Quantum Federated Learning: Analysis, Design and Implementation Challenges
It addresses a knowledge gap for researchers and practitioners in quantum computing and machine learning, but it is incremental as it reviews and synthesizes existing work rather than presenting new experimental results.
This paper tackles the lack of comprehensive understanding in Quantum Federated Learning (QFL) by providing an overview of its current state, developing ideas for new frameworks, and exploring applications and design factors.
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.