Optimal quantum reservoir computing for market forecasting: An application to fight food price crises
Laia Domingo, M. Grande, Gabriel G. Carlo, F. Borondo, J. Borondo · Engineering Applications of Artificial Intelligence · 2026
The emerging technology of Quantum Reservoir Computing (QRC) stands out in the noisy-intermediate scale quantum era for its efficiency, adaptability, and compatibility with current quantum hardware. By harnessing quantum dynamics for feature extraction, QRC offers a promising alternative for complex forecasting tasks — such as predicting agri-commodity price fluctuations — central to addressing food price crises and supply chain sustainability. In this work, we systematically investigate the design of quantum reservoirs and identify circuit complexity, quantified via the majorization criterion, as a practical indicator of forecasting performance. Unlike alternative complexity measures that require full state tomography or time-reversed dynamics, majorization can be evaluated from measurement statistics alone, making it particularly suited for near-term quantum devices. We demonstrate that quantum reservoirs built from a universal gate family composed of Hadamard, controlled-NOT, and T gates achieve strong predictive accuracy while requiring significantly fewer gates than the widely used transverse-field Ising model. Our results show that quantum reservoirs outperform deep learning and statistical baselines, and achieve comparable, slightly superior, performance to classical reservoir computing—an improvement consistent with the compact model structure of QRC, which, with significantly fewer trainable parameters, is better suited to prevent overfitting in data-scarce regimes. We further explore robustness under realistic noise conditions, showing that certain types of quantum noise, such as amplitude damping, can even enhance performance at low error rates. These findings suggest that QRC is a promising tool for time series prediction in data-limited, high-impact domains.