量子支持向量机中特征嵌入位置影响模型性能，新架构更高效
The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs
- 系统分析量子核电路中特征嵌入的布局模式
- 发现现有架构表现不符文献预期，存在冗余门结构
- 提出新型轻量化架构，性能相当但门数更少
在量子支持向量机（QSVM）中设计有效的特征映射是实现超越经典机器学习的关键。选择电路架构——即如何交织特征依赖门与其他门——是一个相对未被充分探索的问题，尤其在量子嵌入核（QEK）模型中尤为重要。本文系统研究并分类了QEK中的多种架构模式，发现现有架构的表现并不如文献所声称。在此基础上，我们提出一种基于旧架构的新型替代方案，其性能与原有架构相当，但使用的门数量更少。
原文摘要 · Abstract (English)
Designing a useful feature map for a quantum kernel is a critical task when attempting to achieve an advantage over classical machine learning models. The choice of circuit architecture, i.e. how feature-dependent gates should be interwoven with other gates is a relatively unexplored problem and becomes very important when using a model of quantum kernels called Quantum Embedding Kernels (QEK). We study and categorize various architectural patterns in QEKs and show that existing architectural styles do not behave as the literature supposes. We also produce a novel alternative architecture based on the old ones and show that it performs equally well while containing fewer gates than its older counterparts.
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