Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
About this item
Full title
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
Subjects
More information
Scope and Contents
Contents
Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has received a lot of interest recently for image classification. The performance of VQC is jeopardized by the noise in Noisy Intermediate-Scale Quantum (NISQ) computers, which...
Alternative Titles
Full title
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2721475529
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2721475529
Other Identifiers
E-ISSN
2331-8422
