Nearest Centroid Classification on a Trapped Ion Quantum Computer

TL;DR

This paper demonstrates a quantum Nearest Centroid classifier on a trapped-ion quantum computer, matching classical accuracy on MNIST and achieving perfect accuracy on synthetic data, showcasing practical quantum machine learning capabilities.

Contribution

It introduces a quantum Nearest Centroid classifier with efficient data loading and distance estimation, experimentally validated on a real quantum device.

Findings

Achieved classical-level accuracy on MNIST dataset

Reached 100% accuracy on 8-dimensional synthetic data

Demonstrated practical quantum machine learning application

Abstract

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.