Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing

Abstract The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural used machine learning. In this context, quantum reservoir computing (QRC) constitutes a extension the use classical recurrent using resources temporal processing. Here, we explore fundamental properties QRC systems based on qubits and continuous variables. We provide analytical results that illustrate how nonlinearity enters input–output map in these implementations. find input encoding through state initialization serve to control type as well dependence history sequences processed.