Quantum

E.ON and Qoro Test Hybrid Quantum Computing for Energy Scheduling

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 29/10/2026, 11:49:24 UTC.

Briefings

E.ON, Qoro, and the UK National Quantum Computing Centre developed a proof-of-concept using hybrid quantum-classical computing to optimize peer-to-peer energy trading. The team used Qoro's Divi SDK and scheduler to decompose large grid problems into subproblems of up to 50 qubits, reducing execution time by approximately threefold on IBM hardware. QAOA sampling combined with classical feasibility repair reduced residual decomposition error compared to classical baselines, though the results remain a proof of concept rather than a deployed operational solution. The project utilized the PEGASE-1354 bus benchmark, which typically requires 1,710 qubits, by intelligently partitioning the workload into manageable fragments.

Key points

• The project successfully reduced quantum hardware execution time by approximately threefold through spatial circuit packing on IBM quantum hardware using the Divi SDK.
• QAOA sampling combined with classical feasibility repair achieved lower residual decomposition error than standard classical baselines for the PEGASE-1354 bus benchmark.

Context

This research addresses the challenge of scheduling peer-to-peer energy transactions in a prosumer grid, where combinatorial complexity and fluctuating variables make classical optimization difficult. The project utilized the SparQ 2025 programme and STFC Hartree Centre oversight to validate that software orchestration can unlock utility-scale applications on current quantum processors.

Limitations

The findings are limited to a proof-of-concept phase and do not constitute a demonstrated deployment in an operational power grid. The benchmark model PEGASE-1354 requires 1,710 qubits, far exceeding current commercial quantum computer capacity, meaning the results rely on simulated tensor network approximations rather than full-scale grid simulation.

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