Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid

TL;DR

This paper demonstrates quantum machine learning using nuclear magnetic resonance (NMR) by implementing a quantum reservoir computing framework with a controllable nuclear spin ensemble, showing promising results for quantum information processing.

Contribution

It introduces a physical implementation of quantum reservoir computing using a nuclear spin ensemble in a solid, enabling learning of nonlinear functions with low errors.

Findings

Successful learning of nonlinear functions from binary and continuous inputs

Demonstration of quantum reservoir computing in NMR systems

Potential for quantum computational supremacy in NMR ensemble systems

Abstract

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.