  The characterization of the Hamiltonian parameters defining a quantum walk is
of paramount importance when performing a variety of tasks, from quantum
communication to computation. When dealing with physical implementations of
quantum walks, the parameters themselves may not be directly accessible, thus
it is necessary to find alternative estimation strategies exploiting other
observables. Here, we perform the multiparameter estimation of the Hamiltonian
parameters characterizing a continuous-time quantum walk over a line graph with
$n$-neighbour interactions using a deep neural network model fed with
experimental probabilities at a given evolution time. We compare our results
with the bounds derived from estimation theory and find that the neural network
acts as a nearly optimal estimator both when the estimation of two or three
parameters is performed.
