الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Liaqat, A., Darwish, A., DiAdamo, S., Holme, D., McDowall, K., Sahin, E., Mohseni, N., Cortiana, G., & O'Meara, C. (2026). Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling. https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling
MLA 9
Liaqat, Amana, et al. "Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling." https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling.
شيكاغو (المؤلف–التاريخ)
Liaqat, Amana, Ahmed Darwish, Stephen DiAdamo, Dan Holme, Kieran McDowall, Emre Sahin, Naeimeh Mohseni, Giorgio Cortiana, and Corey O'Meara. 2026. "Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling." https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling.
هارفارد
Liaqat, A., Darwish, A., DiAdamo, S., Holme, D., McDowall, K., Sahin, E., Mohseni, N., Cortiana, G. and O'Meara, C. (2026) 'Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling', Available at: https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling.
فانكوفر
Liaqat A, Darwish A, DiAdamo S, Holme D, McDowall K, Sahin E, et al. Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling. https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling
IEEE
A. Liaqat, A. Darwish, S. DiAdamo, D. Holme, K. McDowall, E. Sahin, N. Mohseni, G. Cortiana, and C. O'Meara, "Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling," https://omanscience.com/ar/articles/scaling-quantum-optimization-to-the-thousand-qubit-scale-with-distributed-quantum-sampling.