Scalable Scheduling Policies for Quantum Satellite Networks

TL;DR

This paper addresses the challenge of scheduling in quantum satellite networks, proposing heuristic algorithms to optimize entanglement distribution, and demonstrating their effectiveness through simulations on large satellite constellations.

Contribution

It introduces scalable scheduling heuristics for quantum satellite networks and evaluates their performance, addressing the NP-hardness of the general scheduling problem.

Findings

Heuristic algorithms outperform existing methods in key metrics.

The number of receivers per ground station grows slowly with total ground stations.

Proposed algorithms improve entanglement distribution rate and fidelity.

Abstract

As Low Earth Orbit (LEO) satellite mega constellations continue to be deployed for satellite internet and recent successful experiments in satellite-based quantum entanglement distribution emerge, a natural question arises: How should we coordinate transmissions and design scalable scheduling policies for a quantum satellite internet? In this work, we consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We then propose four heuristic algorithms and evaluate their performance for Starlink mega constellation under various amount of resources and placements of the ground stations. We find that the maximum number of receivers necessary per ground station grows very slowly with the total number of deployed ground stations. Our proposed algorithms, leveraging optimal weighted b-matching and the global greedy heuristic, outperform others in entanglement distribution rate, entanglement fidelity, and handover cost metrics. While we develop these scheduling algorithms, we have also designed a software system to simulate, visualize, and evaluate satellite mega-constellations for entanglement distribution.