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

TL;DR

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 and introduces a layer-wise training strategy that gradually increases circuit depth while iteratively refining the solution.

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.

Figures (6)

• Figure 1: Multi-beam satellite system.
• Figure 2: One-layer QAOA solver overview (the components in between the dashed line can be replicated to add more layers).
• Figure 3: QAOA solution distribution vs. hamming distance.
• Figure 4: Throughput vs. hamming distance.
• Figure 5: Energy vs. SPSA iterations.