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IEEE Transactions on Quantum Engineering

Quantum Communication Infrastructure Cost Optimization Using a Genetic Algorithm

Ange Joel Nounga Njanda, E. Egidio Purcino De Souza, Raul Camelo de Andrade Almeida Júnior, Anderson A. Tomkelski, K. D. R. Assis

Published Jan 1, 2026• 0 citations
Publisher

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.

DOI
10.1109/tqe.2026.3714062

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Quantum key distribution (QKD) provides a secure method to exchange encrypted information between two parties in a quantum communication infrastructure (QCI). The primary challenge in deploying a QCI is the cost of using optical fibers and trusted repeater nodes (TRNs). Practical systems combine quantum and classical channels on the same fiber to reduce the cost of fibers dedicated to QKD. In such a system with quantum-classical coexistence, the optimal distribution of QKD requests with minimal deployment cost and power usage on the multiplexed links is challenging due to the diverse key rate demands of the requests, number of classical and quantum channels, guard band spacing between classical and quantum channels, and secret key rate of the quantum channels that decreases with distance. To address these challenges, in this work, we propose a Steiner tree-based approach for constructing a QCI that connects all quantum nodes with minimum TRNs. In addition, we propose a genetic algorithm-based solution to optimally distribute the end-to-end QKD requests over the QCI. We also determine feasible optical bypass routes to reduce the overall energy consumption in the network further. The proposed approach reduces the QCI deployment cost by 19.42% compared to the benchmark MST-Baseline. Also, on average, TAQNet with optical bypass achieves 4.69 kbit per Joule more energy efficiency compared to the nonbypass approach.
Quantum communication ensures that data remains secure from unauthorized access. Despite these advantages, this technology faces several challenges. For instance, photons can be lost when they go through channels, and there is a chance of eavesdropping, that make the system not as trustworthy, safe, and efficient. To make quantum communication systems function effectively in the real world, we need to combine advanced technologies like quantum error correction mechanisms and quantum repeaters. This work suggests a unified Quantum Inspired Genetic Algorithm (QIGA) framework that utilizes collaborative quantum repeaters to conquer the limitations of network links over distance. This study introduces a hybrid optimization topology (OT) that integrates mesh, star, and ring configurations for measurement device-independent quantum key distribution (MDI-QKD) networks. This approach allows for the efficient use of resources for scalable quantum communication networks. The proposed network architecture integrates 12 user nodes, a trusted node, and a single shared quantum repeater within a hybrid topology. The repeater deployment has been designed to support links that are longer than 52 km. This design accommodates deployments on various scales, small, medium, and large, while optimizing resource usage and infrastructure costs. We evaluated the performance and security trade offs in quantum communication networks by optimizing three topologies with a QIGA: ring, ring-star, and ring-star-mesh hybrid (QIGA−OT). The optimization methodology aims to reconcile essential performance metrics, such as the Secure Key Rate (SKR), Quantum Bit Error Rate (QBER), channel loss, and Merit of Quantum Efficiency (MQE). MQE functions as a metric for evaluating trades concerning security, efficiency, and cost-effectiveness within the network. This work presents a novel approach for utilizing quantum-inspired in the effective design and implementation of scalable network topologies across numerous fields of application.
Quantum communication infrastructure (QCI) is gradually becoming a favorable choice for transmitting secure data across the globe. Network operators often prefer to utilize the existing network infrastructure to reduce network setup costs to build a QCI. This implies transforming traditional network nodes to quantum nodes (QNs) by equipping them with quantum processors. Trusted repeater nodes (TRNs) are used to connect distant QNs. However, the network infrastructure cost increases with the number of TRNs due to the usage of dark fibers. Therefore, it is challenging for the network planning to build a QCI that connects all QNs with a minimum number of TRNs to reduce the infrastructure cost. In this work, we construct a minimum cost spanning tree for the QCI to connect all QNs with a minimum number of links and TRNs. The proposed approach considers node distance and attenuation cost of the physical connections. Compared to the traditional spanning tree approach, our proposed approach reduces the cost of QCI deployment by 10.90%.
Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is O ( n 2 ) , where n is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with O ( n ) variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.
Quantum networks rely on repeaters to distribute entangled qubit pairs for applications such as quantum key distribution and distributed quantum computing. Minimizing the number of repeaters—while meeting connectivity and range constraints—is central to making deployments practical at scale. This thesis unifies three complementary lines of work toward that goal for terrestrial and non-terrestrial quantum networks. Two-Step Linear Programming (TSLP) is a scalable optimization framework that decomposes complex problems into two sequential linear programming steps to reduce computational complexity. First, a TSLP approach for repeater placement on existing infrastructure (terrestrial networks) is presented. Integer Linear Programming (ILP) is a mathematical modeling technique where variables are restricted to be integers to find the best possible outcome in a set of requirements. This work contributes a scalable optimization framework that addresses the computational limitations of traditional ILP models. While existing ILP solutions face O(n2) complexity, the proposed TSLP approach reduces this to O(n) variables. Simulation results on synthetic and real-world topologies show that TSLP dramatically decreases running time while achieving higher node connectivity through a strategic, slight increase in the number of repeaters. This advances research by enabling the design of large-scale fiber-based quantum networks that were previously computationally inaccessible. Second, a drone-based (non-terrestrial) repeater network for serving ground stations is studied. A key contribution of this work is the development of a polynomial-time, two- stage algorithm that decouples coverage and connectivity requirements. Stage 1 optimizes the coverage of ground terminals, while Stage 2 ensures the network backbone is fully connected. Third, the thesis addresses the need for fault tolerance in error-prone quantum net- works by extending the 1-connected drone model to achieve 2-connectedness. The Delaunay Triangulation with pruning, Steiner points, and pruning again (DT-PSP) is a geometry-aware heuristic that minimizes the number of required drone-based repeaters by leveraging geometric properties. The major contribution here is the introduction of the DT-PSP approach based on Delaunay Triangulation and Steiner node placement. Collectively, the proposed algorithms improve network connectivity and reduce deployment cost across diverse quantum-network settings. By bridging the gap between theoretical optimality and practical scalability, this PhD work provides a comprehensive toolkit for the real-world deployment of future large-scale quantum networks.
Long-distance quantum communication presents a significant challenge due to the degrading fidelity of transmitted qubits. Quantum repeaters are proposed to overcome this issue through Bell measurements on entangled qubit pairs. However, despite its necessity to enable wide-area quantum internet, the deployment cost of quantum repeaters can be prohibitively expensive; thus, developing a quantum repeater deployment model that can strike a balance between cost and effectiveness is essential. In this work, we present novel heuristic models to quickly determine a minimum number of quantum repeaters to deploy in large-scale networks to provide end-to-end connectivity between all end hosts. The results show that, compared to the integer linear programming approach, the heuristic methods can find near-optimal solutions while reducing the execution time from days to seconds when evaluated against several synthetic and real-world networks such as SURFnet and ESnet. As reliability is critical for any network, we demonstrate that the heuristic method can estimate deployment models to endure up to two link/node failures.