Machine Learning: Quantum vs Classical
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 mach...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9274431/ |
| _version_ | 1818910284153094144 |
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| author | Tariq M. Khan Antonio Robles-Kelly |
| author_facet | Tariq M. Khan Antonio Robles-Kelly |
| author_sort | Tariq M. Khan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T22:40:21Z |
| format | Article |
| id | doaj.art-e466b320dbd740c9be0e622c6deb9f73 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:40:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e466b320dbd740c9be0e622c6deb9f732022-12-21T20:03:05ZengIEEEIEEE Access2169-35362020-01-01821927521929410.1109/ACCESS.2020.30417199274431Machine Learning: Quantum vs ClassicalTariq M. Khan0https://orcid.org/0000-0002-7477-1591Antonio Robles-Kelly1https://orcid.org/0000-0002-2465-5971School of Information Technology, Deakin University, Geelong, VIC, AustraliaSchool of Information Technology, Deakin University, Geelong, VIC, AustraliaEncouraged 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.https://ieeexplore.ieee.org/document/9274431/Quantum machine learningquantum computingquantum algorithmsQuBit |
| spellingShingle | Tariq M. Khan Antonio Robles-Kelly Machine Learning: Quantum vs Classical IEEE Access Quantum machine learning quantum computing quantum algorithms QuBit |
| title | Machine Learning: Quantum vs Classical |
| title_full | Machine Learning: Quantum vs Classical |
| title_fullStr | Machine Learning: Quantum vs Classical |
| title_full_unstemmed | Machine Learning: Quantum vs Classical |
| title_short | Machine Learning: Quantum vs Classical |
| title_sort | machine learning quantum vs classical |
| topic | Quantum machine learning quantum computing quantum algorithms QuBit |
| url | https://ieeexplore.ieee.org/document/9274431/ |
| work_keys_str_mv | AT tariqmkhan machinelearningquantumvsclassical AT antoniorobleskelly machinelearningquantumvsclassical |