What Makes Quantum Machine Learning “Quantum”?

Quantum machine learning (QML) is defined by its use of quantum mechanics to represent, transform, and read out information, not by speed or specific hardware. Unlike classical machine learning, QML represents data as quantum states, uses quantum operations like unitary transformations for its models, and integrates probabilistic measurement as a core part of the learning process. Many methods labeled as QML are merely "quantum-inspired" classical algorithms; a true quantum model cannot have its quantum component replaced without altering its fundamental mathematical structure. While current hardware limitations mean there is no proven "quantum advantage" for ML tasks today, studying QML is crucial for expanding the theoretical foundations of learning and preparing for future hardware advancements.