Quantum distributed deep learning architectures: Models, discussions, and applications

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, security and computational overload problems often arise due to their high power dependency. To solve this problem, quantum (QDL) distributed (DDL) emerged complement existing DL methods. Furthermore, (QDDL) technique that combines maximizes these advantages is getting attention. This paper compares several model structures QDDL discusses possibilities limitations leverage some representative application scenarios.