QSVM中量子核Ansatz中特征嵌入位置的影响
The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs
AI总结：
本文研究了QSVM中量子核QEK的电路架构设计，发现现有架构表现与文献假设不符，并提出一种性能相当但门数更少的新架构。
摘要：
设计有用的量子核特征映射，是在试图获得优于经典机器学习模型优势时的关键任务。电路架构的选择，即如何将特征依赖门与其他门交织，是一个相对未探索的问题，且在使用名为Quantum Embedding Kernels (QEK)的量子核模型时变得非常重要。我们研究并分类了QEKs中的各种架构模式，并表明现有架构风格并不像文献所假设的那样运行。我们还基于旧架构提出了一种新颖的替代架构，并表明其性能同样出色，同时包含的门比旧架构更少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
