Combinatorial optimization of connected UAV communication bridges for emergency response

• URL: http://arxiv.org/abs/2609.02562v1
• Date: Wed, 02 Sep 2026 13:14:12 GMT
• Title: Combinatorial optimization of connected UAV communication bridges for emergency response
• Abstract summary: We present a quadratic optimization problem for the strategic deployment of UAVs equipped with 5G antennas.<n>Our formulation aims to maximize signal coverage and minimize interference while ensuring network connectivity.<n>We show that the quantum adiabatic algorithm (QAA) can accurately solve small-scale instances.
• Score: 1.5861469511290378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• Quantum-Classical Hybrid Quantized Neural Network [8.382617481718643]
  We present a novel Quadratic Binary Optimization (QBO) model for quantized neural network training, enabling the use of arbitrary activation and loss functions.<n>We employ the Quantum Gradient Conditional Descent (QCGD) algorithm, which leverages quantum computing to directly solve the QCBO problem.
  arXiv  Detail & Related papers  (2025-06-23T02:12:36Z)
• Parallel splitting method for large-scale quadratic programs [1.4519462317941787]
  SPLIT is a framework for decomposing large-scale quadratic programs into smaller subproblems, which are then solved in parallel.<n>We show that SPLIT is capable of providing drastic reductions in computational time, while delivering high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T09:45:47Z)
• Quantum Algorithms for Drone Mission Planning [0.0]
  Mission planning often involves optimising the use of ISR (Intelligence, Surveillance and Reconnaissance) assets in order to achieve a set of mission objectives. Finding such solutions is often an NP-Hard problem and cannot be solved efficiently on classical computers. We investigate near term quantum algorithms that have the potential to offer speed-ups against current classical methods.
  arXiv  Detail & Related papers  (2024-09-27T10:58:25Z)
• ILP-based Resource Optimization Realized by Quantum Annealing for Optical Wide-area Communication Networks -- A Framework for Solving Combinatorial Problems of a Real-world Application by Quantum Annealing [5.924780594614675]
  In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system. Here we report on our investigations for optimizing system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave.
  arXiv  Detail & Related papers  (2024-01-01T17:52:58Z)
• Multi-Objective Optimization and Network Routing with Near-Term Quantum Computers [0.2150989251218736]
  We develop a scheme with which near-term quantum computers can be applied to solve multi-objective optimization problems. We focus on an implementation based on the quantum approximate optimization algorithm (QAOA)
  arXiv  Detail & Related papers  (2023-08-16T09:22:01Z)
• Qubit efficient quantum algorithms for the vehicle routing problem on NISQ processors [48.68474702382697]
  Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
  arXiv  Detail & Related papers  (2023-06-14T13:44:35Z)
• Quantum-inspired optimization for wavelength assignment [51.55491037321065]
  We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
  arXiv  Detail & Related papers  (2022-11-01T07:52:47Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Channel Assignment in Uplink Wireless Communication using Machine Learning Approach [54.012791474906514]
  This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
  arXiv  Detail & Related papers  (2020-01-12T15:54:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.