Recent advances for quantum classifiers
نویسندگان
چکیده
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 an emergent frontier machine learning. Along this line, classifiers, which are devices that aim solve classification problems learning, have attracted tremendous attention recently. In review, we give relatively comprehensive overview the studies focus on recent advances. First, will review number algorithms, including support vector machines, kernel methods, decision tree nearest neighbor annealing based classifiers. Then, move introduce variational essentially circuits classifications. We different architectures constructing classifiers barren plateau problem, where training might be hindered by exponentially vanishing gradient. addition, vulnerability aspect setting adversarial experimental progress also discussed.
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ژورنال
عنوان ژورنال: Science China Physics, Mechanics & Astronomy
سال: 2021
ISSN: ['1869-1927', '1674-7348']
DOI: https://doi.org/10.1007/s11433-021-1793-6