  We experimentally demonstrate quantum machine learning using NMR based on a
framework of quantum reservoir computing. Reservoir computing is for exploiting
natural nonlinear dynamics with large degrees of freedom, which is called a
reservoir, for a machine learning purpose. Here we propose a concrete physical
implementation of a quantum reservoir using controllable dynamics of a nuclear
spin ensemble in a molecular solid. In this implementation, we demonstrate
learning of nonlinear functions with binary or continuous variable inputs with
low mean squared errors. Our implementation and demonstration paves a road
toward exploiting quantum computational supremacy in NMR ensemble systems for
information processing with reachable technologies.
