论文标题
在超导量子处理器中使用重复统一的电路来实现量子神经网络
Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
论文作者
论文摘要
人工神经网络已成为用于复杂问题的数字解决方案不可或缺的一部分。但是，在量子处理器上采用神经网络面临与使用量子电路实施非线性函数有关的挑战。在本文中，我们使用通过实时控制流反馈启用的重复启用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的构建块，可以在各种布局中排列，以相干地执行深度学习任务。例如，我们构建了一个最小的前馈量子神经网络，能够通过优化监督学习范式内的网络激活参数来学习所有2-1位布尔函数。该模型被证明可以执行非线性分类，并从单个训练状态的多个副本中有效地学习，该验证由所有输入的最大叠加组成。
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.
