论文标题
非线性量子神经元：量子神经网络的基本构建块
Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
论文作者
论文摘要
量子计算使量子神经网络（QNN）具有超越人工神经网络（ANN）的巨大潜力。神经网络的强大概括归因于非线性激活函数。尽管已经开发了与QNN相关的各种模型，但它们面临着将神经计算的非线性耗散动力学合并到线性统一量子系统中的挑战。在本文中，我们建立了不同的量子电路以近似非线性函数，然后提出了一个可推广的框架以实现任何非线性量子神经元。我们根据提出的框架提出了两个量子神经元示例。构建单个量子神经元所需的量子资源是输入大小的多项式。最后，IBM量子经验结果和数值模拟都说明了所提出的框架的有效性。
Quantum computing enables quantum neural networks (QNNs) to have great potentials to surpass artificial neural networks (ANNs). The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are the polynomial, in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.
