发表机构
Instituto de Pesquisas Eldorado; Universidade Federal de Santa Catarina; Universidade Federal do Rio Grande do Sul（Eldorado研究所; 圣卡塔琳娜联邦大学; 南里奥格兰德联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探究用小型量子神经网络替代基于注意力的路由模型的参数密集型模块，发现编码器前馈替换可减少56.6%参数，是可行的混合模块压缩策略，未体现量子优势。
AI 中文摘要
本研究探讨用于学习路由启发式算法的混合量子-经典神经网络。具体而言，本文探究小型量子神经网络能否替代具有竞争力的基于注意力的路由模型内部参数密集型模块，同时保持解的质量。对于带容量约束的车辆路径问题，编码器前馈替换成为最具前景的设计：它将模型参数数量减少56.6%，在中小规模实例下，混合模型与经典神经网络基线的性能接近，尽管在更大规模实例下差距会扩大。本文还与经典路由算法进行对比，经典算法仍具有很强竞争力，且在固定欧氏测试集上往往更优。因此，本文的结果未表明存在量子优势或求解器主导地位，但确定编码器前馈替换是神经组合优化中可行的混合模块压缩策略。
英文摘要
This work studies hybrid quantum-classical neural networks for learning routing heuristics. Specifically, this paper asks whether small quantum neural networks can replace parameter-heavy modules inside a competitive attention-based routing model while maintaining solution quality. For the capacitated vehicle routing problem, encoder feed-forward replacement emerges as the most promising design: it reduces the number of model parameters by 56.6% while keeping the hybrid model close to the classical neural baseline at small and medium instance sizes, although the gap grows for larger instances. This work also compares to classical routing algorithms, which remain highly competitive and often superior on the fixed Euclidean test sets. Our results therefore do not indicate quantum advantage or solver dominance, but identify encoder feed-forward replacement as a viable hybrid-module compression strategy for neural combinatorial optimization.
Comments5 pages. Submitted to the Congresso Brasileiro de Ciências e Tecnologias Quânticas (CBCTQ 2026)
