Multivariate Time Series Forecasting with Adaptive Non-Local Observables

A new hybrid model called MTSF-ANO, integrating variational quantum circuits with adaptive non-local observables, ranks first or second in mean squared error in 17 of 20 settings on the four ETT datasets, improving over the strongest baseline by up to 20% on ETTh1, according to a preprint on arXiv (2607.24399v1). The model outperforms or matches its fixed local observable counterpart across all settings, suggesting adaptive non-local observables are a promising direction for quantum time series forecasting.

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.