Column Generation for the Optimization of Switching in Repeaterless Quantum Networks

2026-04-22

AI Summary

This work addresses the challenge of optical switch configuration in repeaterless quantum communication networks, where combinatorial explosion renders conventional approaches intractable. The paper presents the first scalable, systematic optimization framework by introducing a novel graph model that unifies the physical and logical structures of the network. This formulation enables the switch configuration problem to be cast as a linear program, which is then efficiently solved via a column generation algorithm capable of navigating the exponentially large configuration space. Experimental results demonstrate that the proposed method achieves optimal solutions while significantly enhancing computational scalability, thereby providing both theoretical foundations and practical algorithms for control optimization in large-scale quantum networks.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Efficient resource allocation and optical switching promise high key rates, network adaptability, and cost reduction in repeaterless quantum communication networks. However, identifying optimal switching configurations remains a significant challenge due to the combinatorial complexity. We introduce a novel graph formulation to model the physical and logical structure of repeaterless quantum networks, enabling the systematic optimization of switching strategies. The problem is posed as a linear program and solved using a column generation approach. This method enables scalable computation despite the exponential number of possible network configurations. Our results not only provide a formal foundation but also a practical algorithm for the optimization of switching. Empirical tests confirm the solver's scalability with network size, demonstrating the framework's effectiveness and laying the groundwork for future optimization of quantum network control.

Problem

• quantum networks
• switching optimization
• combinatorial complexity
• repeaterless communication
• resource allocation

Innovation

• column generation
• repeaterless quantum networks
• switching optimization
• graph formulation
• scalable computation