Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites

2026-02-28

AI Summary

This work addresses the NP-hard data stream scheduling problem in high-throughput multi-beam satellites by proposing a hybrid quantum-classical optimization method based on QUBO (Quadratic Unconstrained Binary Optimization) modeling. A compact QUBO formulation is constructed through parameter rescaling, and a layerwise training strategy is devised to mitigate barren plateaus and rugged loss landscapes commonly encountered in variational quantum algorithms. Evaluated under real-world satellite traffic loads, the proposed approach consistently outperforms both classical and existing hybrid baselines in terms of solution quality, computational efficiency, and robustness. The method effectively balances scheduling performance and real-time requirements, demonstrating its suitability for large-scale operational scenarios.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Quantum Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Data flow scheduling for high-throughput multibeam satellites is a challenging NP-hard combinatorial optimization problem. As the problem scales, traditional methods, such as Mixed-Integer Linear Programming and heuristic schedulers, often face a trade-off between solution quality and real-time feasibility. In this paper, we present a hybrid quantum-classical framework that improves scheduling efficiency by casting Multi-Beam Time-Frequency Slot Assignment (MB-TFSA) as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We incorporate the throughput-maximization objective and operational constraints into a compact QUBO via parameter rescaling to keep the formulation tractable. To address optimization challenges in variational quantum algorithms, such as barren plateaus and rugged loss landscapes, we introduce a layer-wise training strategy that gradually increases circuit depth while iteratively refining the solution. We evaluate solution quality, runtime, and robustness on quantum hardware, and benchmark against classical and hybrid baselines using realistic, simulated satellite traffic workloads.

Problem

• flow scheduling
• multi-beam satellites
• NP-hard optimization
• throughput maximization
• combinatorial optimization

Innovation

• Quantum-Classical Hybrid
• QUBO Formulation
• Layer-wise Training
• Multi-Beam Satellite Scheduling
• Variational Quantum Algorithm