QMAC, or Quantum Machine Learning, refers to the intersection of quantum computing and machine learning, where quantum algorithms are developed to enhance the performance of machine learning tasks. It leverages quantum mechanics principles to potentially process and analyze data more efficiently than classical methods. Researchers explore QMAC to tackle complex problems in areas like pattern recognition, optimization, and data classification, with the aim of achieving speedups and improved accuracy. The field is still in its early stages, with ongoing advancements in both quantum hardware and algorithm development.