  The Gaussian kernel is a very popular kernel function used in many machine
learning algorithms, especially in support vector machines (SVMs). It is more
often used than polynomial kernels when learning from nonlinear datasets, and
is usually employed in formulating the classical SVM for nonlinear problems. In
[3], Rebentrost et al. discussed an elegant quantum version of a least square
support vector machine using quantum polynomial kernels, which is exponentially
faster than the classical counterpart. This paper demonstrates a quantum
version of the Gaussian kernel and analyzes its runtime complexity using the
quantum random access memory (QRAM) in the context of quantum SVM. Our analysis
shows that the runtime computational complexity of the quantum Gaussian kernel
seems to be significantly faster as compared to its classical version.
