论文标题
量子分布式深度学习体系结构：模型，讨论和应用
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
论文作者
论文摘要
尽管深度学习（DL）已经成为用于各种数据处理任务的最先进技术，但由于其高数据和计算能力依赖性，数据安全性和计算超载问题通常会出现。为了解决这个问题，已经出现了量子深度学习（QDL）和分布式深度学习（DDL），以补充现有的DL方法。此外，结合并最大化这些优势的量子分布深度学习（QDDL）技术正在引起人们的注意。本文比较了QDDL的几个模型结构，并讨论了它们的可能性和局限性，以利用QDDL的某些代表性应用程序方案。
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) has emerged to complement existing DL methods. Furthermore, a quantum distributed deep learning (QDDL) technique that combines and maximizes these advantages is getting attention. This paper compares several model structures for QDDL and discusses their possibilities and limitations to leverage QDDL for some representative application scenarios.
