Nearest centroid classification on a trapped ion quantum computer

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 provid...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros SINGK, Anupam Prakash, Jungsang Kim, Iordanis Kerenidis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/7ac446645ae74a218aaee8d6c4706aec
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7ac446645ae74a218aaee8d6c4706aec
record_format	dspace
spelling	oai:doaj.org-article:7ac446645ae74a218aaee8d6c4706aec2021-12-02T18:49:16ZNearest centroid classification on a trapped ion quantum computer10.1038/s41534-021-00456-52056-6387https://doaj.org/article/7ac446645ae74a218aaee8d6c4706aec2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00456-5https://doaj.org/toc/2056-6387Abstract 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.Sonika JohriShantanu DebnathAvinash MocherlaAlexandros SINGKAnupam PrakashJungsang KimIordanis KerenidisNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Sonika Johri Shantanu Debnath Avinash Mocherla Alexandros SINGK Anupam Prakash Jungsang Kim Iordanis Kerenidis Nearest centroid classification on a trapped ion quantum computer
description	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.
format	article
author	Sonika Johri Shantanu Debnath Avinash Mocherla Alexandros SINGK Anupam Prakash Jungsang Kim Iordanis Kerenidis
author_facet	Sonika Johri Shantanu Debnath Avinash Mocherla Alexandros SINGK Anupam Prakash Jungsang Kim Iordanis Kerenidis
author_sort	Sonika Johri
title	Nearest centroid classification on a trapped ion quantum computer
title_short	Nearest centroid classification on a trapped ion quantum computer
title_full	Nearest centroid classification on a trapped ion quantum computer
title_fullStr	Nearest centroid classification on a trapped ion quantum computer
title_full_unstemmed	Nearest centroid classification on a trapped ion quantum computer
title_sort	nearest centroid classification on a trapped ion quantum computer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7ac446645ae74a218aaee8d6c4706aec
work_keys_str_mv	AT sonikajohri nearestcentroidclassificationonatrappedionquantumcomputer AT shantanudebnath nearestcentroidclassificationonatrappedionquantumcomputer AT avinashmocherla nearestcentroidclassificationonatrappedionquantumcomputer AT alexandrossingk nearestcentroidclassificationonatrappedionquantumcomputer AT anupamprakash nearestcentroidclassificationonatrappedionquantumcomputer AT jungsangkim nearestcentroidclassificationonatrappedionquantumcomputer AT iordaniskerenidis nearestcentroidclassificationonatrappedionquantumcomputer
_version_	1718377568354172928

Nearest centroid classification on a trapped ion quantum computer

Ejemplares similares