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E.ON, Qoro and the UK National Quantum Computing Centre developed a proof of concept using hybrid quantum-classical methods to schedule peer-to-peer energy transactions. The team says its approach reduced hardware execution time by about threefold and lowered residual decomposition error against classical baselines, but it has not been tested in 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 schedule peer-to-peer energy transactions. The team says its approach split large grid optimisation problems into subproblems of up to 50 qubits and cut quantum hardware execution time by about threefold on IBM hardware; it has not demonstrated the system in an operational power grid.
The project combined Qoro’s open-source Divi SDK with the company’s scheduler to decompose grid optimisation tasks, run smaller problem fragments, then assemble results using classical processing. The work received funding through the UK’s SparQ 2025 programme and was conducted with oversight from the STFC Hartree Centre and the NQCC, according to the project announcement.
The team used spectral graph partitioning to divide large optimisation problems into subgrids. It prototyped the approach using Qoro’s Maestro simulation engine and then tested fragments on IBM quantum hardware. The project report says sampling with the Quantum Approximate Optimisation Algorithm (QAOA), followed by classical feasibility repair, produced lower residual decomposition error than standard classical baselines. The report also describes approximately threefold lower hardware execution time through spatial circuit packing.
The source material says a realistic model based on the PEGASE-1354 bus benchmark typically requires 1,710 qubits, beyond the capacity of current commercial quantum computers. In this project, decomposition brought individual subproblems down to as many as 50 qubits. Those figures describe the benchmark and the project’s partitioned workload, not a live grid deployment or a measured change in real-world energy scheduling.
A Test for Local Energy Trading
As more households and businesses generate electricity as well as consume it, operators may need to coordinate peer-to-peer energy transactions across changing supply, demand and prices. Scheduling such exchanges can involve many interacting variables. The project tests whether splitting that optimisation workload and combining quantum and classical computation can improve how candidate schedules are produced.
The reported performance figures are technical results on benchmark models, not evidence of lower bills, reduced grid losses or improved reliability in practice. If the method proves useful beyond the proof-of-concept stage, it could offer energy companies another way to investigate complex scheduling problems while quantum hardware remains limited. For now, the value is in showing a possible workflow for research and testing, rather than an available grid-management product.
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From Grid Models to Hardware
The collaboration builds on earlier work by E.ON and the Hartree Centre, according to the project materials. Its focus is the rise of energy “prosumers”: customers that both use and produce electricity, often through technologies such as distributed clean-energy generation. Coordinating transactions between those participants is one potential use for optimisation tools.
The team’s strategy addresses a present hardware constraint. Instead of running a model requiring 1,710 qubits as one quantum computation, it partitioned the workload and used classical computation to help manage and recombine the smaller pieces. The project says this reduced the solution decomposition length, which it relates to the number of scheduling rounds in a prospective energy-trading application. That relationship remains a model-based result, not a report of fewer rounds in an operating market.
““Efficient scheduling is key to scalable peer-to-peer energy markets.””
— Dr Corey O’Meara, chief quantum scientist at E.ON
No Live Grid Trial Yet
The reported results remain a proof of concept. The project materials do not describe a trial scheduling actual transactions in an operating power grid, nor do they provide evidence of effects on energy costs, grid losses, reliability or the time needed to settle real transactions. The reported threefold execution-time reduction is tied to the project’s hardware tests and spatial circuit packing; its precise measurement conditions and broader comparability are not detailed in the supplied material.
It is also not clear from the announcement how the method would perform across different grid models, changing real-time conditions or larger numbers of participants. The source says tools are planned for contribution to the open-source Divi ecosystem, but does not specify which tools will be released or when. Further independent testing would be needed to establish whether the benchmark results translate to practical scheduling benefits.
Research and Tool Releases
The team points to a research paper on arXiv describing the work. Qoro CEO Dan Holme said the company plans to contribute several project tools to the Divi open-source ecosystem, although a release schedule was not provided. The project’s next meaningful test would be evaluation on additional models or an energy-market setting that reflects real operating constraints.
For now, the companies have reported a technical demonstration rather than announced a commercial deployment. Further information on reproducibility, performance across benchmarks and any plans for grid trials will clarify whether the approach can move from partitioned quantum computations toward use by energy operators.
Key Questions
What did E.ON and Qoro test?
They tested a hybrid quantum-classical method for peer-to-peer energy transaction scheduling. It divides large optimisation problems into smaller parts, runs fragments on quantum hardware and uses classical processing to repair and recombine results.
Was the system used to manage a live power grid?
No live-grid deployment is described. The work is reported as a proof of concept using benchmark models, simulation and tests on IBM quantum hardware.
What does the threefold result refer to?
The project report says spatial circuit packing reduced quantum hardware execution time by approximately threefold in its tests. It does not establish a threefold improvement in real-world energy scheduling or grid operations.
Why was the problem divided into smaller pieces?
The project materials say the PEGASE-1354 benchmark model typically requires 1,710 qubits, more than current commercial quantum computers can handle as a single problem. Decomposition produced fragments of up to 50 qubits for testing.
What happens next?
Qoro says it plans to contribute several tools from the project to its open-source Divi SDK. No release date or operational grid trial has been announced in the supplied material.
Source: thequantuminsider.com
