Qoro, E.ON, and the UK National Quantum Computing Centre (NQCC) have successfully developed a proof-of-concept, exploring future applications of hybrid quantum-classical computing in peer-to-peer energy trading. With SparQ grant funding, the team developed a new decomposition-based optimization pipeline, built using Qoro’s open-source software development kit (SDK), Divi, and orchestrated through Qoro’s scheduler. The resulting proof-of-concept improved upon previous hybrid methods using energy grid benchmark models, outperforming classical baselines. It also reduced hardware execution time by approximately 3x through spatial circuit packing on IBM quantum hardware. This proves that software orchestration can unlock utility-scale applications on today’s quantum processors.
By mathematically breaking the massive grid optimization problem into smaller, more manageable pieces, the algorithm could reduce the energy transaction scheduling rounds (by reducing the solution decomposition length) to achieve high solution accuracy. In a real-world application, the solution decomposition length acts as ‘scheduling rounds’ that plan and coordinate energy transactions between grid users. By proving that the number of required rounds can be drastically reduced, these findings offer a near-term pathway for power grids to more efficiently manage complex energy transaction scheduling and ease grid strain.
This project was conducted with oversight from the STFC Hartree Centre and the NQCC through the SparQ 2025 program, a UK national initiative supporting industry-led quantum computing projects focused on real-world applications. Earlier research by E.ON and the Hartree Centre laid the groundwork for this collaboration.
The Rise of the Energy Prosumer
Many households and businesses now both consume and produce electricity, thanks to innovations in clean energy technologies. These “prosumers” are creating a need for coordinated peer-to-peer energy transactions, where excess electricity can be shared on a local basis. However, efficiently scheduling these transactions on classical computers is challenging due to combinatorial complexity and the high number of constantly fluctuating variables, such as production and consumption rates, price, and demand.
To solve this, Qoro and E.ON, one of Europe’s largest energy companies, set out to investigate whether hybrid computing techniques could improve the efficiency of large-scale peer-to-peer energy transaction scheduling. The results demonstrated that Quantum Approximate Optimization Algorithm (QAOA) sampling with classical feasibility repair achieved lower residual decomposition error than standard classical baselines.
“Efficient scheduling is key to scalable peer-to-peer energy markets,” said Dr. Corey O’Meara, Chief Quantum Scientist at E.ON. “Our new approach removes a major technical barrier, letting organizations start with classical or simulated quantum methods and transition seamlessly to real quantum hardware as it advances.”
Breaking the Quantum Hardware Limit
To achieve these results, the project used spectral graph partitioning techniques to break large grid optimization problems into smaller subgrids. These were prototyped using Maestro, Qoro’s unified simulation engine, to perform large-scale tensor network simulation, then tested on quantum hardware.
Crucially, modeling a realistic power grid based on the PEGASE-1354 bus benchmark typically requires 1,710 qubits, which is far beyond the capacity of today’s commercial quantum computers. However, using Qoro’s Divi SDK and scheduler, the team intelligently partitioned the workload into subproblems of up to 50 qubits, executed the fragments across IBM quantum hardware, and pieced everything back together using classical feasibility repair. Through this approach, they proved that today’s quantum hardware, combined with problem decomposition, can contribute meaningfully to problems of this scale.
“Working with E.ON,” said Dan Holme, CEO of Qoro Quantum, “we’ve shown that by breaking large problems into smaller pieces, the Qoro platform enables practical optimization for renewable energy grids, reducing losses and improving resilience, creating a clear pathway for near-term quantum adoption. We plan to contribute several of the tools developed during this project back to the open-source ecosystem through our Divi SDK, enabling other researchers and practitioners to build on this work.”