Testing Quantum–Classical Hybrid Optimization for Industrial-Scale Power Operations
2026/8/4Through the UK National Quantum Computing Centre (NQCC) SparQ programme, JIJ Europe worked with ORCA Computing, NQCC and bp to evaluate the performance and future potential of quantum–classical hybrid optimization on a large-scale power-operations problem: unit commitment.
What is the unit commitment problem?
The unit commitment problem determines which power-generation assets should start or stop, when they should do so, and at what output levels, while ensuring that electricity demand is met. It must account for many constraints at once, including demand forecasts, generation costs, start-up and shut-down costs, equipment performance and operating reserves. As power systems become more complex with the growth of renewable energy and distributed generation, computational methods that can produce better plans within practical timeframes are increasingly important.
A hybrid quantum–classical approach
For this project, JIJ used a decomposition method it developed to divide a large mixed-integer linear programming problem into multiple smaller problems. The combinatorial optimization component governing generator start-up and shut-down decisions was converted into QUBO form and run on ORCA Computing’s photonic quantum processor. Continuous variables such as generation output were handled by classical computers, and the results were then combined to construct a solution to the full problem.
JIJ Europe led the project and developed the decomposition algorithm and optimization workflow. The workflow was integrated with ORCA Computing’s photonic quantum system and evaluated in NQCC’s testbed environment. bp participated as an observer, contributing energy-industry expertise.
Benchmarking at industrial scale
The evaluation used benchmark data whose industrial relevance had been reviewed by bp’s digital R&D team. The largest problem contained 25,755 variables and 48,939 constraints. The quantum–classical hybrid workflow generated solutions using a real quantum processor, and those solutions were verified as feasible for the original problem.
For short runtimes on the current PT-2 system, Gurobi performed better. The hybrid approach, however, continued to improve its solutions as runtime increased and ultimately produced competitive solution quality under the conditions tested. Results for the next-generation PT-3 system are performance projections based on current measurements.
These findings do not establish commercial quantum advantage or readiness for operational deployment. Rather, the industrial-scale benchmarks show what today’s hybrid approach can achieve and clarify the remaining challenges, including runtime and system architecture.
Toward practical deployment
The project also examined the hardware and software requirements needed to scale quantum–classical hybrid optimization to larger problems. JIJ will continue testing on real hardware and comparing results with classical methods to assess practical opportunities in the energy sector.
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