Large-Scale Wireless Coverage Optimization: A Quantum Leap #Sciencefather

Introduction

Wireless network coverage optimization plays a pivotal role in enhancing service quality and ensuring seamless connectivity in modern communication infrastructures. However, as networks grow in scale and complexity, traditional optimization methods face computational bottlenecks. The advent of quantum computing offers promising alternatives, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, where hybrid approaches can be leveraged for efficiency. This work introduces a quantum-driven divide-and-conquer method that models coverage as a graph problem, partitions it via QUBO formulation, and employs advanced quantum algorithms to deliver scalable solutions.

Quantum Modelling of Network Coverage

The research models the wireless coverage optimization problem as a covering graph, enabling a structured representation of network nodes, coverage zones, and overlap constraints. This graph-based model provides a natural mapping to Quadratic Unconstrained Binary Optimization (QUBO), a form well-suited for quantum solvers. By abstracting real-world wireless environments into a mathematically tractable graph model, the approach allows for efficient problem decomposition and paves the way for hybrid classical-quantum computational strategies.

QUBO Formulation and Problem Partitioning

Central to this methodology is the QUBO formulation, which encodes the network coverage objectives and constraints into a binary optimization framework. The large-scale nature of real networks is addressed through a partitioning strategy that breaks the global problem into smaller, more manageable subproblems. This divide-and-conquer approach ensures compatibility with current quantum hardware limitations while maintaining solution fidelity.

Filtered Variational Quantum Eigensolver (FVQE) Approach

To solve the QUBO-partitioned subproblems, the research adopts a Filtered Variational Quantum Eigensolver (FVQE), which enhances the standard VQE by introducing filtering mechanisms for solution refinement. FVQE exploits quantum parallelism to explore a broader solution landscape while systematically eliminating low-quality candidates. This ensures convergence towards high-quality solutions even in the presence of hardware noise.

Experimental Validation on Quantum Hardware

The proposed method is experimentally validated using both a coherent Ising machine and a superconducting quantum processor, demonstrating its practical feasibility. Performance comparisons with classical optimization algorithms such as Simulated Annealing (SA) and Particle Swarm Optimization (PSO) reveal that the quantum-based method can achieve competitive or superior results in terms of coverage efficiency, convergence time, and solution robustness.

Expanding the Solution Landscape for Large-Scale Networks

By combining graph partitioning, QUBO formulation, and FVQE-based subproblem solving, this research broadens the scope of network optimization beyond what classical methods can handle efficiently. The hybrid quantum-classical approach not only scales to larger network sizes but also reveals novel configurations in the solution space, offering opportunities for future integration with AI-driven network management systems.

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Hashtags:

#WirelessNetworkOptimization, #QuantumComputing, #NetworkCoverage, #QUBO, #VQE, #FVQE, #QuantumAlgorithms, #NISQ, #DivideAndConquer, #GraphPartitioning, #IsingMachine, #SuperconductingQubits, #SimulatedAnnealing, #ParticleSwarmOptimization, #HybridQuantumClassical, #QuantumEnhancedOptimization, #CoveragePlanning, #5GOptimization, #TelecomResearch, #QuantumNetworking,