Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
This work addresses data security and computational efficiency issues for deep learning practitioners, but it appears incremental as it reviews and discusses existing QDL and DDL combinations without presenting new results.
The paper tackles the data security and computational overload problems in deep learning by exploring quantum distributed deep learning (QDDL) architectures, comparing model structures and discussing their potential and limitations for applications.
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.