Openly accessible

Machine Learning: Quantum vs Classical

Khan, Tariq and Robles-Kelly, Antonio 2020, Machine Learning: Quantum vs Classical, IEEE Access, vol. 8, pp. 219275-219294, doi: 10.1109/access.2020.3041719.

Attached Files
Name Description MIMEType Size Downloads
khan-machinelearningquantumvs-2020.pdf Published version application/pdf 2.19MB 80

Title Machine Learning: Quantum vs Classical
Author(s) Khan, TariqORCID iD for Khan, Tariq
Robles-Kelly, AntonioORCID iD for Robles-Kelly, Antonio
Journal name IEEE Access
Volume number 8
Start page 219275
End page 219294
Total pages 20
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 2169-3536
Keyword(s) quantum machine learning
quantum computing
quantum algorithms
Science & Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Machine learning
Machine learning algorithms
Task analysis
Computational modeling
Summary Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.
Language eng
DOI 10.1109/access.2020.3041719
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution Non-Commercial No-Derivatives licence
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 73 Abstract Views, 80 File Downloads  -  Detailed Statistics
Created: Tue, 05 Jan 2021, 12:40:39 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact