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.
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.
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.
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.
Airline and space mission scheduling present complex combinatorial optimization challenges, requiring sophisticated coordination of multiple resources under stringent operational constraints. This paper introduces a novel hybrid quantum-classical framework for scheduling optimization, the Quantum Scheduling Optimization Algorithm (QSOA), addressing crew assignment, gate allocation, and trajectory planning through advanced quantum machine learning techniques. We formulate the scheduling problems as stochastic multi-objective optimization and propose three integrated solution approaches: (1) hybrid quantum optimization via QAOA with constraint-aware loss functions and problem decomposition strategies, (2) classical-quantum integration using graph partitioning and variational circuit optimization, and (3) comprehensive baselines including mixed-integer programming, genetic algorithms, and reinforcement learning formulations. For multi-objective handling, we develop Pareto-optimal sampling techniques combining quantum superposition with classical post-processing for constraint repair and solution refinement. Our experimental evaluation on realistic airline crew scheduling benchmarks and space mission simulators demonstrates significant improvements: the hybrid QSOA achieves 19.2\% cost reduction in airline scheduling compared to classical heuristics, while quantum methods provide competitive solutions for moderate-sized space mission subproblems with 15-18\% improvement in multi-objective trade-offs. The framework shows promise for scalable deployment in real-world transportation networks, with practical implications for next-generation scheduling optimization in complex operational environments.
Quantum computing, as an emerging computational paradigm, has garnered significant attention due to its potential advantages in combinatorial optimization and quantum chemistry problems. A hybrid quantum-classical model that integrates VQE and QAOA for efficient task assignment and path optimization is suggested. However, constrained by hardware noise, qubit limitations and parameter optimization bottlenecks, these algorithms still face challenges such as convergence instability, gradient vanishing and low ground state probability in large-scale problems. This paper proposes a hybrid optimization framework that integrates improved VQE and QAOA, introducing adaptive parameter initialization and multi-level parameter scheduling mechanisms into VQE. In contrast, QAOA combines an improved classical optimizer and parameter perturbation strategy, thereby enhancing the algorithm’s global convergence capability in the search space. Experiments on combined optimization examples with 6, 9, 10 and 12 qubits demonstrate that this method can significantly improve ground state probability and convergence stability under limited hardware conditions. The results validate the effectiveness of the proposed framework in complex scheduling and resource allocation tasks, showcasing its potential applications in the practical implementation of quantum optimization.
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.