Noise-enhanced quantum kernels on analog quantum computers

Abstract

The quantum kernel method, a promising quantum machine learning algorithm, possesses substantial potential for demonstrating quantum advantage. Although the majority of the quantum kernel is constructed in the context of gate-based quantum circuits, inspired by the idea of analog quantum computing, here we construct an analog quantum kernel and a hybrid quantum kernel, and show their competitiveness against other kernel methods in a benchmarking task and the practical problem of estimating non-Markovianity from sparse data. Additionally, we also incorporate operational noise into the quantum kernels. Our results reveal that the presence of operational noise can be beneficial to the performance of the developed quantum kernels. We attribute this counterintuitive noise-enhanced performance to the improved expressivity and higher model complexity induced by noise. These results pave the way for practical implementations of quantum kernel methods and provide an efficient approach for estimating non-Markovianity with reduced experimental demands.