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.
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.
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.
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 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.
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.
With the rapid advancement of smart agriculture, Unmanned Aerial Vehicles (UAVs) have become indispensable for efficient coverage and path planning in grid-mapped agricultural fields. However, optimizing UAV flight paths in large-scale environments remains a complex Nondeterministic Polynomial-time (NP)-hard problem. Traditional path planning methods are often constrained by local optima, limited scalability, and slow convergence, which significantly restrict their effectiveness in solving large-scale problems. To address these limitations, this paper proposes a novel optimization framework utilizing a Graphics Processing Unit (GPU)-parallelized Ising solver. Our method mimics the operational principles of quantum annealing on GPU hardware, enabling rapid search for the minimum-energy state of Ising models. Unlike physical quantum devices, which are often constrained by the number of qubits, our approach leverages the Fixstars Amplify (FA) platform to perform parallel annealing on highly parallelized GPUs. This enables the simultaneous evaluation of thousands of potential path candidates and vast state transitions. By leveraging large-scale parallel processing, this framework's core strength lies in minimizing computation time even as the problem scale increases. Simulation results demonstrate that our proposed method consistently identifies superior flight paths while maintaining stable computational performance compared with the genetic algorithm and simulated annealing method.
Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we propose a formulation to control the traveling routes to minimize the travel time. We validate our formulation through simulation in a virtual plant and authenticate the effectiveness for faster distribution compared to a greedy algorithm that does not consider the overall detour distance. Furthermore, we utilize reverse annealing to maximize the advantage of the D-Wave's quantum annealer. Starting from relatively good solutions obtained by a fast greedy algorithm, reverse annealing searches for better solutions around them. Our reverse annealing method improves the performance compared to standard quantum annealing alone and performs up to 10 times faster than a commercial classical solver, Gurobi. This study extends a use of optimization with general problem solvers in the application of multi-AGV systems and reveals the potential of reverse annealing as an optimizer.