论文标题
深神经网络作为量子神经网络的半古典限制
Deep Neural Networks as the Semi-classical Limit of Quantum Neural Networks
论文作者
论文摘要
我们的工作旨在表明：（1）可以将量子神经网络（QNN）映射到SpinNetworks上，结果是可以在拓扑量子场理论（TQFT）的一侧进行其操作的分析水平； （2）深度神经网络（DNN）是QNN的一个亚个子，从某种意义上说，它们是QNN的半经典极限； （3）可以使用TQFT的术语来重塑许多机器学习（ML）密钥概念。我们的框架也提供了一个有效的假设，用于理解DNN的概括行为，并将其与所涉及的图形结构的拓扑特征相关联。
Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN; (3) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNN, relating it to the topological features of the graphs structures involved.
