Classification with Quantum Machine Learning: A Survey

TL;DR

This survey reviews recent advances in Quantum Machine Learning, focusing on classification techniques, encoding methods, quantum subroutines, and applications, highlighting challenges and future directions in the field.

Contribution

It provides a comprehensive classification scheme, summarizes recent works, and discusses encoding methods, quantum subroutines, and applications in QML, offering a valuable overview of the field.

Findings

Summarized 30 recent publications in QML.

Proposed a classification scheme for quantum classification methods.

Discussed encoding techniques and quantum subroutines for improved performance.

Abstract

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented.