Ensemble classification with Rigetti and Qiskit devices : parameters

PennyLane Plugins
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Hey @nauvan!

As I said here, the parameters here are simply obtained from minimizing a cost function akin to what’s presented in the variational classifier demo.

Basically, the predict function in the ensemble classification demo would then be fed into a cost function akin to this

def cost(trainable_parameters, X, Y):
    predictions = [model(trainable_parameters, x) for x in X]
    return square_loss(Y, predictions)

Then you can minimize cost and look at its parameters, which is what parameters.npy is in the ensemble classification demo :slight_smile:.