“…Our approach builds on the ideas of [11], (see [12] for a generalization to quantum walks), where stochastic analogues of reachable and non-observable spaces are used to characterize equivalent hidden-Markov processes. Exploiting the ideas developed in [13,14] for unconditional dynamics, the method leverages algebraic quantum probability tools, in particular conditional expectations, to allow for reduced conditional models that generate the same measurement distribution, reproduce the average of observables of interest, and do so while maintaining the same structure and physical constraints as the original model. In general, existing reduction approaches cannot guarantee that the reduced evolution is completely positive T. Grigoletto and F. Ticozzi are with the Department of Information Engineering, University of Padova, Via Gradenigo 6, 35131 Padova, Italy.…”