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This research aims to enhance power grid simulation efficiency through optimal graph partitioning via quantum annealing.

January 18, 2026Open Access

Quantum Annealing Based Power Grid Partitioning for Parallel Simulation

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This research aims to enhance power grid simulation efficiency through optimal graph partitioning via quantum annealing.
Used quantum annealing to achieve optimal partitioning of a power grid graph.
Mapped partitioning requirements to a quadratic unconstrained binary optimization (QUBO) formulation.
Tested the QA approach on a current D-Wave quantum processing unit (QPU).
Discussed limitations of QUBO embedding impacting problem size and solution time.
Achieved optimal graph partitioning for grid simulations with reduced idle times.
Demonstrated effective use of quantum annealing for partitioning problems but highlighted limitations in embedding.
Limited current implementations to under 200 buses due to hardware constraints.

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Graph partitioning optimizes simulation times in power systems, indicating the need for improved quantum solutions.

Hartmann et al. (2025) studied this question.

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