Summary
In this chapter, we first learned what support vector machines are, and how they can be trained to solve binary classification problems. We began by considering vanilla vector machines, and then we introduced the kernel trick — which opened up a world of possibilities! In particular, we saw how QSVMs are nothing more than a support vector machine with a quantum kernel.
From there on, we learned how quantum kernels actually work and how to implement them. We explored the essential role of feature maps, and discussed a few of the most well-known ones.
Finally, we learned how to implement, train, and use quantum support vector machines with PennyLane and Qiskit. In addition, we were able to very easily run QSVMs on real hardware thanks to Qiskit’s interface to IBM Quantum.
And that pretty much covers how QSVMs can help you can identify wines — or solve any other classification task — like an expert, all while happily ignoring what the ”alkalinity...
