Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.

人工神经网络正在成为复杂问题数字解决方案不可或缺的一部分。然而，在量子处理器上采用神经网络面临着与使用量子电路实现非线性函数相关的挑战。在本文中，我们使用由实时控制流反馈启用的重复直到成功电路来实现具有非线性激活函数的量子神经元。这些神经元构成了基本构建块，可以以各种布局进行排列，以量子连贯地执行深度学习任务。例如，我们构建了一个最小前馈量子神经网络，能够通过在监督学习范式中优化网络激活参数来学习所有 2 至 1 位布尔函数。该模型被证明可以执行非线性分类，并有效地从由所有输入的最大叠加组成的单个训练状态的多个副本中学习。