Optimizing Orbital Parameters of Satellites for a Global Quantum Network

2026-03-02

AI Summary

This study addresses the optimization of orbital parameters for satellite constellations to maximize the global quantum entanglement generation rate between ground stations. For the first time, Bayesian optimization (BO) and genetic algorithms (GA) are systematically integrated into the design of quantum satellite constellations, combining the rapid convergence of BO with the robust global search capability of GA. Experimental results demonstrate that the proposed approach significantly enhances entanglement distribution efficiency compared to baseline methods that optimize only for ground coverage. Specifically, BO achieves faster convergence, while GA more effectively avoids local optima. This work establishes a novel paradigm for the design of high-performance global quantum networks.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Due to fundamental limitations on terrestrial quantum links, satellites have received considerable attention for their potential as entanglement generation sources in a global quantum internet. In this work, we focus on the problem of designing a constellation of satellites for such a quantum network. We find satellite inclination angles and satellite cluster allocations to achieve maximal entanglement generation rates to fixed sets of globally distributed ground stations. Exploring two black-box optimization frameworks: a Bayesian Optimization (BO) approach and a Genetic Algorithm (GA) approach, we find comparable results, indicating their effectiveness for this optimization task. While GA and BO often perform remarkably similar, BO often converges more efficiently, while later growth noted in GAs is indicative of less susceptibility towards local maxima. In either case, they offer substantial improvements over naive approaches that maximize coverage with respect to ground station placement.

Problem

• satellite constellation
• quantum network
• entanglement generation
• orbital parameters
• global quantum internet

Innovation

• satellite constellation optimization
• quantum network
• entanglement distribution
• Bayesian optimization
• genetic algorithm