Abstract
The 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.
Highlights
Quantum machine learning is an emerging field that has attracted much attention recently
Their performance is limited compared to the fault-tolerant quantum computer, simulation of the noisy intermediate-scale quantum (NISQ) devices with 100 qubits and sufficiently high gate fidelity are beyond the reach for the existing supercomputer and classical simulation algorithms[5,6,7]
In the one-dimensional regression task, we observed the trend of better performance with longer evolution time
Summary
Quantum machine learning is an emerging field that has attracted much attention recently. The major algorithmic breakthrough was an algorithm invented by Harrow–Hassidim-Lloyd[1] This algorithm has been further developed to more sophisticated machine learning algorithms[2,3]. Noisy intermediate-scale quantum (NISQ) devices[4], which consist of several tens or hundreds of noisy qubits, are the most advanced technology Their performance is limited compared to the fault-tolerant quantum computer, simulation of the NISQ devices with 100 qubits and sufficiently high gate fidelity are beyond the reach for the existing supercomputer and classical simulation algorithms[5,6,7]. This fact motivates us to explore its power for solving practical problems
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