Questions tagged [kernel-methods]

Kernel methods are a class of machine learning algorithms for pattern analysis (e.g. SVMs). Any linear model can be turned into a non-linear model by applying the kernel trick to the model, i.e. replacing its features with a kernel function. Quantum computers are expected to improve existing classical kernel-based ML methods through their ability to efficiently access and manipulate data in large quantum feature spaces, which is classically intractable.

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Quantum SVM with large feature set

I am trying to practice QSVM from the following tutorial Introduction into Quantum Support Vector Machines The author has used 2 feature_dimension with 2 component PCA ...
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How does the ZZ Feature Map influence the measurement?

I've been look at this Notebook from qiskit and trying to understand whats happening, but can't quite figure it out. From my understanding, rotations around the Z ...
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Kernel ridge regression with qiskit's FeatureMap shows nonlinear patterns outside [0,1] range

I'm implementing a kernel ridge regressor using qiskit's FeatureMap and QuantumKernel to compute the alpha parameters of the solution. If I try to fit my model with non-normalized features I obtain ...
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Is a "kernel" just the quantum equivalent of classical SVMs?

I'm confused about the relationship between kernel methods and SVM methods used in quantum machine learning. Sometimes the two seem to be used interchangeably, but often I'll see them both in the same ...