  Recent advancements in quantum computing have positioned it as a prospective
solution for tackling intricate computational challenges, with supervised
learning emerging as a promising domain for its application. Despite this
potential, the field of quantum machine learning is still in its early stages,
and there persists a level of skepticism regarding a possible near-term quantum
advantage. This paper aims to provide a classical perspective on current
quantum algorithms for supervised learning, effectively bridging traditional
machine learning principles with advancements in quantum machine learning.
Specifically, this study charts a research trajectory that diverges from the
predominant focus of quantum machine learning literature, originating from the
prerequisites of classical methodologies and elucidating the potential impact
of quantum approaches. Through this exploration, our objective is to deepen the
understanding of the convergence between classical and quantum methods, thereby
laying the groundwork for future advancements in both domains and fostering the
involvement of classical practitioners in the field of quantum machine
learning.
