Quantum Federated Learning: Analysis, Design and Implementation Challenges

TL;DR

This paper provides a comprehensive overview of Quantum Federated Learning, analyzing its current state, challenges, and future research directions in the context of quantum computing and distributed machine learning.

Contribution

It offers a detailed analysis of existing QFL frameworks, explores diverse applications, and discusses design considerations and open research questions.

Findings

Examines technical contributions and limitations of current QFL projects

Identifies critical factors influencing QFL design

Proposes future research directions and open questions

Abstract

Quantum Federated Learning (QFL) has gained significant attention due to quantum computing and machine learning advancements. As the demand for QFL continues to surge, there is a pressing need to comprehend its intricacies in distributed environments. This paper aims to provide a comprehensive overview of the current state of QFL, addressing a crucial knowledge gap in the existing literature. We develop ideas for new QFL frameworks, explore diverse use cases of applications, and consider the critical factors influencing their design. The technical contributions and limitations of various QFL research projects are examined while presenting future research directions and open questions for further exploration.