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.
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.
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.
Quantum computers can potentially solve large-scale combinatorial problems very efficiently when the problems are first converted into the quadratic unconstrained binary optimization (QUBO) format. Scheduling in fifth generation (5G) base stations is a practical combinatorial problem that cannot be solved optimally in real-time using classical computing. We formulate the downlink (DL) multiple-input multiple-output (MIMO) scheduling at 5G base stations as QUBO and analyze the QUBO formulation scalability with respect to the key system parameters. The single-user MIMO (SU-MIMO) QUBO formulation looks promising because the number of QUBO variables grows linearly while the problem search space grows exponentially with increasing number of users. Based on the simulation results, the suboptimal greedy algorithm for the SU-MIMO performs well with a high number of users and low bandwidth. A hybrid approach, where either a quantum solver or a suboptimal low-complexity classical algorithm is selected based on the system parameters, seems sensible in practice. This work paves the way for future quantum and quantum-inspired implementations of scheduling in cellular systems.
An increase in earth observation missions has increased the demand of efficient design and optimization of satellite constellations. Maximizing coverage of the target while effectively utilizing the limited orbital resources is one of the critical design challenges for complex combinatorial optimization problems. The maximal covering location problem (MCLP), serves as a base for orbital coverage modeling, is NP-hard and computationally intractable for large-constellation instances. Using heuristics, metaheuristics, and mixed-integer linear programming, classical solvers have achieved optimal or near-optimal results, yet their scalability is limited as the problem size increases. Quantum computing advancements, including the quantum approximate optimization algorithms, offer a potential solution to NP-hard combinatorial optimization problems. Current quantum hardware limitations, such as low qubit counts and circuit depth, restrict solutions for small-scale instance problems. To address this challenge, this paper proposes a scalable quantum optimization framework for MCLP in satellite constellation design. A decomposition-based quantum methodology is proposed, in which large MCLP instances are partitioned into subgraphs by classical decomposition, optimized independently via quantum optimization circuits, and combined using quantum reconstruction strategies. Computational results across different constellation sizes reveal better scalability in less time while maintaining competitive coverage performance compared to classical solvers.
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.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
We study the problem of emergency operation center placement in disaster response, where a minimal number of hubs must be selected to ensure timely coverage of all affected locations. This task can be formulated as a minimum dominating set problem on a graph encoding reachability within a target response time. We propose a hybrid quantum-classical approximation framework that leverages neutral-atom quantum computers as independent set samplers. Candidate dominating sets are constructed from both small maximal independent sets and complements of large independent sets, and are subsequently refined via a lightweight classical procedure. We benchmark the approach on synthetic instances and realistic case studies, and implement it on the Fresnel quantum processor by Pasqal, solving instances of up to 100 nodes. Our results show that quantum-generated samples, despite hardware noise, enable near-optimal solutions of the placement problem. Overall, our results demonstrate that neutral-atom devices operating in analog mode can already be used to tackle graph optimization problems for real-world applications.