迈向多波束数字卫星的量子优化流量调度
Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites
AI总结：
本文提出混合量子经典框架，将多波束卫星调度问题转化为QUBO问题，通过分层训练策略提升调度效率与鲁棒性。
摘要：
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，常常面临解决方案质量与实时可行性之间的权衡。在本文中，我们提出了一种混合量子经典框架，通过将多波束时频槽分配（MB-TFSA）转换为二次无约束二元优化（QUBO）问题来提高调度效率。我们通过参数重新缩放将吞吐量最大化目标和操作约束纳入紧凑的QUBO中，以保持公式的可处理性。为了解决变分量子算法中的优化挑战，如 barren plateaus 和 rugged loss landscapes，我们引入了分层训练策略，逐步增加电路深度，同时迭代优化解决方案。我们在量子硬件上评估了解决方案质量、运行时间和鲁棒性，并使用现实的模拟卫星交通工作负载对经典和混合基线进行基准测试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
