Bibliographic Details
| Title: |
Nearest centroid classification on a trapped ion quantum computer |
| Authors: |
Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros SINGK, Anupam Prakash, Jungsang Kim, Iordanis Kerenidis |
| Source: |
npj Quantum Information, Vol 7, Iss 1, Pp 1-11 (2021) |
| Publisher Information: |
Nature Portfolio, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Physics
LCC:Electronic computers. Computer science |
| Subject Terms: |
Physics, QC1-999, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2056-6387 |
| Relation: |
https://doaj.org/toc/2056-6387 |
| DOI: |
10.1038/s41534-021-00456-5 |
| Access URL: |
https://doaj.org/article/7ac446645ae74a218aaee8d6c4706aec |
| Accession Number: |
edsdoj.7ac446645ae74a218aaee8d6c4706aec |
| Database: |
Directory of Open Access Journals |
