Quantum Support Vector Machine

Quantum Support Vector Machines (QSVMs) aim to leverage quantum computing's power to enhance the performance of classical Support Vector Machines, primarily for classification tasks. Current research focuses on optimizing quantum kernel design, exploring various feature map architectures and algorithms like variational quantum circuits and quantum kernel estimation, and integrating QSVMs into hybrid quantum-classical pipelines to address limitations of current quantum hardware. This field holds significant promise for improving the accuracy and efficiency of machine learning in diverse applications, including healthcare diagnostics, cybersecurity, and financial fraud detection, although challenges remain in scaling to larger datasets and achieving clear quantum advantages over classical methods.