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Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
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本文对近期量子算法在动态卫星网络路由中的应用进行了评估,发现静态量子优化器和量子强化学习方法在解决此类问题上存在显著挑战,如优化复杂性和学习不稳定等,并提出了未来研究方向。

arXiv:2508.04288v1 Announce Type: cross Abstract: Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free simulations, we find that these algorithms face significant challenges. Specifically, static optimizers are unable to solve even a classically easy 4-node shortest path problem due to the complexity of the optimization landscape. Likewise, a basic QRL agent based on policy gradient methods fails to learn a useful routing strategy in a dynamic 8-node environment and performs no better than random actions. These negative findings highlight key obstacles that must be addressed before quantum algorithms can offer real advantages in communication networks. We discuss the underlying causes of these limitations, including barren plateaus and learning instability, and suggest future research directions to overcome them.

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量子算法 卫星网络 路由优化 量子计算 强化学习
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