This paper addresses the multi-UAV cooperative path planning problem, a typical combinatorial optimization challenge, by systematically investigating a quantum annealing-based computational approach. Two QUBO modeling strategies are proposed: one defines inter-node transitions, while the other introduces a temporal dimension, both transforming the Multiple Traveling Salesman Problem into a Hamiltonian form solvable via quantum annealing. Experimental validation on a 10-node scenario, comparing results from a coherent Ising machine and the ant colony algorithm, demonstrates that the proposed models achieve solution quality comparable to the classical method. Specifically, the edge-variable model yields shorter total path distance, whereas the time-step model exhibits superior load-balancing performance. The findings provide feasible insights and methodological support for applying quantum computing to combinatorial optimization scenarios such as UAV formation control and logistics scheduling.
This paper addresses the multi-UAV cooperative path planning problem, a typical combinatorial optimization challenge, by systematically investigating a quantum annealing-based computational approach. Two QUBO modeling strategies are proposed: one defines inter-node transitions, while the other introduces a temporal dimension, both transforming the Multiple Traveling Salesman Problem into a Hamiltonian form solvable via quantum annealing. Experimental validation on a 10-node scenario, comparing results from a coherent Ising machine and the ant colony algorithm, demonstrates that the proposed models achieve solution quality comparable to the classical method. Specifically, the edge-variable model yields shorter total path distance, whereas the time-step model exhibits superior load-balancing performance. The findings provide feasible insights and methodological support for applying quantum computing to combinatorial optimization scenarios such as UAV formation control and logistics scheduling.
UAV path planning in multi-target mission scenarios presents complex optimization challenges, particularly when considering constraints such as energy consumption and time efficiency. This paper proposes a quantum computing-driven approach using the QUBO model, integrated with a coherent Ising machine (CIM) on an optical quantum platform, to optimize UAV path planning. By modeling the path planning problem as a QUBO formulation and utilizing quantum heuristic algorithms for optimization, the proposed method overcomes the bottlenecks of traditional heuristic algorithms in terms of computational efficiency and convergence speed. Experimental results demonstrate that the proposed approach significantly outperforms traditional algorithms such as Tabu Search and Simulated Annealing, particularly as the number of target points increases. This method provides a new approach for applying quantum computing to UAV path planning, promoting the practical use of quantum technology in industrial applications.
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.
This paper explores the application of quantum annealing to the challenging domain of combinatorial optimization within large-scale networks. Specifically, it investigates the feasibility of utilizing quantum annealing for problems such as network routing and scheduling, where traditional classical algorithms often struggle due to computational complexity. The core approach involves formulating the network optimization problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This transformation enables the mapping of the problem onto the quantum annealing hardware, leveraging the quantum annealing process – characterized by its adiabatic evolution – to discover the optimal solution. We argue that this method presents a potential advantage over conventional algorithms for large-scale network scenarios, offering a pathway to tackling problems previously considered intractable. The theoretical framework and underlying principles are thoroughly discussed, outlining the methodology and highlighting the anticipated benefits.
The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.
Autonomous mobile robots are being used increasingly as consumer devices around the globe, such as for fetching items and cleaning purposes, to name a few, in households and industries. Such robots are being employed to traverse a set of target locations and provide the necessary services. In a 2-dimensional environment, these robots are required to traverse following a Hamiltonian path to reduce energy consumption and time requirements. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, upon urgent requirements, these robots must traverse in real-time, demanding speedy path planning from the TSP instance. Among the well-known optimization techniques for solving the TSP problem, Ant Colony Optimization has a good stronghold in providing good approximate solutions. Moreover, ACO not only provides near-optimal solutions for TSP instances but can also output optimal or near-optimal solutions for many other demanding hard optimization problems. However, most of the implementations of Ant Colony Optimization on quantum or hybrid quantum architecture proposed in the literature require conversion of classical data to qubits before being fed to the algorithm, and cannot be automated. But quantum-enabled mobile robots require automated path formation after receiving the commands from the environment. The novelties of the proposed work are many-fold. Firstly, the proposed work allows ACO to be applied as its classical counterparts, allowing automation in path formation in quantum-enabled mobile robots. Secondly, this allows a new way of incorporating quantum processing unit in the research of quantum-enabled mobile robots. Researchers around the globe have been trying to incorporate quantum computing in autonomous mobile robots, and true to the best of authors’ knowledge, no work in path planning for multiple targets in quantum-enabled mobile robots have been found in literature. Thirdly, quantum processing unit has been applied at exactly that point where it will be most useful, as in NISQ era quantum computer is not reliable for arithmetic processing. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results with average error percentage from optimumresults of only 6.985%. Hence, it is expected that the proposed work to be important in future research of fusing the two rising domains of quantum computing and autonomous mobile robots.