Quantum machine learning applications in the biomedical domain: a systematic review

Vista sencilla de ítem
dc.contributor.authorMaheshwari, Danyal
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorSierra-Sosa, Daniel
dc.date.accessioned2024-11-14T11:37:27Z
dc.date.available2024-11-14T11:37:27Z
dc.date.issued2022
dc.date.updated2024-11-14T11:37:27Z
dc.description.abstractQuantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a challenging field in the case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models and quantum circuits using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.en
dc.description.sponsorshipThis work was supported by the eVIDA Research Group, University of Deusto, Bilbao, Spain, through the Basque Government, under Grant IT-1536-22en
dc.identifier.citationMaheshwari, D., Garcia-Zapirain, B., & Sierra-Sosa, D. (2022). Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review. IEEE Access, 10, 80463-80484. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3195044
dc.identifier.doi10.1109/ACCESS.2022.3195044
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1858
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherBiomedical and healthcare
dc.subject.otherQuantum computing
dc.subject.otherQuantum machine learning
dc.titleQuantum machine learning applications in the biomedical domain: a systematic reviewen
dc.typereview article
dcterms.accessRightsopen access
oaire.citation.endPage80484
oaire.citation.startPage80463
oaire.citation.titleIEEE Access
oaire.citation.volume10
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Ficheros en el ítem
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
maheshwari_quantum_2022.pdf
Tamaño:
4.31 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos