Recent advances for quantum classifiers. (arXiv:2108.13421v1 [quant-ph])

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 machine, quantum kernel methods, quantum decision tree, and
quantum nearest neighbor algorithm. 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.



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