithms with quantum/digital annealers specifically designed for quadratic optimization problems and shows improvements over generic traditional solvers such as Gurobi. We empirically test the solvability of our annealing solutions on both hand-crafted hard test cases as well as simulated ‘real-world’ examples of urban environments where the operations of the UAVs can be affected by the direction and intensity of wind, which is conditioned by the presence of buildings.
This paper studies the application of unmanned aerial vehicles (UAVs) in sensing systems, focusing on minimizing total detection errors by jointly designing the UAV’s trajectory and beamforming vectors. Due to the structure of a mixed-integer nonlinear program (MINLP), the formulated optimization problem poses significant computational challenges for classical processors. To address this bottleneck, we first determine the optimal beamforming vectors as a closed-form solution at each UAV coordinate. We then leverage the advantages of quantum annealers to optimize the trajectory of the UAV. Specifically, we formulate the UAV’s trajectory optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem, with the constraints effectively handled by our suggested penalty factors. We further ensure the feasibility by establishing a lower bound of penalty factors. Numerical results demonstrate the efficiency of the proposed quantum-based approach, which is comparable to the genetic algorithm with a substantially shorter running time.
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.
Rapid advancement in Unmanned Aerial Vehicles (UAVs) and their increasing utility in different areas of human life raises a need for collision-free flying in airspace during operation. Especially when thousands of UAVs operates in specific airspace of few kilometres then it is difficult to manage collision free operations of these UAVs. However, each UAV can choose from many possible trajectories, speeds, or altitudes to avoid conflicts. As the number of UAVs increases, it becomes computationally infeasible to find an optimal and collision-free solution by examining all possible configurations. As a result, this is classified as NP-complete problem, meaning that there is no known conventional algorithm can provide a solution in feasible time. It means that the classical algorithms require exponential time to solve it for large problem instances. On the other hand quantum computing could provide potential speed up for solving such problem. Here, we present a quantum method for reduce the time complexity of solving the problem. Our contribution in this paper are to design a quantum algorithm for unmanned aerial vehicles conflict resolution (UAVs-CR) problem, to design an explicit oracle to solve this problem as well as encoding the problem information into basis states in order to use quantum search algorithm, known for its quadratic speed-up in unstructured database search. A series of experiments were carried out using the IBM Quantum computer and the AerSimulator running locally to examine the scalability, correctness, and runtime efficiency of the proposed quantum methodology.
Quantum computing holds transformative potential for optimizing large-scale drone fleet operations, yet its nearterm limitations necessitate hybrid approaches blending classical and quantum techniques. This work introduces Quantum Unmanned Aerial Delivery Routing Optimization (QUADRO), a novel hybrid framework addressing the Energy-Constrained Capacitated Unmanned Aerial Vehicle Routing Problem and the Unmanned Aerial Vehicle Scheduling Problem. By formulating these challenges as Quadratic Unconstrained Binary Optimization problems, QUADRO leverages the Quantum Approximate Optimization Algorithm for routing and scheduling, enhanced by classical heuristics and post-processing. We minimize total transit time in routing, considering payload and battery constraints, and optimize makespan scheduling across various drone fleets. Evaluated on adapted Augerat benchmarks (16-51 nodes), QUADRO competes against classical and prior hybrid methods, achieving scalable solutions with fewer than one hundred qubits. The proposed results underscore the viability of hybrid quantumclassical strategies for real-world drone logistics, paving the way for quantum-enhanced optimization in the Noisy Intermediate Scale Quantum era.