When Federated Learning Meets Quantum Computing: Survey and Research Opportunities

Quantum Federated Learning (QFL) is a developing field that combines Quantum Computing (QC) advancements to enhance the scalability and efficiency of decentralized Federated Learning (FL) models. This paper presents a systematic survey of the challenges and solutions at the intersection of FL and QC, focusing on architectural limitations, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation. It introduces two new metrics: qubit utilization efficiency and quantum model training strategy, providing a comprehensive analysis of current QFL research.