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#quantum feature maps
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- Quantum Kernels and QSVMs: Can Quantum Feature Spaces Give Machine Learning an Edge?
Support vector machines classify data by finding a separating hyperplane in a high-dimensional feature space, using the kernel trick to avoid computing the feature map explicitly. Quantum computers can evaluate inner products in exponentially large Hilbert spaces — making quantum kernels a natural candidate for quantum advantage in machine learning. Here is how quantum kernel SVMs work, what has been proved about their advantage, and where the honest limits currently lie.
