Quantum Communication Infrastructure Cost Optimization Using a Genetic Algorithm

Abstract

This article proposes an optimization model for the design of quantum communication infrastructures (QCI) with the aim of minimizing capital expenditure (CAPEX), focusing on the costs associated with the deployment of quantum links and trusted repeater nodes. The model is formulated using an integer linear programming (ILP) program that incorporates physical and operational constraints to ensure efficient resource allocation. The simulation results obtained using an ILP solver show that increasing wavelength availability leads to significant reductions in CAPEX while consistently producing solutions that satisfy all constraints. However, because ILP becomes computationally expensive for large-scale networks, we propose two genetic algorithms ($\text{GA}_{1}$ and $\text{GA}_{2}$) designed to improve scalability. Both $\text{GA}_{1}$ and $\text{GA}_{2}$ offer competitive computation times compared with the ILP solver and provide near-optimal solutions compared with the ILP solution. Overall, this approach is effective for small, medium, and large-scale QCI deployments, offering a scalable and practical solution suitable for real-world implementation. For example, in the small network topology with nine nodes and nine requests, we observe a reduction of CAPEX up to 33% when the number of available wavelengths increases from 1 to 2, and we note that GAs in a large topology, such as NET-4 network, reduce CAPEX by 34.6% ($\text{GA}_{1}$), and 21.1% ($\text{GA}_{2}$) compared to the benchmarks.