Gaussian quantum reservoir computing with a hybrid cavity magnomechanical system

Abstract

We propose a quantum reservoir computing framework based on a hybrid cavity magnomechanical system in the linearized Gaussian regime. The reservoir combines microwave-cavity, magnon, and mechanical degrees of freedom, and is extended by an auxiliary cavity acting as an input port, with time-dependent signals encoded in its detuning. The covariance matrix of the quadrature fluctuations provides the features for a trained linear readout. Using linear-memory, nonlinearmemory, and parity-check benchmarks, we find strong temporal memory together with more limited nonlinear processing, whose balance is controlled by the reservoir evolution time, and we show that intermode correlations substantially enhance the information accessible to the readout. The same architecture reconstructs a time-dependent signal encoded in the auxiliary-cavity detuning, with an accuracy governed by the interplay between the internal couplings and the encoding strength, and robust against Gaussian detuning noise. Accounting for finite measurement statistics reveals a trade-off between encoding strength and the precision of the covariance estimation, so that the optimal encoding depends on the available measurement budget. These results establish hybrid cavity magnomechanical systems as a promising platform for continuous-variable quantum reservoir computing with potential applications in signal probing.