Quantum Machine Learning: Core Principles, Challenges and Enablers

QML combines quantum computing and machine learning to efficiently solve complex problems. This survey reviews QML's fundamentals, key models, and enabling technologies, focusing on data encoding, quantum algorithms, and the integration of large language models (LLMs). We highlight LLMs' potential to optimize algorithms, address challenges like barren plateaus, and improve interpretability. Advances in quantum hardware and software bridging theory and practice are discussed, alongside quantum feature encoding methods critical for high-dimensional data. Key QML models demonstrating quantum speedups in fields such as finance, healthcare, and logistics are examined. Despite its promise, QML faces challenges including noise, limited hardware scalability, and encoding complexity, necessitating progress in error correction, hardware, and hybrid algorithms.