Quantum computing is a new mode that follows the laws of quantum mechanics. It performs computational tasks based on the control of quantum units. From the view of computable problems, quantum computers can only solve the problems that traditional computers can solve, but considering the efficiency of computation, due to the existence of quantum superposition, some known quantum algorithms are exponentially faster than traditional general-purpose computers. In this article, we combine quantum computing with a classical neural network to design a quantum Hopfield network. Each neuron is initialized to a specified state and evolved into a steady state. The output result is one of patterns in a training set. We also present an application of this protocol in image recognition. The simulation results show that this network works in the quantum environment and the output images are correct, therefore the feasibility of this protocol is verified.

中文翻译：

---

量子Hopfield神经网络模型和图像识别

量子计算是一种遵循量子力学定律的新模式。它基于量子单位的控制执行计算任务。从可计算问题的角度来看，量子计算机只能解决传统计算机可以解决的问题，但是考虑到计算效率，由于存在量子叠加，某些已知的量子算法比传统的通用计算机快几倍。在本文中，我们将量子计算与经典神经网络相结合以设计量子Hopfield网络。每个神经元都初始化为指定状态，然后演化为稳定状态。输出结果是训练集中的模式之一。我们还提出了该协议在图像识别中的应用。