Published Paper Details:

Quantum Machine Learning (QML) merges the capabilities of quantum computing with the methods of machine learning to address complex problems with greater efficiency. This paper explores the foundational principles of QML, recent progress in the field, and its application across diverse sectors such as optimization, natural language processing, and quantum chemistry. A comparative analysis is conducted between quantum-enhanced algorithms and their classical counterparts, with a focus on performance metrics. The challenges of implementing QML on noisy intermediate-scale quantum (NISQ) devices are also examined. The paper concludes by discussing future directions in the field, particularly in relation to the development of quantum hardware, algorithms, and software, as well as the potential transformative impact of QML in fields ranging from drug discovery to financial modeling and autonomous systems. Despite the current constraints of NISQ technology, we highlight the substantial promise that QML holds in advancing artificial intelligence and computational science.