Nearest Centroid Classification on a Trapped Ion Quantum Computer

• URL: http://arxiv.org/abs/2012.04145v2
• Date: Wed, 9 Dec 2020 23:56:38 GMT
• Title: Nearest Centroid Classification on a Trapped Ion Quantum Computer
• Authors: Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros Singh, Anupam Prakash, Jungsang Kim and Iordanis Kerenidis
• Abstract summary: We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We 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.
• Score: 57.5195654107363
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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