Cavity Optomechanics as a Platform for Quantum Reservoir Computing

Neuromorphic computing represents a promising pathway for enhancing the efficiency and adaptability of modern machine learning techniques. Previous work in Quantum Reservoir Computing (QRC) has suggested there may be empirical advantages over comparable classical models in certain regimes, motivating investigation into its physical implementation.

In this work, we study cavity optomechanical systems as a platform for QRC, proposing a multi-mode optomechanical model incorporating radiation-pressure nonlinearities to realize a high-dimensional quantum reservoir. The system dynamics are simulated using master-equation solvers to evaluate learning performance on benchmark time-series prediction tasks such as NARMA and Mackey–Glass under realistic decoherence and measurement noise.

Building on previous results suggesting that continuous-variable nonlinear mappings in quantum systems can yield increased performance relative to comparable classical reservoirs, we investigate cavity optomechanical systems as a potentially advantageous architecture for quantum machine learning, and lay the groundwork for future experimental realization.