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.

内核技巧允许我们在不显式存储特征的情况下为机器学习任务使用高维特征空间。最近，利用量子系统利用干涉计算核函数的想法已经通过实验得到证实。然而，这些实验中特征空间的维度一直小于数据的数量，这使得它们失去了与显式方法相比的计算优势。在这里，我们展示了量子核机的第一个实验演示，该机实现了特征空间的维度大大超过使用¹H 核在固体中自旋。NMR 的使用使我们能够通过单次实验获得内核值。我们采用与 25 次自旋相关的工程动力学，这相当于使用维度超过 10 ¹⁵的特征空间。这项工作展示了使用迄今为止最大的量子系统之一的量子机器学习。