  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.
