Quantum Algorithms for Machine Learning

Through the integration of machine learning techniques with the computational power of quantum computing, quantum algorithms greatly improve the processing and analytical capabilities of systems. In contrast to traditional techniques, quantum computing effectively manages large and intricate datasets, providing answers to issues that conventional systems are unable to handle. Quantum Machine Learning (QML) has shown promise in a variety of fields, such as large-scale optimization, image recognition, and natural language processing. Because it can produce better and faster results, QML is positioned to revolutionize industries that depend on insights from data. The implementation of QML faces operational and scalability complications when used in healthcare applications and other fields of practical use. This study evaluates the active QML algorithms while demonstrating their improved functionality compared to usual approaches, and it identifies implementation barriers in real-world scenarios.