Nearest centroid classification on a trapped ion quantum computer

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Johri, S; Debnath, S; Mocherla, A; Singk, A; Prakash, A; Kim, J; Kerenidis, I

Published Date

  • December 1, 2021

Published In

Volume / Issue

  • 7 / 1

Electronic International Standard Serial Number (EISSN)

  • 2056-6387

Digital Object Identifier (DOI)

  • 10.1038/s41534-021-00456-5

Citation Source

  • Scopus