Nearest centroid classification on a trapped ion quantum computer

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