Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
The research scaled quantum optimization for complex problems like Minimum Birkhoff Decomposition to thousand-qubit scales using distributed quantum sampling. By combining QAOA, spectral partitioning, and spatial circuit packing across multiple QPUs, the method achieved lower decomposition errors than classical methods. This demonstrates a viable path for deploying gate-based quantum sampling on current hardware.