Distributed Quantum Machine Learning: Federated and Model-Parallel Approaches

In this article, we explore two types of distributed quantum machine learning (DQML) methodologies: quantum federated learning and quantum model-parallel learning. We discuss the challenges encountered in DQML, propose potential solutions, and highlight future research directions in this rapidly evolving field. Additionally, we implement two solutions tailored to the two types of DQML, aiming to enhance the reliability of the computing process. Our results show the potential of DQML in the current Noisy Intermediate-Scale Quantum era.