Quantum Computing, Quantum Algorithms, Quantum Machine Learning, Code, Artificial Intelligence.
comes into play when we deal with non-linearly separable data. In such cases, SVM is typically extended using kernel functions to map the input space into a higher-dimensional space where the data becomes linearly separable, and in this higher-dimensional space, the dividing hyperplane is flat.