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.
@article{arxiv.1806.10910,
title = {Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid},
author = {Makoto Negoro and Kosuke Mitarai and Keisuke Fujii and Kohei Nakajima and Masahiro Kitagawa},
journal= {arXiv preprint arXiv:1806.10910},
year = {2018}
}