Hello, could you explain all the potential ways of using loss functions when the interface is set to NumPy?
Loss functions, whether the interface is NumPy, Pytorch, etc., describe something you generally want to find the optimal value of (usually the global minimum). In that sense, the use of a loss function is pretty one-dimensional. Hope that
With PennyLane, you have the flexibility to define custom loss functions using NumPy functions, and combine quantum and classical components to create your loss function for optimization tasks. You can then use PennyLane’s optimizers to update the parameters of your quantum circuit to minimize this loss function. Our gradients and training page might be a good resource.
Hope this helps!
Thank you, I appreciate it.