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This collaboration marks a significant milestone in applying hybrid quantum-classical machine learning to utility-scale energy forecasting using NISQ hardware with over 100 qubits. The study validates the potential of current-generation IBM Quantum processors to model complex, correlated electricity demand patterns without requiring fault-tolerant systems yet. For the industry, this demonstrates practical near-term applicability of QML in optimizing smart grid operations and load balancing.
WISER and E.ON Benchmark Quantum Machine Learning for Smart Grid Energy Forecasting
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) [...] The post WISER and E.ON Benchmark Quantum Machine Learning for Smart Grid Energy Forecasting appeared first on 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…
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