With the overwhelming success in the field of quantum information in the last
decades, the "quest" for a Quantum Neural Network (QNN) model began in order to
combine quantum computing with the striking properties of neural computing.
This article presents a systematic approach to QNN research, which so far
consists of a conglomeration of ideas and proposals. It outlines the challenge
of combining the nonlinear, dissipative dynamics of neural computing and the
linear, unitary dynamics of quantum computing. It establishes requirements for
a meaningful QNN and reviews existing literature against these requirements. It
is found that none of the proposals for a potential QNN model fully exploits
both the advantages of quantum physics and computing in neural networks. An
outlook on possible ways forward is given, emphasizing the idea of Open Quantum
Neural Networks based on dissipative quantum computing.

本文提出了一种系统方法，对量子神经网络的研究进行了综述和总结，旨在将量子计算与神经计算的突出特性相结合，但是目前为止并没有一个潜在的 QNN 模型能够完全利用量子物理和计算在神经网络中的优势，未来的发展方向可能是基于耗散量子计算的开放量子神经网络。