research-article

Quantum Machine Learning: Current State and Challenges

Authors:

Maria Avramouli,

Georgia Garani,

Anna VasilakiAuthors Info & Claims

PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics

Pages 397 - 402

https://doi.org/10.1145/3503823.3503896

Published: 22 February 2022 Publication History

Abstract

In recent years, machine learning has penetrated a large part of our daily lives, which creates special challenges and impressive progress in this area. Nevertheless, as the amount of daily data is grown, learning time is increased. Quantum machine learning (QML) may speed up the processing of information and provide great promise in machine learning. However, it is not used in practice yet, because quantum software and hardware challenges are still unsurmountable. This paper provides current research of quantum computing and quantum machine learning algorithms. Also, the quantum vendors, their frameworks, and their platforms are presented. A few fully implemented versions of quantum machine learning are presented, which are easier to be evaluated. Finally, QML's challenges, and problems are discussed.

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Cited By

Munshi MGupta RJadav NTanwar SNair AGarg D(2024)Quantum Machine Learning-based Lung Cancer Prediction Framework for Healthcare 4.02024 Asia Pacific Conference on Innovation in Technology (APCIT)10.1109/APCIT62007.2024.10673456(1-6)Online publication date: 26-Jul-2024
https://doi.org/10.1109/APCIT62007.2024.10673456
Kharsa RBouridane AAmira A(2023)Advances in Quantum Machine Learning and Deep Learning for Image ClassificationNeurocomputing10.1016/j.neucom.2023.126843560:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126843
Singh JBhangu K(2022)Contemporary Quantum Computing Use Cases: Taxonomy, Review and ChallengesArchives of Computational Methods in Engineering10.1007/s11831-022-09809-530:1(615-638)Online publication date: 26-Sep-2022
https://doi.org/10.1007/s11831-022-09809-5

Index Terms

Quantum Machine Learning: Current State and Challenges
1. Computing methodologies
  1. Machine learning
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics

November 2021

499 pages

ISBN:9781450395557

DOI:10.1145/3503823

Copyright © 2021 ACM.

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Publication History

Published: 22 February 2022

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PCI 2021

PCI 2021: 25th Pan-Hellenic Conference on Informatics

November 26 - 28, 2021

Volos, Greece

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Cited By

Munshi MGupta RJadav NTanwar SNair AGarg D(2024)Quantum Machine Learning-based Lung Cancer Prediction Framework for Healthcare 4.02024 Asia Pacific Conference on Innovation in Technology (APCIT)10.1109/APCIT62007.2024.10673456(1-6)Online publication date: 26-Jul-2024
https://doi.org/10.1109/APCIT62007.2024.10673456
Kharsa RBouridane AAmira A(2023)Advances in Quantum Machine Learning and Deep Learning for Image ClassificationNeurocomputing10.1016/j.neucom.2023.126843560:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126843
Singh JBhangu K(2022)Contemporary Quantum Computing Use Cases: Taxonomy, Review and ChallengesArchives of Computational Methods in Engineering10.1007/s11831-022-09809-530:1(615-638)Online publication date: 26-Sep-2022
https://doi.org/10.1007/s11831-022-09809-5

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