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

2018/06/28 by Makoto Negoro, Negoro, Makoto, Kosuke Mitarai +7 · 2 citations
Computer Science · Physics and Astronomy · #FOS: Physical sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing #Quantum Physics (quant-ph) #Spectroscopy and Quantum Chemical Studies

paper · pdf · doi:10.48550/arxiv.1806.10910

openalex publication_date 2018/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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