Quantum-classical hybrid models based on error correction for time series forecasting
Introduces quantum models into established error-correction hybridization schemes for time series forecasting, potentially improving predictive performance.
Proposed first quantum-classical hybrid forecasting system based on error correction, where quantum models extract patterns and classical models capture remaining patterns from quantum errors. Achieved best results in most problems compared to classical single models and classical-classical hybrids.
Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concurrently, quantum models are providing a means to augment the classical capacity, including in time series forecasting, by acting alongside classical models in hybrid architectures. In this work, we propose the first forecasting system based on error correction that jointly uses quantum and classical models. Here, quantum models first extract patterns by exploring quantum phenomena, and classical models capture the remaining patterns from the quantum errors. Compared to classical single models and classical-classical hybrid models based on error correction, the complementary capacity that emerges from this quantum-classical system provided the best results in most of the addressed problems. Therefore, this work paves the way to introduce quantum models in established hybridization schemes for time series forecasting.