  Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented
perspectives for both fundamental research and commercial applications, giving
rise to an emergent research frontier of quantum machine learning. Along this
line, quantum classifiers, which are quantum devices that aim to solve
classification problems in machine learning, have attracted tremendous
attention recently. In this review, we give a relatively comprehensive overview
for the studies of quantum classifiers, with a focus on recent advances. First,
we will review a number of quantum classification algorithms, including quantum
support vector machines, quantum kernel methods, quantum decision tree
classifiers, quantum nearest neighbor algorithms, and quantum annealing based
classifiers. Then, we move on to introduce the variational quantum classifiers,
which are essentially variational quantum circuits for classifications. We will
review different architectures for constructing variational quantum classifiers
and introduce the barren plateau problem, where the training of quantum
classifiers might be hindered by the exponentially vanishing gradient. In
addition, the vulnerability aspect of quantum classifiers in the setting of
adversarial learning and the recent experimental progress on different quantum
classifiers will also be discussed.
