[eng] Reservoir Computing is a paradigm of Machine Learning that harnesses the information processing potential of complex dynamical systems. Recently, quantum systems have been suggested
as promising candidates for reservoir computing due to the significant growth in degrees of freedom they allow against their classical counterparts. In this thesis, a photonic platform is proposed
for reservoir computing and on-line data processing, in the realm of continuous variable quantum
optics. A non-linear medium provides the needed interaction between different optical modes. The
output light is splitted in a re-injected and a measured component. Control of the needed memory
is effectively achieved through the coherent optical feedback. The problem of quantum measurements and back-action are addressed for the first time in this context. The performance of the
model is numerically tested for each of the main parameters that can be externally tuned. The
role of quantum effects, i.e. squeezing, is also addressed in connection with the computational
capacity of the system. To conclude, aspects relevant for possible experimental implementations
are commented