In large-scale disaster scenarios, rapid localization of missing persons is a critical challenge for search-and-rescue (SAR) operations. Unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) receivers can support RF-based localization by collecting signals emitted from mobile devices at spatially distributed sensing locations. This paper addresses the resulting waypoint-selection problem: determining a minimum set of UAV waypoints that provides at least threefold coverage of every potential target location. We formulate this task as an extended k-coverage problem that independently defines the UAV-navigable and target regions, and derive an exact-penalty quadratic unconstrained binary optimization (QUBO) formulation with a sufficient penalty condition that preserves feasibility and minimum waypoint cardinality. The QUBO is mapped to an Ising-form cost Hamiltonian and evaluated using the quantum approximate optimization algorithm (QAOA) on both a noise-free simulator and IBM's 127-qubit Eagle processor. On the tested simulator instances, QAOA recovers the known minimum-cardinality solutions. Across the rectangular hardware test cases, the mean 3-coverage ratio exceeded 95%. In the campus-scale evaluation, ten hardware executions achieved 99.3% mean 3-coverage with a 90% feasible-run rate, while the shortest feasible flight path was up to 37.0% shorter than those of the deterministic grid-based baselines. Additional comparisons with classical optimization and learning-based baselines are provided, together with computational and quantum-resource analyses for larger generated instances. These results establish an exact QUBO representation for RF-based SAR waypoint selection and characterize its implementation on current gate-based quantum hardware.
In large-scale disaster scenarios, rapid localization of missing persons is a critical challenge for search-and-rescue (SAR) operations. Unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) receivers can support RF-based localization by collecting signals emitted from mobile devices at spatially distributed sensing locations. This paper addresses the resulting waypoint-selection problem: determining a minimum set of UAV waypoints that provides at least threefold coverage of every potential target location. We formulate this task as an extended k-coverage problem that independently defines the UAV-navigable and target regions, and derive an exact-penalty quadratic unconstrained binary optimization (QUBO) formulation with a sufficient penalty condition that preserves feasibility and minimum waypoint cardinality. The QUBO is mapped to an Ising-form cost Hamiltonian and evaluated using the quantum approximate optimization algorithm (QAOA) on both a noise-free simulator and IBM's 127-qubit Eagle processor. On the tested simulator instances, QAOA recovers the known minimum-cardinality solutions. Across the rectangular hardware test cases, the mean 3-coverage ratio exceeded 95%. In the campus-scale evaluation, ten hardware executions achieved 99.3% mean 3-coverage with a 90% feasible-run rate, while the shortest feasible flight path was up to 37.0% shorter than those of the deterministic grid-based baselines. Additional comparisons with classical optimization and learning-based baselines are provided, together with computational and quantum-resource analyses for larger generated instances. These results establish an exact QUBO representation for RF-based SAR waypoint selection and characterize its implementation on current gate-based quantum hardware.
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.
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.
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.
We present a combinatorial optimization problem for the strategic deployment of UAVs equipped with 5G antennas to assist rescue operations in regions hit by natural disasters. Our goal is to optimize the placement of UAVs to provide coverage in flying ad-hoc networks among given candidate sites. Our formulation aims to maximize signal coverage and minimize interference while ensuring network connectivity. To mitigate interference effects, we incorporate the use of multiple frequencies. We formulate this problem as an integer quadratic program (IQP). We present numerical solutions obtained via the CPLEX solver and conduct a preliminary analysis of the problem's scalability in realistic network configurations. Our findings reveal a significant exponential increase in Time-to-Solution (TTS) as the number of sites grows, which poses a critical challenge in urgent, time-sensitive scenarios. To address this issue, approximate suboptimal solutions can be produced by enforcing a time limit on the solver. Although these solutions are not optimal, they preserve connectivity in most cases, providing a practical trade-off between solution quality and computational times that remain within feasible limits for real-time UAV redeployment. Recognizing the limitations of classical solvers in these contexts, we explore quantum computing as a promising alternative. Specifically, we reformulate the problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for most quantum algorithms. Through high-performance computing emulation, we show that the quantum adiabatic algorithm (QAA) can accurately solve small-scale instances, paving the way for future application of quantum computing to large-scale, time-critical optimization problems in disaster response.
Drone-delivery planning combines routing decisions with operational constraints such as payload capacity and limited onboard energy, resulting in combinatorial optimization problems that can become costly to encode directly in binary form. This work investigates a hybrid quantum--classical approach in which candidate drone missions are generated and evaluated classically before constructing a quadratic unconstrained binary optimization (QUBO) model for mission selection. Payload- and energy-infeasible missions are removed during preprocessing, while the reduced QUBO is solved using exact enumeration, simulated annealing (SA), and the Quantum Approximate Optimization Algorithm (QAOA), with mixed-integer linear programming (MILP) used as a classical reference. Experiments on controlled synthetic delivery instances evaluate solution quality, the effect of preprocessing, and computational scaling. Exact QUBO optimization reproduces the MILP optimum on the evaluated small instances, while SA achieves 100% feasibility with a median optimality gap of 0%. Combined payload-and-energy preprocessing reduces the mean number of QUBO variables by 29.60% and quadratic interactions by 49.20%, while reducing mean SA runtime by 31.26%. In contrast, the evaluated shallow statevector-based QAOA configuration achieves lower feasibility and is restricted to small QUBO instances. These results indicate that, in the considered setting, the main practical benefit of the hybrid architecture comes from classical feasibility preprocessing that reduces the binary search space before QUBO-based optimization.
The rapid development of Machine Learning (ML) algorithms across domains necessitates efficient Hyperparameter Optimization (HPO) to tailor models to specific tasks. Classical HPO techniques become prohibitively slow as model size and search space dimensionality grow. Quantum computing, particularly the Quantum Approximate Optimization Algorithm (QAOA), offers a principled way to explore large combinatorial spaces, but existing QAOA approaches are limited in black-box ML settings, where objective evaluations are expensive. The need for fast yet accurate HPO is most acute in security-critical applications such as Unmanned Aerial Vehicle (UAV) fingerprinting, where defenders must identify hostile drones within seconds. To address this challenge, we propose a QAOA-Based High-Speed Weighted Black-box Hyperparameter Optimization (QWBO), a generic HPO method that directly encodes discretized hyperparameters into qubits. A customized QAOA circuit imposes data-driven penalty weights, and its low-energy states decode to the optimal hyperparameter set. We evaluate QWBO on a realworld XGBoost-based UAV fingerprinting task. QWBO attains 93% classification accuracy, surpassing grid search and Bayesian optimization while requiring far less runtime. Moreover, its runtime grows sublinearly with search space size, demonstrating strong potential for rapid, large-scale HPO across diverse ML models and security domains.