Recent advances for quantum classifiers

TL;DR

This review summarizes recent progress in quantum classifiers, highlighting algorithms, architectures, challenges like barren plateaus, and experimental developments in quantum machine learning applications.

Contribution

It provides a comprehensive overview of recent advances in quantum classifiers, including algorithms, architectures, and experimental progress, with insights into current challenges.

Findings

Quantum classifiers include support vector machines, kernel methods, decision trees, nearest neighbor, and annealing-based algorithms.

Variational quantum classifiers use quantum circuits and face challenges like barren plateaus.

Recent experiments demonstrate practical progress in implementing quantum classifiers.

Abstract

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.