A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm

Quantum ML is a very fast-growing area of research with much theoretical variety & applications. For picture identification, machine learning algorithms learn a desired input–output relationship. Researchers looked into quantum computing technologies in the past few of years to see whether they...

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Bibliographic Details
Published in:Materials today : proceedings Vol. 56; pp. 2025 - 2030
Main Authors: Rana, Anurag, Vaidya, Pankaj, Gupta, Gaurav
Format: Journal Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Pattern Recognition
Quantum Computing
Quantum ML
Quantum SVM
SVM
Quantum Computing
Pattern Recognition
Quantum SVM
Quantum ML
SVM
ISSN:2214-7853, 2214-7853
Online Access:Get full text
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Summary:Quantum ML is a very fast-growing area of research with much theoretical variety & applications. For picture identification, machine learning algorithms learn a desired input–output relationship. Researchers looked into quantum computing technologies in the past few of years to see whether they may help improve traditional ML algorithms. Quantum computing explores the existing approaches of quantum ML. In this article the SVM in a quantum environment gave results accurate and in a speedup manner. The major observations are that the machine recognized the handwritten letters/characters across whole instances. This comparative study offers the comparative analysis as well as the technical scopes in a unified manner and addresses the future of quantum learning theory.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2021.11.350