Quantum machine learning (QML) sits at the intersection of quantum computing and classical machine learning, offering the prospect of new computational paradigms and advantages for processing complex data. This chapter introduces the fundamentals of QML for readers from both communities, establishing a shared conceptual foundation. We connect the worst-case adversarial perspective from theoretical computer science with the physical principles of quantum systems, highlighting how superposition, entanglement, and measurement collapse influence learning and robustness. Special attention is given to adversarial robustness, understood as the ability of QML models to resist inputs crafted to cause failure. We motivate the study of QML in adversarial settings, outline key distinctions between classical and quantum data and computation, and propose an extended taxonomy incorporating the adversary as a core element. This chapter serves as a conceptual entry point to quantum adversarial machine learning and lays the groundwork for deeper discussions on quantum robustness in subsequent chapters.