量子分布式深度学习架构：模型、讨论和应用
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
摘要：
虽然深度学习（DL）已经成为各种数据处理任务的最先进技术，但由于其高度的数据和计算能力依赖性，数据安全和计算过载问题经常出现。为了解决这个问题，量子深度学习（QDL）和分布式深度学习（DAI）已经出现，以补充现有的深度学习方法。此外，一种结合并最大化这些优势的量子分布式深度学习（QSTs）技术正在引起人们的关注。本文比较了几种模型结构的QSTOM，并讨论了它们的可能性和局限性，利用QSTOM的一些代表性的应用场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
