  Achieving fast cooling of motional modes is a prerequisite for leveraging
such bosonic quanta for high-speed quantum information processing. In this
work, we address the aspect of reducing the time limit for cooling below that
constrained by the conventional sideband cooling techniques; and propose a
scheme to apply deep reinforcement learning (DRL) to achieve this. In
particular, we have shown how the scheme can be used effectively to accelerate
the dynamic motional cooling of a macroscopic magnonic sphere, and how it can
be uniformly extended for more complex systems, for example, a tripartite
opto-magno-mechanical system to obtain cooling of the motional mode below the
time bound of coherent cooling. While conventional sideband cooling methods do
not work beyond the well-known rotating wave approximation (RWA) regimes, our
proposed DRL scheme can be applied uniformly to regimes operating within and
beyond the RWA, and thus this offers a new and complete toolkit for rapid
control and generation of macroscopic quantum states for application in quantum
technologies.
