Quantum Kernel Estimation

Quantum kernel estimation (QKE) aims to leverage quantum computers to efficiently compute kernel functions, crucial components of many classical machine learning algorithms like Support Vector Machines (SVMs). Current research focuses on developing and benchmarking QKE methods for classification and anomaly detection tasks, often employing variational quantum circuits and quantum feature maps within hybrid quantum-classical workflows. This approach shows promise for improving the performance and scalability of classical machine learning, particularly in high-dimensional data scenarios, with applications emerging in diverse fields such as medical diagnosis and satellite image analysis. However, challenges remain in achieving consistent performance improvements over classical methods, especially when generalizing to unseen data.