A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.
D., Temme, K., Harrow, A
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.