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This research introduces MTSF-ANO, a hybrid model combining variational quantum circuits with adaptive non-local observables to enhance multivariate time series forecasting accuracy. By addressing the expressivity limitations of fixed local measurements in current quantum neural networks, the study demonstrates significant performance gains on standard ETT datasets compared to both classical baselines and previous quantum approaches. These findings highlight a promising architectural direction for leveraging quantum advantages in practical machine learning applications involving temporal data.
Multivariate Time Series Forecasting with Adaptive Non-Local Observables
arXiv:2607.24399v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity. We propose MTSF-ANO, a simple hybrid model for MTSF that integrates variational quantum circuits with adaptive non-local observables (ANO). On the four ETT datasets, MTSF-ANO ranks first or second in MSE in 17 of 20 settings, improving over the strongest baseline by up to 20% on ETTh1, and outperforms or matches its fixed local observable counterpart across all settings. Our ablations show how the quantum circuit design and ANO non-locality affect performance. These results suggest that ANO is a promising direction for quantum time series forecasting.
Abstract:Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity. We propose MTSF-ANO, a simple hybrid model for MTSF that integrates variational quantum circuits with adaptive non-local observables (ANO). On the four ETT datasets, MTSF-ANO ranks first or second in MSE in 17 of 20 settings, improving over the strongest baseline by up to 20% on ETTh1, and outperforms or matches its fixed local observable counterpart across all settings. Our ablations show how the quantum circuit design and ANO non-locality affect performance. These results suggest that ANO is a promising direction for quantum time series forecasting.
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2607.24399 [quant-ph]
(or arXiv:2607.24399v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.24399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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