The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we demonstrate that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of evaluation of the use of artificial neural networks efficiently implemented on near-term quantum devices.

翻译：早期的人工量子神经元模型沿袭了经典模型的路径，仅能处理离散数值。本文提出了一种通过操纵复数相位来推广二进制模型的算法。我们设计、测试并实现了一种可在量子计算机上处理连续数值的神经元模型。通过仿真实验，我们证明该模型能够采用梯度下降作为学习算法的混合训练方案。本工作为评估在近期量子设备上高效实现人工神经网络的应用前景迈出了新的一步。