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This research introduces the first forecasting system leveraging error correction to jointly utilize quantum and classical models for time series prediction. By having quantum components extract specific patterns while classical models address residual errors from the quantum output, this hybrid architecture outperforms both standalone and purely classical-hybrid approaches on most tested problems. The study establishes a viable pathway for integrating quantum capabilities into established machine learning pipelines without requiring full-scale fault-tolerant hardware immediately.
Quantum-classical hybrid models based on error correction for time series forecasting
arXiv:2606.15213v1 Announce Type: cross 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.
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.
Submitted to Nature Computational Science. 24 pages, 10 figures
Quantum Physics (quant-ph); Machine Learning (cs.LG)
arXiv:2606.15213 [quant-ph]
(or arXiv:2606.15213v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.15213 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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