Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting

• URL: http://arxiv.org/abs/2511.17698v1
• Date: Fri, 21 Nov 2025 18:36:25 GMT
• Title: Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting
• Authors: Nawfel Mechiche-Alami, Eduardo Rodriguez, Jose M. Cardemil, Enrique Lopez Droguett,
• Abstract summary: This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting.<n>Each signal is windowed, amplitude-encoded, transformed by a QFT, then passed through a protective rotation layer to avoid the QFT/QFT adjoint cancellation.<n>Exogenous predictors are incorporated by convexly fusing feature-specific kernels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Each signal is windowed, amplitude-encoded, transformed by a QFT, then passed through a protective rotation layer to avoid the QFT/QFT adjoint cancellation; the resulting kernel is used in kernel ridge regression (KRR). Exogenous predictors are incorporated by convexly fusing feature-specific kernels. On multi-station solar irradiance data across Koppen climate classes, the proposed kernel consistently improves median R2 and nRMSE over reference classical RBF and polynomials kernels, while also reducing bias (nMBE); complementary MAE/ERMAX analyses indicate tighter average errors with remaining headroom under sharp transients. For both quantum and classical models, the only tuned quantities are the feature-mixing weights and the KRR ridge alpha; classical hyperparameters (gamma, r, d) are fixed, with the same validation set size for all models. Experiments are conducted on a noiseless simulator (5 qubits; window length L=32). Limitations and ablations are discussed, and paths toward NISQ execution are outlined.

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