Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications

TL;DR

This paper reviews quantum distributed deep learning architectures, comparing models, discussing their potential and limitations, and exploring their applications to address data security and computational challenges in deep learning.

Contribution

It provides a comprehensive comparison of QDDL models, highlighting their advantages, limitations, and potential application scenarios.

Findings

QDDL models offer promising solutions for secure and efficient deep learning.

Different QDDL architectures have unique strengths and limitations.

Potential applications include data security and computational efficiency improvements.

Abstract

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.