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.

本文主要讨论了量子神经网络（Quantum Neural Networks）在监督学习任务中的不同结构和编码策略，并使用 Julia 语言编写的量子模拟器 Yao.jl 来衡量它们的性能，以方便科学家们开发出强大的变分量子学习模型及相应的实验验证。