  Utilising dynamic electromagnetic field control over charged particles serves
as the basis for a quantum machine learning platform that operates on
observables rather than directly on states. Such a platform can be physically
realised in ion traps or particle confinement devices that utilise
electromagnetic fields as the source of control. The electromagnetic field acts
as the ansatz within the learning algorithm. The models discussed are exactly
solvable, with exact solutions serving as precursors for learning tasks to
emerge, including regression and classification algorithms as particular cases.
This approach is considered in terms of canonical variables with semi-classical
behaviour, disregarding relativistic degrees of freedom.
