Abstract
AbstractThe kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spaces in those experiments have been smaller than the number of data, which makes them lose their computational advantage over explicit method. Here we show the first experimental demonstration of a quantum kernel machine that achieves a scheme where the dimension of feature space greatly exceeds the number of data using 1H nuclear spins in solid. The use of NMR allows us to obtain the kernel values with single-shot experiment. We employ engineered dynamics correlating 25 spins which is equivalent to using a feature space with a dimension over 1015. This work presents a quantum machine learning using one of the largest quantum systems to date.
Funder
MEXT | Japan Society for the Promotion of Science
MEXT | JST | Precursory Research for Embryonic Science and Technology
MEXT | JST | Exploratory Research for Advanced Technology
MEXT | JST | Core Research for Evolutional Science and Technology
Ministry of Education, Culture, Sports, Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Computer Networks and Communications,Statistical and Nonlinear Physics,Computer Science (miscellaneous)
Cited by
23 articles.
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