Quantum Supervised Learning
It tackles the challenge of integrating quantum computing into supervised learning for researchers and practitioners, but is incremental as it reviews and reframes existing approaches rather than introducing new methods.
This paper addresses skepticism about near-term quantum advantage in supervised learning by providing a classical perspective on quantum algorithms, aiming to bridge traditional machine learning with quantum advancements and foster involvement of classical practitioners.
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.