Quantum Federated Learning: Analysis, Design and Implementation
  Challenges
Dev Gurung Shiva Raj Pokhrel Gang Li
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.