🤖 AI Summary
This work addresses the limitations of single models and classical hybrid approaches in capturing complex temporal patterns by proposing the first quantum-classical hybrid forecasting system based on an error correction mechanism. The method leverages a quantum model to capture high-dimensional nonlinear features in time series data, while a classical model explicitly learns the residual errors from the quantum predictions, enabling complementary and synergistic interaction between the two components. By integrating quantum machine learning into a classical error correction framework for the first time, the proposed system demonstrates significant performance gains over purely classical models and classical-classical hybrid architectures across multiple time series forecasting benchmarks, thereby validating the efficacy and superiority of quantum-classical collaboration in predictive modeling.
📝 Abstract
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.