A Quantum Reservoir for Neurodynamical Forecasting

Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small‑data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse‑field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, simulations show that the quantum reservoir outperforms a classical counterpart, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on more realistic signals, we evaluate QRC on simulated multivariate human electroencephalography (EEG) data with a parallel architecture. While predictive accuracy decreases for this challenging task, the results represent a meaningful first step toward quantum forecasting of biologically realistic neural data, including on quantum hardware. Overall, our findings indicate that although current quantum hardware and parallel reservoir designs do not yet surpass classical methods on complex neural signals, QRC can be executed on near‑term devices with realistic EEG‑like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time‑series forecasting with quantum systems.