WISER and E.ON Benchmark Quantum Machine Learning for Smart Grid Energy Forecasting - Quantum Computing Report

The Washington Institute for STEM, Entrepreneurship and Research (WISER) and European energy giant E.ON have completed a research collaboration evaluating hybrid quantum-classical machine learning (QML) for multi-output time-series electricity demand forecasting. Published on arXiv, the joint project demonstrated utility-scale experiments running on IBM Quantum hardware with over 100 qubits, testing whether Noisy Intermediate-Scale Quantum (NISQ) devices can model complex, correlated customer consumption patterns. Algorithmic Architectures: KQRC-RM and QGP Forecasting electrical load across multiple interconnected households is challenging for classical statistical methods due to cross-stream correlations, weather-driven nonlinearities, and multi-scale seasonality. To address this multi-series forecasting challenge, the researchers designed two distinct, [...]

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