9.2 Going quantum
As we have already mentioned, quantum support vector machines are particular cases of SVMs. To be more precise, they are particular cases of SVMs that rely on the kernel trick.
We have seen in the previous section how, with the kernel trick, we take our data to a feature space: a higher dimensional space in which, we hope, our data will be separable by a hyperplane with the right choice of feature map. This feature space is usually just the ordinary Euclidean space but, well, with a higher dimension. But we can consider other choices. How about…the space of quantum states?
9.2.1 The general idea behind quantum support vector machines
A QSVM works just like an ordinary SVM that relies on the kernel trick — with the only difference that it uses as feature space a certain space of quantum states.
As we discussed before, whenever we use the kernel trick, all we need from the feature space is a kernel function. That’s the only ingredient involving the...
