A Survey on Quantum Machine Learning: Current Trends, Challenges,
Opportunities, and the Road Ahead
- URL: http://arxiv.org/abs/2310.10315v1
- Date: Mon, 16 Oct 2023 11:52:54 GMT
- Title: A Survey on Quantum Machine Learning: Current Trends, Challenges,
Opportunities, and the Road Ahead
- Authors: Kamila Zaman and Alberto Marchisio and Muhammad Abdullah Hanif and
Muhammad Shafique
- Abstract summary: Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing.
QC is applied to Machine Learning (ML) applications, it forms a Quantum Machine Learning (QML) system.
We discuss different QML algorithms and their domain applicability, quantum datasets, hardware technologies, software tools, simulators, and applications.
- Score: 6.14975265413396
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum Computing (QC) claims to improve the efficiency of solving complex
problems, compared to classical computing. When QC is applied to Machine
Learning (ML) applications, it forms a Quantum Machine Learning (QML) system.
After discussing the basic concepts of QC and its advantages over classical
computing, this paper reviews the key aspects of QML in a comprehensive manner.
We discuss different QML algorithms and their domain applicability, quantum
datasets, hardware technologies, software tools, simulators, and applications.
In this survey, we provide valuable information and resources for readers to
jumpstart into the current state-of-the-art techniques in the QML field.
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