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.

翻译：量子计算的最新进展使其成为解决复杂计算难题的潜在方案，而监督学习正成为其应用前景广阔的领域。尽管存在这一潜力，量子机器学习领域仍处于早期阶段，且对于近期实现量子优势的可能性仍存在一定程度的质疑。本文旨在从经典视角审视当前用于监督学习的量子算法，有效衔接传统机器学习原理与量子机器学习的进展。具体而言，本研究规划了一条与当前量子机器学习文献主流关注点不同的研究路径：从经典方法的需求出发，阐明量子方法可能产生的影响。通过这一探索，我们的目标是深化对经典方法与量子方法融合的理解，从而为两个领域的未来发展奠定基础，并促进经典机器学习研究者参与量子机器学习领域。