  Current technologies in quantum-based communications bring a new integration
of quantum data with classical data for hybrid processing. However, the
frameworks of these technologies are restricted to a single classical or
quantum task, which limits their flexibility in near-term applications. We
propose a quantum reservoir processor to harness quantum dynamics in
computational tasks requiring both classical and quantum inputs. This analog
processor comprises a network of quantum dots in which quantum data is incident
to the network and classical data is encoded via a coherent field exciting the
network. We perform a multitasking application of quantum tomography and
nonlinear equalization of classical channels. Interestingly, the tomography can
be performed in a closed-loop manner via the feedback control of classical
data. Therefore, if the classical input comes from a dynamical system,
embedding this system in a closed loop enables hybrid processing even if access
to the external classical input is interrupted. Finally, we demonstrate
preparing quantum depolarizing channels as a novel quantum machine learning
technique for quantum data processing.
