  Quantum machine learning is a growing research field that aims to perform
machine learning tasks assisted by a quantum computer. Kernel-based quantum
machine learning models are paradigmatic examples where the kernel involves
quantum states, and the Gram matrix is calculated from the overlap between
these states. With the kernel at hand, a regular machine learning model is used
for the learning process. In this paper we investigate the quantum support
vector machine and quantum kernel ridge models to predict the degree of
non-Markovianity of a quantum system. We perform digital quantum simulation of
amplitude damping and phase damping channels to create our quantum dataset. We
elaborate on different kernel functions to map the data and kernel circuits to
compute the overlap between quantum states. We show that our models deliver
accurate predictions that are comparable with the fully classical models.
