Are the classes separable with e.g. the intertwined spiral dataset in the TensorFlow demo? Maybe only with a radial basis function kernel?
Separable state https://en.wikipedia.org/wiki/Separable_state :
> In quantum mechanics, separable states are quantum states belonging to a composite space that can be factored into individual states belonging to separate subspaces. A state is said to be entangled if it is not separable. In general, determining if a state is separable is not straightforward and the problem is classed as NP-hard.
An algorithm may converge upon the same wrong - or 'high error' - answer; regardless of a random seed parameter.
It looks like there is randomization for SVMs for e.g. Platt scaling [1], though I had confused Simulated Annealing with SVMs. And then re-read Quantum Annealing; what is the ground state of the Hamiltonian any why would I use a hyperplane instead?