Quantum Feature Map

Quantum feature maps are mathematical functions that encode classical data into quantum states, enabling the application of quantum algorithms to machine learning tasks. Current research focuses on developing effective feature map designs, often within the context of quantum kernel methods and support vector machines, investigating their performance across various datasets and comparing them to classical counterparts. This research aims to determine the practical advantages of quantum approaches, particularly in scenarios with limited data or complex relationships, and to improve the efficiency and accuracy of quantum machine learning algorithms for applications in diverse fields such as medical diagnostics and finance.