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E.ON, Qoro and the UK National Quantum Computing Centre developed a proof of concept using hybrid quantum-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, but has not demonstrated the method in an operational power grid.
E.ON, Qoro and the UK National Quantum Computing Centre have developed and tested a hybrid quantum-classical method for scheduling peer-to-peer energy transactions, reporting lower residual decomposition error than classical baselines and roughly threefold lower quantum hardware execution time. The work was tested on IBM quantum hardware, but remains a proof of concept, not a deployment in an operating power grid.
The project used Qoro’s Divi software development kit and scheduler to divide large grid optimisation problems into smaller subproblems of up to 50 qubits. The team first prototyped the approach with Maestro, Qoro’s simulation engine, then ran fragments on IBM quantum hardware and combined the results using a classical feasibility-repair step. The project was conducted with oversight from the STFC Hartree Centre and NQCC through the UK’s SparQ 2025 programme.
According to the project report, sampling with the Quantum Approximate Optimisation Algorithm (QAOA), followed by classical feasibility repair, produced lower residual decomposition error than the classical baselines used in the tests. The team also reported that spatial circuit packing reduced quantum hardware execution time by approximately threefold. These are results from the project’s benchmark and proof-of-concept work; they do not establish performance or cost savings in live energy-market operations.
The report gives a sense of the scale challenge: a realistic grid model based on the PEGASE-1354 bus benchmark typically requires 1,710 qubits, while the tested method broke the workload into pieces of up to 50 qubits. The authors describe decomposition length as corresponding to scheduling rounds for coordinating transactions. Their finding is that the approach reduced residual error and hardware execution time in the tested setup; the source does not provide a field trial, commercial rollout date or quantified reduction in actual grid scheduling rounds.
A Smaller Route to Grid Scheduling
Peer-to-peer energy markets depend on coordinating electricity transfers among households and businesses that may both produce and consume power. Scheduling such transactions involves changing generation, consumption, prices and demand. The project addresses a practical computing problem: how to handle a large optimisation task when it exceeds the scale of currently available quantum hardware.
The reported method combines classical computing and quantum processing rather than asking a quantum processor to solve the entire grid model. If later research validates the results in more realistic settings, decomposing a workload into smaller pieces could offer a way to test quantum-assisted optimisation without requiring a processor capable of handling the full problem at once. For now, the findings concern benchmark performance; they do not show that power companies can already schedule real transactions faster or at lower cost using the method.
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From Grid Benchmarks to Hardware Tests
The work builds on earlier research by E.ON and the Hartree Centre, according to the project materials. It was carried out with funding from the SparQ programme, a UK initiative supporting industry-led quantum computing projects, and oversight from the STFC Hartree Centre and the NQCC. The research is also described in a paper posted to arXiv.
Peer-to-peer trading is relevant as more homes and businesses gain technologies that let them generate electricity as well as use it. The project report says that coordinating local transactions can be difficult for classical optimisation because the number of combinations and changing variables grows quickly. Its proposed response is spectral graph partitioning: divide the grid optimisation problem into subgrids, process the fragments, and use classical methods to repair feasibility and assemble a solution.
““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 report does not describe a trial on an operational power grid or a live peer-to-peer market. It also does not specify the size of the test datasets, the full numerical residual-error results, or the measurement details behind the approximately threefold hardware execution-time reduction. The comparison is limited to the project’s stated benchmarks and classical baselines.
It remains unclear how the approach would perform with real-time grid data, changing market conditions, operational reliability requirements or the costs of running the full workflow. The project materials describe potential applications and a near-term research pathway, but provide no deployment schedule or independently verified estimate of savings, reduced grid strain or improved resilience.
Open Tools and Further Testing
Qoro said it plans to contribute several tools developed during the project to its open-source Divi SDK. That may let researchers and practitioners examine or extend the software, though the materials do not give a publication timetable for those contributions. The full research is available as an arXiv paper.
The next evidence needed is testing beyond benchmark models: results on broader workloads, clearer reporting of accuracy and execution-time measurements, and trials that account for the data and constraints of actual energy scheduling. Until such work is reported, the result should be read as a technical demonstration of a hybrid workflow, not proof that quantum computing is ready to run peer-to-peer energy markets.
Key Questions
What did E.ON and Qoro test?
They tested a hybrid quantum-classical workflow for dividing and solving parts of peer-to-peer energy transaction scheduling problems, using Qoro software and IBM quantum hardware.
Did the team use a quantum computer to solve an entire grid model?
No. The project report says a PEGASE-1354 benchmark model typically requires 1,710 qubits. The team partitioned the workload into subproblems of up to 50 qubits, then used classical feasibility repair to combine results.
What results did the project report?
The team reported lower residual decomposition error than its classical baselines when using QAOA sampling with classical feasibility repair, and about threefold lower quantum hardware execution time through spatial circuit packing. These findings apply to the reported tests, not a live grid deployment.
Is the method being used in a real power grid?
The source describes the work as a proof of concept and does not report operational deployment or a field trial. Whether it can meet real-world scheduling requirements remains unestablished.
What happens next?
Qoro says it plans to release several project tools through its open-source Divi SDK. Further testing and published results would be needed to assess performance on operational energy scheduling problems.
Source: thequantuminsider.com
