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.
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.
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.
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.
Ising machines (IMs) are hardware solvers designed to tackle computationally complex combinatorial optimization problems (COPs), harnessing physical processes such as quantum annealing to simulate the Ising model, enabling these specially designed solvers to tackle a wide range of computationally complex NP-hard problems in real-world applications, such as portfolio optimization and logistics planning. While prior works propose to fabricate dedicated integrated circuits for building IMs, we leverage off-the-shelf Graphics Processing Unit (GPU) chips for implementing Ising algorithms for quick development. In this work, we explore parallel tempering (PT), an Ising algorithm, which shows promise owing to its capability for parallel processing of multiple independent searches for the optimal solution, each with a different amount of randomness that allows escaping local minima. We propose several optimization strategies, including addressing the dependency problem in PT to enhance algorithm parallelism and integrating genetic algorithm (GA)-like operations to increase the diversity of the search process for effective solution discovery. Empirical evaluations demonstrate that our proposed Parallel Quantum-inspired Search (PQS) solver achieves a 2.66× speedup over the state-of-the-art GPU-based solution without sacrificing solution quality.
Optimization problems, which involve finding the best solution from a set of possible solutions, are ubiquitous in various fields, from finance to engineering. Traditional algorithms sometimes struggle with these problems, especially when the solution space is vast, or the landscape is filled with numerous local minima. Quantum-inspired computing, which emulates quantum mechanical principles on classical hardware, emerges as a promising paradigm to address these challenges. This paper delves into two notable approaches: coherent Ising machines (CIM) and graphics processing unit (GPU)-accelerated simulated annealing. In essence, both methods offer innovative strategies to navigate the solution landscape, potentially bypassing the pitfalls of local optima and ensuring more efficient convergence to solutions. By harnessing the strengths of these quantum-inspired techniques, we can pave the way for enhanced computational capabilities in tackling complex optimization problems, even without a fault-tolerant quantum computer.
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.