论文标题
连续变化的深度量子神经网络，用于灵活学习结构化经典信息
Continuous-Variable Deep Quantum Neural Networks for Flexible Learning of Structured Classical Information
论文作者
论文摘要
使用光学模式的量子计算在构建深神经网络的能力方面已经建立了良好的。这些网络已被证明在架构上以及正在处理的数据类型方面都具有灵活性。我们利用连续变量（CV）模型的这种属性来构建堆叠的单模式网络，这些网络被证明可以学习结构化的经典信息，同时对网络的大小没有限制，同时保持其复杂性。简历模型的标志是它使用一组允许其完全统一的门锻造非线性函数的能力。提出的模型举例说明，可以将适当的光子硬件与当今的光学通信系统集成在一起，以满足我们的信息处理要求。在本文中，使用手写数字的MNIST数据集上的草莓田软件库，我们演示了网络的适应性，以学习经典信息以超过99.98％的忠诚度
Quantum computation using optical modes has been well-established in its ability to construct deep neural networks. These networks have been shown to be flexible both architecturally as well as in terms of the type of data being processed. We leverage this property of the Continuous-Variable (CV) model to construct stacked single mode networks that are shown to learn structured classical information, while placing no restrictions on the size of the network, and at the same time maintaining it's complexity. The hallmark of the CV model is its ability to forge non-linear functions using a set of gates that allows it to remain completely unitary. The proposed model exemplifies that the appropriate photonic hardware can be integrated with present day optical communication systems to meet our information processing requirements. In this paper, using the Strawberry Fields software library on the MNIST dataset of hand-written digits, we demonstrate the adaptability of the network to learn classical information to fidelities of greater than 99.98%
