For the multi-unmanned aerial vehicle (UAV) grouping problem in complex combat scenarios, a quantum-classical hybrid solution method based on quantum approximate optimization algorithm (QAOA) is proposed. According to the principle of minimizing combat distance, the task set is divided in-to task areas, and the multi-UAV grouping problem is transformed into a task area coverage problem to minimize the supply-demand vector of multi-UAV task grouping, achieving the matching of platform capability supply and task resource demand. The solution idea of the quantum approximate algorithm is proposed, the optimal mathematical model is derived into the final Hamiltonian and converted into the quantum Ising model; a quantum circuit is designed based on the pyQPanda framework and iteratively optimized using the gradient descent optimizer to form a closed-loop feedback calculation for solution. Experiments have proved the feasibility of the QAOA in the multi-UAV grouping problem and analyzed the algorithm performance, providing a research basis for subsequent more complex multi-UAV grouping problems.
For the multi-unmanned aerial vehicle (UAV) grouping problem in complex combat scenarios, a quantum-classical hybrid solution method based on quantum approximate optimization algorithm (QAOA) is proposed. According to the principle of minimizing combat distance, the task set is divided in-to task areas, and the multi-UAV grouping problem is transformed into a task area coverage problem to minimize the supply-demand vector of multi-UAV task grouping, achieving the matching of platform capability supply and task resource demand. The solution idea of the quantum approximate algorithm is proposed, the optimal mathematical model is derived into the final Hamiltonian and converted into the quantum Ising model; a quantum circuit is designed based on the pyQPanda framework and iteratively optimized using the gradient descent optimizer to form a closed-loop feedback calculation for solution. Experiments have proved the feasibility of the QAOA in the multi-UAV grouping problem and analyzed the algorithm performance, providing a research basis for subsequent more complex multi-UAV grouping problems.
Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs' collision avoidance model.
Abstract The three-dimensional matching problem is a classic NP-complete problem in combinatorial optimization, with wide applications in resource allocation, task scheduling, and many other practical scenarios. Traditional algorithms for solving the three-dimensional matching problem face the challenge of high time complexity, making it difficult to solve large-scale problems within a reasonable time frame. To improve the solution efficiency, a quantum circuit solution based on the Quantum Approximate Optimization Algorithm (QAOA) is proposed. By constructing a mathematical model of the three-dimensional matching problem, the corresponding Hamiltonian expression is derived. A QAOA-based quantum circuit is designed and the parameters of the parameterized quantum gates are optimized using the classical optimization algorithm COBYLA. Simulation experiments are performed using IBM's quantum development framework Qiskit. The experimental results show that the solution to the three-dimensional matching problem can be obtained with 84% probability in polynomial time, verifying the feasibility and effectiveness of solving the three-dimensional matching problem based on the Quantum Approximate Optimization Algorithm.
The increasing availability of quantum hardware has stimulated interest in applying quantum algorithms to combinatorial optimization problems. Collision avoidance for unmanned aerial vehicles (UAVs) is one such challenge due to the computational complexity of coordinating multiple agents in shared airspace. In this paper, we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model that captures route selection and collision constraints, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA). To improve efficiency, we introduce a constrained QAOA Ansatz employing an XY-mixer that preserves one-hot route assignment constraints and restricts the search space to valid solutions. We evaluate the approach through noiseless simulations and device-based noise models, analyzing its behavior under realistic conditions and highlighting the opportunities and limitations of near-term quantum hardware for multi-agent routing problems.
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.
The mission planning problem in command and control organization can be mapped into a combinatorial optimization problem with many variables and is difficult to solve. The traditional heuristic list planning method faces the problems of high time complexity and poor real-time response. For the mission planning problem in command and control organization, a quantum circuit solution scheme is proposed based on quantum approximate optimization algorithm in this work. Firstly, the mission planning problem is transformed into a typical combinatorial optimization problem, the exact coverage problem. Then, by constructing the corresponding mathematical model, the final state Hamiltonian expression of the quantum approximate optimization algorithm for the exact coverage problem is derived. The quantum circuit based on the quantum approximate optimization algorithm is designed. Finally the parameters in the quantum logic gate are optimized by the momentum gradient descent algorithm, and the simulation experiment is carried out by using the quantum software development environment of the Origin Quantum Computing Company. The simulation results show that the quantum circuit scheme can be used to solve the mission planning problem, reduce the time complexity of the algorithm, and improve the resource utilization to a certain extent. This work lays the foundation for further application of quantum algorithm to solving the mission planning problem in command and control organization.
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.