Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing

Abstract Quantum reservoir computing aims at harnessing the rich dynamics of quantum systems for machine-learning purposes. It can be used online time series processing while having a remarkably low training cost. Here, we establish potential continuous-variable Gaussian states linear dynamical computing. We prove that resources are enough universal find encoding input into is both source and means to tune nonlinearity overall input-output map. further show full proposed model reached by fluctuations, such as squeezed vacuum, instead classical fields or thermal fluctuations. Our results introduce research paradigm engineered states.