*Result*: 量子算法在网络路由优化中的发展与挑战.

*In large-scale heterogeneous communication networks, routing optimization involves complex demands such as high-dimensional state spaces, multiple constraints, and real-time dynamics. A quantum-classical hybrid computing paradigm, leveraging quantum superposition and entanglement, offers a promising pathway to efficiently address NP-hard routing problems. By analyzing representative routing scenarios, a modeling framework for quantum algorithms was outlined, and key technical obstacles in applying quantum approaches to network routing were systematically summarized. These include conflicts between structural constraints and model expressiveness, biases in multi-objective formulations and delayed responses, the trade-off between diversity and convergence in high-dimensional solution spaces, and limited generalization capability under dynamic conditions. To overcome these issues, several directions were proposed: algorithmic expressiveness was enhanced through improved structural modeling, evolutionary design, and integrated scheduling strategies; computational accuracy and responsiveness were improved via multi-objective optimization, modular decoupling, and tight quantum-classical interaction; convergence and diversity were balanced through hybrid architectural design, evolutionary policy adaptation, and structural constraint management; and adaptability and generalization in dynamic environments were boosted by incorporating meta-learning, task decomposition, and structural transfer techniques. Furthermore, the co-development of quantum algorithms and hardware architectures were advocated, paving the way for practical deployment of quantum computing in communication networks within the NISQ era. [ABSTRACT FROM AUTHOR]*

*在大规模异构通信网络中，路由优化问题涵盖高维状态空间、多重约束条件、动态实时性等复杂需求。以量子-经典混合计算为核心的新型范式，依托量子叠加与纠缠效应，为NP难路由问题提供了高效的潜在求解方案。梳理典型路由问题的量子算法建模框架，系统性归纳当前量子算法在网络路由优化中面临的核心挑战，包括结构性约束与算法建模表达冲突、多目标建模偏差与响应滞后、高维空间中多样性与收敛性矛盾以及动态环境下策略泛化能力不足等。为此，提出结构建模优化、演化设计与调度机制协同，增强量子算法的表达能力；结合多目标优化、模块解耦与量子-经典协作，提高计算精度与响应速度；基于混合架构构建、策略演化设计与结构约束控制，平衡收敛性与解空间多样性；引入元学习、任务分解与结构迁移方法，提升量子算法在动态网络中的适应性和泛化能力等发展方向。进一步地，推动算法设计与硬件架构的协同发展，为含噪中等规模量子（noisy intermediate-scale quantum，NISQ）时代量子计算在通信网络中的实用化部署提供理论支撑与技术实现路径。 [ABSTRACT FROM AUTHOR]

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