Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision. (15th May 2022)

• Record Type: Journal Article
• Authors: Houssein, Essam H.; Abohashima, Zainab; Elhoseny, Mohamed; Mohamed, Waleed M.
• Is Part Of: Expert systems with applications. Volume 194(2022)
• Publication Date: 2022-05-15
• Subjects: Quantum machine learning -- Quantum computing -- Quantum deep learning -- Quantum inspired -- Hybrid quantum–classical -- Quantum classification -- Variational quantum algorithms; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116512
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Machine learning has become a ubiquitous and effective technique for data processing and classification. Furthermore, due to the superiority and progress of quantum computing in many areas (e.g., cryptography, machine learning, healthcare), a combination of classical machine learning and quantum information processing has established a new field, called, quantum machine learning. One of the most frequently used applications of quantum computing is machine learning. This paper aims to present a comprehensive review of state-of-the-art advances in quantum machine learning. Besides, this paper outlines recent works on different architectures of quantum deep learning, and illustrates classification tasks in the quantum domain as well as encoding methods and quantum subroutines. Furthermore, this paper examines how the concept of quantum computing enhances classical machine learning. Two methods for improving the performance of classical machine learning are presented. Finally, this work provides a general review of challenges and the future vision of quantum machine learning. Highlights: Organize the most recent research works to pave the way for QML researchers. Demonstrate the commonly used methods in the classification of real problems. Provide readers with various quantum methods to enhance classical ML. Present some of the challenges and future directions of QML.