Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

近年来，量子机器学习在理论和实践方面取得了长足的发展，并已成为寻找量子计算机在现实世界中的应用的一个有前途的领域。为了实现这一目标，我们在这里结合了最先进的算法和量子硬件，以提供量子机器学习应用程序的实验演示，并为其性能和效率提供可证明的保证。特别是，我们设计了一个量子最近质心分类器，使用将经典数据有效加载到量子态和执行距离估计的技术，并在 11 量子位捕获离子量子机上进行实验证明，与经典最近质心分类器的精度相匹配： MNIST 手写数字数据集，并对 8 维合成数据实现高达 100% 的准确率。