A Comprehensive Survey on Quantum Machine Learning and Possible Applications

A Comprehensive Survey on Quantum Machine Learning and Possible Applications

Muhammad Junaid Umer, Muhammad Imran Sharif
Copyright: © 2022 |Pages: 17
DOI: 10.4018/IJEHMC.315730
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Abstract

Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
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1. Introduction

Due to the improvement of computational ability ML has become a very important field to automatically analyze different tasks. A machine learning algorithm can analyze a large amount of data in terms of making an intelligent decision based on training data. The learning process in machine learning is mainly divided into three categories unsupervised, supervised, and re-enforcement learning. The first two categories are mainly utilized for data mining and data analysis tasks while re-enforcement learning is for interactive tasks where learning increased at every step. In the last two decades, with advancements in technology, computing power has increased rapidly, and new algorithms have come up continuously. Different studies have been proposed in which different classical ML and deep learning techniques are utilized in the literature (Akbar et al., 2017; Akhtar et al., 2020; Akram et al., 2018; Amin et al., 2016a, 2016b; Amin, Sharif, Rehman, et al., 2018; Amin, Sharif, Yasmin, Saba, Anjum, & Fernandes, 2019b; Sharif et al., 2017; Sharif, Khan, et al., 2018; Sharif et al., 2019, 2020; Sharif & Shah, 2019). Machine learning is being applied at a large scale in medical diagnosis for example brain tumor detection with classical methods are presented in (Amin, Sharif, Gul, Raza, Anjum, Nisar, et al., 2019; Amin, Sharif, Raza, et al., 2018; Amin, Sharif, Raza, Saba, & Anjum, 2019; Amin, Sharif, Raza, Saba, & Rehman, 2019; Amin, Sharif, Yasmin, et al., 2018; Amin, Sharif, Yasmin, Saba, Anjum, & Fernandes, 2019a, 2019b; Khan et al., 2019; Masood et al., 2013; Raza et al., 2012; Saba et al., 2020; Sharif, Tanvir, et al., 2018; Yasmin, Mohsin, et al., 2012; Yasmin, Sharif, et al., 2012a, 2012b). Breast cancer detection by using the classical ML and deep learning are presented in (Mughal et al., 2017, 2018, 2019; Mughal & Sharif, 2017; Yasmin et al., 2013). Different studies in which classical computing is utilized for solution of different tasks are presented in (Amin, Sharif, Yasmin, Ali, et al., 2017; Amin, Sharif, Yasmin, & Fernandes, 2017; Arunkumar et al., 2017; S. L. Fernandes et al., 2017; Raja et al., 2018; Rajinikanth et al., 2017). The data growth is also increasing at a much higher rate as compared to the computer’s performance. So, in the area of classical ML computing power is reducing due to the high complexity of big data.

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