Speaker
Description
Reliable short- to medium-horizon forecasts of cosmic-ray/neutron monitor count rates support detector operations, data-quality monitoring, and space-weather analyses, but modern deep sequence models can be costly to train and tune across stations and solar conditions. We present a practical Quantum Reservoir Computing (QRC) pipeline for sustainable time-series forecasting on neutron monitor data, focusing on long-running stations including Lomnický štít and complementary monitors as well as temporal cosmic-ray datasets. Our approach uses a small, fixed parameterized quantum circuit as a nonlinear dynamical reservoir driven by lagged count-rate inputs; only a lightweight linear readout is trained, enabling rapid model updates and low training energy. We evaluate point and probabilistic forecasts under diurnal/seasonal variability and solar transient periods using rolling-origin backtesting, and compare against persistence, ARIMA, classical echo-state networks, and compact deep baselines (e.g., temporal CNN/LSTM). Across stations, QRC achieves competitive accuracy while substantially reducing trainable parameters and retraining time, and it remains robust under moderate concept drift via fast readout re-fitting. We provide an end-to-end, reproducible workflow (data ingestion, normalization, feature lagging, hyperparameter sweeps, and metrics) suitable for deployment in monitoring pipelines and for extension to multivariate inputs (e.g., pressure corrections, geomagnetic indices). This work demonstrates QRC as a feasible near-term quantum/hybrid tool for HEP-adjacent time-series workloads emphasizing efficiency and maintainability.