Nearest centroid classification on a trapped ion quantum computer

• Main Authors: Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros SINGK, Anupam Prakash, Jungsang Kim, Iordanis Kerenidis
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-08-01
• Series: npj Quantum Information
• Online Access: https://doi.org/10.1038/s41534-021-00456-5

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.