  Utilizing a quantum system for reservoir computing has recently received a
lot of attention. Key challenges are related to how on can optimally en- and
decode classical information, as well as what constitutes a good reservoir. Our
main contribution is a generalization of the standard way to robustly en- and
decode time series into subspaces defined by the cosets of a given stabilizer.
A key observation is the necessity to perform the decoding step, which in turn
ensures a consistent way of encoding. This provides a systematic way to encode
classical information in a robust way. We provide a numerical analysis on a
discrete time series given by two standard maps, namely the logistic and the
H\'enon map. Our numerical findings indicate that the system's performance is
increasing with the length of the training data.
