In \cite{simon2023algorithms} we introduced four algorithms for the training
of neural support vector machines (NSVMs) and demonstrated their feasibility.
In this note we introduce neural quantum support vector machines, that is,
NSVMs with a quantum kernel, and extend our results to this setting.

我们引入了四种用于训练神经支持向量机的算法，并证明了它们的可行性。在本文章中，我们引入了神经量子支持向量机，即使用量子核的 NSVM，并将我们的结果扩展到该设置中。

神经量子支持向量机

On Neural Quantum Support Vector Machines

Neural support vector machines (NSVMs) allow for the incorporation of domain
knowledge in the design of the model architecture. In this article we introduce
a set of training algorithms for NSVMs that leverage the Pegasos algorithm and
provide a proof of concept by solving a set of standard machine learning tasks.

通过引入域知识，使用 Pegasos 算法为 NSVMs 提供一组训练算法，并通过解决一组标准机器学习任务展示其概念验证。