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E.ON, Qoro and the UK National Quantum Computing Centre developed a proof of concept that combines quantum hardware with classical computing to optimise peer-to-peer energy transaction scheduling. The team reports lower residual decomposition error than classical baselines and about threefold lower quantum hardware execution time, but the work has not been demonstrated on an operational power grid.
E.ON, Qoro and the UK National Quantum Computing Centre have developed a proof of concept that uses hybrid quantum-classical computing to optimise peer-to-peer energy transaction scheduling. The team says its approach reduced residual decomposition error compared with classical baselines and cut quantum hardware execution time by about threefold, but it has not been tested as a live grid deployment.
The project used Qoro’s Divi software development kit and scheduler to split large grid optimisation problems into smaller subproblems of up to 50 qubits. The fragments were run on IBM quantum hardware and recombined using classical feasibility repair. The team also used Qoro’s Maestro simulation engine for large-scale tensor network simulations before hardware testing.
According to the project announcement, QAOA sampling combined with classical feasibility repair produced lower residual decomposition error than standard classical baselines on energy-grid benchmark models. Spatial circuit packing on IBM hardware cut execution time by approximately threefold. The release does not provide a wall-clock time, detailed error values or a comparison across different hardware providers.
The researchers used spectral graph partitioning to divide the optimisation workload. Their material gives the PEGASE-1354 bus benchmark as an example of a realistic grid model that typically requires 1,710 qubits, beyond the capacity of today’s commercial quantum computers. That figure describes the model’s stated requirement; the proof of concept instead processed smaller partitions and does not establish that the full model was solved end to end on quantum hardware.
Implications for Local Energy Trading
The work addresses a practical computing challenge for peer-to-peer electricity markets, where households and businesses may both consume and produce power. Coordinating transactions among these “prosumers” involves changing levels of generation and demand, as well as prices and other constraints. The project’s proposed method aims to make such scheduling problems more manageable by dividing them into smaller pieces.
If further testing supports the reported results, reducing the number of decomposition steps could help shorten the rounds needed to coordinate transactions. That may be relevant as distributed renewable generation grows and local energy trading becomes more complex. However, the announcement’s claims about easing grid strain, reducing losses and improving resilience are potential applications, not measured outcomes from a field deployment. The current evidence is a benchmark-based proof of concept.
The project also illustrates a near-term role for quantum computing that does not require a large, fault-tolerant machine: use available quantum hardware for parts of a larger workflow and classical methods for partitioning and repair. The result is relevant to energy researchers and utilities, but it does not show that quantum computing is already a cheaper, faster or more reliable replacement for conventional scheduling systems.
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From Grid Research to Hardware Tests
The collaboration received SparQ grant funding and was conducted under oversight from the UK’s Science and Technology Facilities Council Hartree Centre and the National Quantum Computing Centre through the SparQ 2025 programme. The programme supports industry-led quantum computing projects focused on practical applications. The announcement says earlier research by E.ON and the Hartree Centre helped prepare for this work.
The project tackles a mismatch between the scale of grid optimisation models and the size of current quantum processors. Its method uses classical techniques to partition the larger problem, runs smaller pieces on IBM hardware and then applies classical processing to repair feasibility. The researchers say the full findings are described in a paper posted on arXiv, a preprint repository; the supplied material does not establish that the paper has undergone peer review.
““Efficient scheduling is key to scalable peer-to-peer energy markets.””
— Dr Corey O’Meara, chief quantum scientist at E.ON
Limits of the Proof of Concept
The results remain a proof of concept, and the announcement does not report a trial on an operational electricity network or a live peer-to-peer energy market. It is not clear how the method would perform against production scheduling systems when confronted with real-time grid data, operating constraints and changing demand.
The available account also leaves open how the reported error reduction was measured, the size of the benchmark comparisons, and whether the approximately threefold execution-time reduction includes the full workflow or only quantum hardware execution. No costs, energy use, reliability figures or commercial deployment timetable are provided. The linked arXiv paper is identified as a recent publication, but its peer-review status is not specified in the source material.
Research and Open-Source Follow-Up
The research paper is available on arXiv, where readers can examine the method and benchmark results in greater detail. Qoro CEO Dan Holme said the company plans to contribute several tools developed during the project to the open-source Divi SDK. The announcement does not give a release date or identify which tools will be published.
The next evidence needed is further testing that clarifies performance across larger or more operationally representative scheduling tasks, alongside transparent comparisons with classical methods. E.ON, Qoro and the NQCC have not announced a timetable for a grid trial or commercial rollout in the supplied material.
Key Questions
What did E.ON and Qoro test?
They tested a hybrid quantum-classical approach for optimising peer-to-peer energy transaction scheduling, with the UK National Quantum Computing Centre involved in the project.
Was the method used on a live power grid?
No live-grid deployment is reported. The work is described as a proof of concept using energy-grid benchmark models and IBM quantum hardware.
What results did the team report?
The team reported lower residual decomposition error for QAOA sampling combined with classical feasibility repair than for classical baselines, and about threefold lower quantum hardware execution time through spatial circuit packing. Detailed timing and error values are not included in the supplied announcement.
Why split the problem into smaller parts?
The project says a PEGASE-1354 grid model typically calls for 1,710 qubits, beyond current commercial quantum hardware capacity. Dividing the workload into subproblems of up to 50 qubits allowed the team to test fragments on available processors and recombine them with classical methods.
What happens next?
The research is available on arXiv, and Qoro says it plans to release some project tools through its open-source Divi SDK. No operational trial or commercial rollout schedule has been announced in the provided material.
Source: thequantuminsider.com
