محاسبات کوانتومی - Quantum-Computing
عنوان انگلیسی مقاله:
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
ترجمه فارسی عنوان مقاله:
کاربردهای یادگیری ماشین کوانتومی در حوزه زیست پزشکی: مرور سیستماتیک
ieee - ieee Access;2022;10; ;10:1109/ACCESS:2022:3195044
DANYAL MAHESHWARI 1, BEGONYA GARCIA-ZAPIRAIN 1, (Member, IEEE), AND DANIEL SIERRA-SOSA
Quantum 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
INDEX TERMS: Quantum computing | quantum machine learning | biomedical and healthcare.
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