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The work is a hybrid quantum-classical method: shallow QAOA runs on subproblems split from a 1,710-variable grid benchmark, with classical stitching and repair. The reported ~3× reduction in quantum execution time is a relative figure from a preprint, not a demonstrated advantage over classical solvers. For quantum readers, the useful takeaways are the decomposition strategy under the 50-qubit limit and the offline-calibrated fixed QAOA angles, which avoid runtime parameter tuning.
E.ON, Qoro Quantum, and NQCC Scale Constrained Optimization on IBM Heron QPUs via Distributed QAOA and Spatial Packing
E.ON, Qoro Quantum, and NQCC have successfully demonstrated a distributed hybrid optimization pipeline that reduced quantum execution time by approximately three times on IBM Quantum hardware. They achieved this by decomposing a 1,710-variable problem into 41 subgraphs, each bounded to 50 qubits, to map the PEGASE-1354 European transmission grid benchmark onto 156-qubit IBM Heron processors. This method, detailed in a preprint on arXiv, leverages shallow QAOA sampling, spectral partitioning, and greedy feasibility repair to address the Minimum Birkhoff Decomposition problem. The post E.ON, Qoro Quantum, and NQCC Scale Constrained Optimization on IBM Heron QPUs via Distributed QAOA and Spatial Packing appeared first on Quantum Computing Report.
Residual graphs are partitioned into QAOA subproblems and dispatched across IBM QPUs using spatial circuit packing. Samples are stitched, repaired to enforce matching feasibility, and added to the classical E-FCFW active set. European energy utility E.ON, quantum software developer Qoro Quantum, and the UK National Quantum Computing Centre (NQCC) have demonstrated a distributed hybrid optimization pipeline that cuts…
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