Practical and Scalable Quantum Reservoir Computing

Quantum Reservoir Computing (QRC) leverages quantum systems to solve complex computational tasks with unprecedented efficiency and reduced energy consumption. We present a novel QRC framework utilizing a quantum optical reservoir composed of two-level atoms within a single-mode optical cavity, where a feedback mechanism is introduced to use observable readouts from the cavity and atoms to modify the input function encoded in coherent laser driving. The QRC is conveniently scalable as connections among atoms are guaranteed by their shared cavity field, and practically measurable due to continuous quantum measurements. We evaluate the reservoir's performance in memory retention and nonlinear data processing through two primary tasks: the prediction of time-series data via the Mackey-Glass task and the classification of sine-square waveforms. Our results demonstrate significant enhancements in performance brought by the increased number of atoms, the feedback mechanism, and the polynomial regression technique, establishing the advantage of QRC compared to traditional classical reservoir computing. This study confirms the potential of QRC to offer an efficient solution for advanced computational challenges, marking a significant step forward in the integration of quantum physics with machine learning technology.