  In this thesis we aim to analyze and quantify the energetic and information
contents that can be extracted from a dynamical system subject to the external
environment. The latter is usually assumed to be deleterious for the
feasibility of specific control tasks, since it can be responsible for
uncontrolled time-dependent changes of the system. However, if the effects of
the random interaction with a noisy environment are properly modeled by the
introduction of a given stochasticity within the dynamics of the system, then
even noise contributions might be seen as control knobs. As a matter of fact,
even a partial knowledge of the environment can allow to set the system in a
dynamical condition in which the response is optimized by the presence of noise
sources. In particular, we have investigated what kind of measurement devices
can work better in noisy dynamical regimes and studied how to maximize the
resultant information via the adoption of estimation algorithms. Moreover, we
have shown the optimal interplay between quantum dynamics, environmental noise
and complex network topology in maximizing the energy transport efficiency.
Then, foundational scientific aspects, such as the occurrence of an ergodic
property for the system-environment interaction modes of a randomly perturbed
quantum system or the characterization of the stochastic quantum Zeno
phenomena, have been analyzed by using the predictions of the large deviation
theory. Finally, the energy cost in maintaining the system in the
non-equilibrium regime due to the presence of the environment is evaluated by
reconstructing the corresponding thermodynamics entropy production. In
conclusion, the present thesis can constitute the basis for an effective
resource theory of noise, which is given by properly engineering the
interaction between a dynamical system and its external environment.
