Quantum Machine Learning: A Review of Concepts, Innovations, and Future Directions

Quantum Machine Learning (QML) is a new interdisciplinary field that combines quantum computing and machine learning to address the limitations of classical algorithms. By using qubits, superposition, and entanglement, quantum models aim to accelerate training, optimization, and data classification. This review paper gives an overview of the basics of QML, examines key research contributions, discusses recent advancements such as variational quantum circuits and quantum support vector machines, and points out potential uses in fields like healthcare, finance, and cybersecurity. It also critically reviews challenges like hardware limitations, noise, and data encoding issues. The paper concludes by identifying gaps in research and suggesting future directions, positioning QML as a promising but developing area. Keywords: Quantum Computing, Machine Learning, Quantum Algorithms, Variational Circuits, Quantum Neural Networks.