Quantum-Assisted Design of Space-Terrestrial Integrated Networks

TL;DR

This paper explores using quantum algorithms to optimize the design of integrated satellite-terrestrial networks, demonstrating comparable or superior performance to classical methods in complex network deployment problems.

Contribution

It introduces a hybrid quantum-classical approach for key network optimization problems, formalizes three combinatorial problems, and benchmarks quantum solutions against classical methods.

Findings

Quantum solutions closely match classical exact solvers.

Quantum approaches outperform greedy heuristics.

Results suggest potential advantages for larger or more complex instances.

Abstract

Achieving ubiquitous global connectivity requires integrating satellite and terrestrial networks, particularly to serve remote and underserved regions. In this work, we investigate the design and optimization of Space-Terrestrial Integrated Networks (STINs) using a hybrid quantum-classical approach. We formalize three key combinatorial optimization problems: the Satellite Selection Problem (SSP), the Gateway Selection Problem (GSP), and the Spectrum Assignment Problem (SAP), each capturing critical aspects of network deployment and operation. Leveraging neutral-atom quantum processors, we map the SSP onto a Maximum Weight Independent Set problem, embedding it onto the Aquila platform and solving it via the Quantum Adiabatic Algorithm (QAA). Postprocessing ensures feasible solutions that guide downstream GSP and SAP optimization. Benchmarking across 165 realistic remote regions shows that QAA solutions closely match classical exact solvers and outperform greedy heuristics, while subsequent GSP and SAP outcomes remain largely robust to differences in initial satellite selection. These results demonstrate that quantum optimization achieves performance broadly comparable to classical approaches for end-to-end STIN design, with rare instances where it can even surpass state-of-the-art solvers. This suggests that, while not yet consistently superior, quantum methods may offer competitive advantages for larger or more complex instances of the underlying combinatorial subproblems.