[
    {
        "id": "osp-18807",
        "type": "article-journal",
        "title": "Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling",
        "author": [
            {
                "family": "Liaqat",
                "given": "Amana"
            },
            {
                "family": "Darwish",
                "given": "Ahmed"
            },
            {
                "family": "DiAdamo",
                "given": "Stephen"
            },
            {
                "family": "Holme",
                "given": "Dan"
            },
            {
                "family": "McDowall",
                "given": "Kieran"
            },
            {
                "family": "Sahin",
                "given": "Emre"
            },
            {
                "family": "Mohseni",
                "given": "Naeimeh"
            },
            {
                "family": "Cortiana",
                "given": "Giorgio"
            },
            {
                "family": "O'Meara",
                "given": "Corey"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "The Minimum Birkhoff Decomposition (MBD) seeks a sparse weighted sum of matchings and is a challenging optimization problem with applications in network scheduling and energy trading. We scale a quantum-assisted decomposition method by combining single-layer QAOA sampling with Extended Fully-Corrective Frank-Wolfe (E-FCFW) optimization, spectral graph partitioning, and greedy feasibility repair. We demonstrate the pipeline on the 1,354-node PEGASE bus test case (representing a 1,354-bus transmission grid with 1,710 transmission lines), whose 1,710 edges define a native 1,710-variable matching optimization problem requiring 1,710 qubits in the unpartitioned encoding. Partitioning enables distributed execution on IBM superconducting quantum processors. The best reported hardware result uses a 50-qubit partition bound, while reducing partitions to 20 qubits degrades convergence in the partition-size comparison. Larger partitions are more demanding for matrix product state (MPS) simulation, and bond-dimension tests show that truncating quantum correlations reduces candidate quality. The results therefore motivate retaining a substantive quantum sampling task within each partition as the overall problem scales. On the PEGASE-1354 benchmark, repaired QAOA samples achieve lower residual decomposition error than both simulated annealing and uniform random sampling baselines, while spatial circuit packing reduces hardware execution time by approximately a factor of three. These results demonstrate that combining spectral partitioning, spatial circuit packing, and classical feasibility repair provides an executable path for deploying gate-based quantum sampling on thousand-variable constrained optimization problems on current hardware."
    }
]