  Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing.
Meanwhile, rapid progress has been made in the field of quantum computation
including developing both powerful quantum algorithms and advanced quantum
devices. The interplay between machine learning and quantum physics holds the
intriguing potential for bringing practical applications to the modern society.
Here, we focus on quantum neural networks in the form of parameterized quantum
circuits. We will mainly discuss different structures and encoding strategies
of quantum neural networks for supervised learning tasks, and benchmark their
performance utilizing Yao.jl, a quantum simulation package written in Julia
Language. The codes are efficient, aiming to provide convenience for beginners
in scientific works such as developing powerful variational quantum learning
models and assisting the corresponding experimental demonstrations.
