Quantum Kernel

Quantum kernels are a hybrid quantum-classical approach in machine learning aiming to leverage quantum computation for enhanced kernel methods. Current research focuses on developing efficient quantum feature maps for various kernel types (e.g., fidelity, projected, Laplacian), exploring different model architectures like quantum support vector machines and Gaussian process regression, and optimizing training procedures through techniques such as coreset selection and distributed computing. This field is significant because it offers a potential pathway to quantum advantage in machine learning tasks, particularly where classical methods struggle with high dimensionality or limited data, with applications emerging in diverse areas like image classification, medical diagnostics, and time series forecasting.