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E.ON, Qoro and the UK National Quantum Computing Centre developed a proof of concept that combines quantum and classical computing to schedule peer-to-peer energy transactions. The team reports lower residual decomposition error than classical baselines and about threefold lower quantum-hardware execution time through circuit packing; the work has not been demonstrated in an operational power grid.
E.ON, Qoro and the UK National Quantum Computing Centre have developed and tested a proof of concept that uses hybrid quantum-classical computing to schedule peer-to-peer energy transactions. The project reports lower residual decomposition error than classical baselines and about threefold lower quantum-hardware execution time through circuit packing on IBM hardware, but it has not demonstrated the method in an operational electricity grid.
The team used Qoro’s Divi software development kit and scheduler to divide large grid optimisation problems into smaller subproblems of up to 50 qubits. The project said a realistic model based on the PEGASE-1354 bus benchmark would typically require 1,710 qubits if treated as one problem, beyond the capacity of current commercial quantum computers. Decomposition let the researchers run fragments on IBM quantum hardware and combine results using classical processing.
The approach used spectral graph partitioning to split the optimisation task into subgrids. The researchers first prototyped the work with Maestro, Qoro’s simulation engine, including large-scale tensor network simulation, before testing fragments on hardware. They report that QAOA sampling with classical feasibility repair produced lower residual decomposition error than standard classical baselines. The source also reports an approximately threefold reduction in hardware execution time from spatial circuit packing; it does not provide a deployment result or an operational-grid performance measure.
The project received SparQ grant funding and was conducted under oversight from the Science and Technology Facilities Council’s Hartree Centre and the NQCC through the SparQ 2025 programme. The findings are described in a recent paper on arXiv. The source characterises the work as a proof of concept, not a commercial service or a live trial with energy customers.
Testing Quantum Scheduling at Smaller Scale
Peer-to-peer energy markets could require coordinating electricity transactions among households and businesses that both consume and generate power. The project’s immediate relevance is technical: it tests whether a large optimisation problem can be divided into manageable pieces and partly processed by quantum hardware, rather than requiring a single quantum computer large enough to handle the entire model.
The reported reductions in decomposition error and hardware execution time are research results, not evidence that electricity scheduling is already faster, cheaper or more reliable in practice. If further tested, decomposition could offer a way to investigate quantum processors on problems that exceed their present size limits. Operational value remains unproven: the report does not establish effects on grid costs, reliability, renewable-energy use or transaction volumes.
The work also bears on how near-term quantum applications are developed. Its hybrid design assigns some tasks to classical software and uses quantum hardware for selected subproblems. That makes the test relevant to organisations exploring quantum computing without assuming that current processors can independently solve full-scale grid optimisation tasks.
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From Grid Benchmarks to Hardware Tests
As more homes and businesses install technologies that let them produce as well as consume electricity, local energy trading may involve many changing variables, including generation, consumption, prices and demand. The project’s source describes this as a scheduling challenge with combinatorial complexity. Its focus was peer-to-peer transaction scheduling, rather than the operation of a complete electricity system.
The researchers drew on earlier work by E.ON and the Hartree Centre, then extended the investigation with Qoro and the NQCC through the SparQ 2025 programme, a UK initiative supporting industry-led quantum computing projects. They used benchmark models and a staged workflow: partition the optimisation problem, simulate and prototype the pieces, run fragments on hardware, then use classical feasibility repair to assemble a solution.
The team’s framing is that reducing the number or length of decomposition rounds could make scheduling more manageable. Those rounds are part of the model and should not be mistaken for proven reductions in real-world grid scheduling time. The reported hardware test establishes that small fragments were run; it does not show that a utility adopted the pipeline.
““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 available account does not establish whether the method has been tested with live grid data, customers or operational energy transactions. It also does not give enough detail to assess how the reported error and execution-time results compare across different hardware settings, problem sizes or scheduling conditions. The approximately threefold figure concerns quantum-hardware execution time through spatial circuit packing, not an overall threefold improvement in grid operations.
The source says the method outperformed classical baselines on benchmark models, but does not provide their full specification or quantify the results in the supplied report. It is also unclear how the workflow would perform as the number of subproblems grows, or what costs, communication requirements and reliability constraints would arise in a utility deployment. The researchers’ claims about easing grid strain and improving resilience remain potential outcomes, not demonstrated findings.
Research and Open-Source Follow-Up
The project’s research is available as a paper on arXiv, where readers can look for methodological details and results beyond the summary reported here. Qoro’s CEO said the company plans to contribute several project tools to the open-source Divi SDK; a release date and list of tools have not been specified in the source.
The next evidence needed would include fuller benchmark comparisons and tests showing whether the approach remains useful as problem size and operational constraints increase. Further work would also need to establish how the method performs against established scheduling systems using real-world conditions. Until such results are reported, the work should be read as an investigation of a possible quantum-assisted workflow, not a demonstrated grid deployment.
Key Questions
What did E.ON and Qoro test?
They tested a hybrid quantum-classical method for peer-to-peer energy transaction scheduling. The approach splits large optimisation problems into smaller pieces, runs fragments on IBM quantum hardware and uses classical processing to combine and repair the results.
Was the system used in a live electricity grid?
No live-grid deployment is reported. The work is described as a proof of concept using benchmark models and quantum hardware, not as a trial scheduling actual customer transactions.
What performance results did the project report?
The team reports lower residual decomposition error for QAOA sampling with classical feasibility repair than for standard classical baselines. It also reports about threefold lower quantum-hardware execution time through spatial circuit packing. These results do not establish a comparable improvement in real-world grid operations.
Why did the researchers split the problem?
The source says a PEGASE-1354 benchmark model would typically require 1,710 qubits if handled as one optimisation problem, while the project tested subproblems of up to 50 qubits. Splitting the workload made it possible to test fragments on current quantum hardware.
What happens after the proof of concept?
Qoro’s CEO said the company plans to release several tools developed during the project through its open-source Divi SDK. The source gives no release date, and it does not announce a grid deployment or a schedule for further testing.
Source: thequantuminsider.com
