Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis

The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum t...

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Autores principales:	Vashisth Shubham, Dhall Ishika, Aggarwal Garima
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	quantum machine learning supervised machine learning support vector machines quantum support vector machines breast cancer classification Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/51d9fb0e20b34212b528a359a9c3deb5
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id	oai:doaj.org-article:51d9fb0e20b34212b528a359a9c3deb5
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spelling	oai:doaj.org-article:51d9fb0e20b34212b528a359a9c3deb52021-12-05T14:10:51ZDesign and analysis of quantum powered support vector machines for malignant breast cancer diagnosis2191-026X10.1515/jisys-2020-0089https://doaj.org/article/51d9fb0e20b34212b528a359a9c3deb52021-09-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0089https://doaj.org/toc/2191-026XThe rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants.Vashisth ShubhamDhall IshikaAggarwal GarimaDe Gruyterarticlequantum machine learningsupervised machine learningsupport vector machinesquantum support vector machinesbreast cancer classificationScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 998-1013 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	quantum machine learning supervised machine learning support vector machines quantum support vector machines breast cancer classification Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	quantum machine learning supervised machine learning support vector machines quantum support vector machines breast cancer classification Science Q Electronic computers. Computer science QA75.5-76.95 Vashisth Shubham Dhall Ishika Aggarwal Garima Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
description	The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants.
format	article
author	Vashisth Shubham Dhall Ishika Aggarwal Garima
author_facet	Vashisth Shubham Dhall Ishika Aggarwal Garima
author_sort	Vashisth Shubham
title	Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
title_short	Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
title_full	Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
title_fullStr	Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
title_full_unstemmed	Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
title_sort	design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/51d9fb0e20b34212b528a359a9c3deb5
work_keys_str_mv	AT vashisthshubham designandanalysisofquantumpoweredsupportvectormachinesformalignantbreastcancerdiagnosis AT dhallishika designandanalysisofquantumpoweredsupportvectormachinesformalignantbreastcancerdiagnosis AT aggarwalgarima designandanalysisofquantumpoweredsupportvectormachinesformalignantbreastcancerdiagnosis
_version_	1718371666223955968

Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis

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