  Given the key role that quantum tunneling plays in a wide range of
applications, a crucial objective is to maximize the probability of tunneling
from one quantum state/level to another, while keeping the resources of the
underlying physical system fixed. In this work, we demonstrate that an
effective solution to this challenge can be achieved by coupling the tunneling
system with an ancillary system of the same kind. By utilizing machine learning
techniques, the parameters of both the ancillary system and the coupling can be
optimized, leading to the maximization of the tunneling probability. We provide
illustrative examples for the paradigmatic scenario involving a two-mode system
and a two-mode ancilla with arbitrary couplings and in the presence of several
interacting particles. Importantly, the enhancement of the tunneling
probability appears to be minimally affected by noise and decoherence in both
the system and the ancilla.
