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.

翻译：高吞吐量多波束卫星的数据流调度是一个具有挑战性的NP难组合优化问题。随着问题规模的扩大，传统方法（如混合整数线性规划和启发式调度器）往往需要在解的质量与实时可行性之间进行权衡。本文提出一种混合量子-经典框架，通过将多波束时频隙分配问题建模为二次无约束二进制优化问题，以提高调度效率。我们通过参数重缩放将吞吐量最大化目标与操作约束整合为紧凑的QUBO形式，以保持模型的可处理性。针对变分量子算法中的优化挑战（如贫瘠高原和崎岖损失景观），我们引入一种分层训练策略，在逐步增加电路深度的同时迭代优化解。我们在量子硬件上评估了解的质量、运行时间和鲁棒性，并使用模拟的真实卫星流量工作负载，与经典及混合基线方法进行了性能对比。