  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.

Quantum Physics;Emerging Technologies;Machine Learning;Neural and Evolutionary Computing