On the expressivity of embedding quantum kernels

arXiv:2309.14419v2 Announce Type: replace-cross
Abstract: One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quantum kernels are typically evaluated by explicitly constructing quantum feature states and then taking their inner product, here called embedding quantum kernels. Since classical kernels are usually evaluated without using the feature vectors explicitly, we wonder how …

abstract arxiv context cs.lg embedding feature kernel machine machine learning natural products quant-ph quantum quantum kernels spaces stat.ml type vectors