Quantum

Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling

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Briefings

A new method combines single-layer QAOA sampling with Extended Fully-Corrective Frank-Wolfe optimization to solve the Minimum Birkhoff Decomposition problem on the 1,354-node PEGASE bus test case. This approach uses spectral graph partitioning to distribute the 1,710-variable matching optimization across 50-qubit IBM superconducting processors, achieving lower residual decomposition error than simulated annealing and uniform random sampling baselines. Spatial circuit packing reduces hardware execution time by approximately a factor of three, enabling gate-based quantum sampling on thousand-variable constrained optimization problems with current hardware.

Key points

• The Minimum Birkhoff Decomposition (MBD) is a challenging optimization problem requiring 1,710 qubits for its 1,710-variable matching optimization on the PEGASE-1354 benchmark.
• Partitioning the problem into 50-qubit subtasks on IBM superconducting processors yields lower residual decomposition error than both simulated annealing and uniform random sampling baselines.

Context and limitations

The pipeline demonstrates results on the 1,354-node PEGASE bus test case; larger partitions degrade convergence, and MPS simulation demands increase with partition size.

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