Quantum Machine Learning: Fundamentals, Potential and Challenges for Scientific Training

Abstract

Quantum machine learning (QML) represents one of the most promising intersections between computer science, quantum physics, and data analysis, enabling significant advances in several scientific and technological areas. This article explores the theoretical and practical foundations of QML, explaining how quantum algorithms can overcome limitations of classical methods, especially in problems involving large volumes of data and complex patterns. Based on a detailed analysis of the main techniques, current challenges, and future perspectives, this work aims to guide students on the relevance of the topic for the development of contemporary scientific research and its practical applications in academia and industry. The approach adopted emphasizes the need for critical understanding of hardware, algorithms, and the conditions under which the quantum advantage actually manifests itself, forming a solid foundation for the training of future researchers in data science and quantum computing.