Integration of Machine Learning with Quantum Annealing

Hadi Salloum, Hamza Shafee Aldaghstany, Osama Orabi, Ahmad Haidar, Mohammad Reza Bahrami, Manuel Mazzara

Abstract: The hasty advancement of Quantum Computing (QC) technologies has led to offer plethora of promising offering promising opportunities for solving complex optimization problems, especially Quantum Annealing (QA). This paper presents an investigation into the fusion of ML and QA. It also explores and elucidates the capabilities of ML empowered by QA techniques. Furthermore, it introduces an implementation strategy that increases the potential of QA through the integration of ML, and vice versa. This research advances our understanding of utilizing QA paradigms to enhance optimization methodologies within the realm of ML.