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SEMO error-mitigated and standard quantum annealing and classical computing solutions of weighted max-cut on cubic lattice

Commonwealth Scientific and Industrial Research Organisation
Yang, Sam ; Tyson, Peter
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/8b1k-4s54&rft.title=SEMO error-mitigated and standard quantum annealing and classical computing solutions of weighted max-cut on cubic lattice&rft.identifier=https://doi.org/10.25919/8b1k-4s54&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=The weighted Max-Cut is a NP hard problem with application implications. This collection includes Python script and numerical results solving the Max-Cut problem on a cubic lattice with 11x11x11 nodes and random edge weights with mixed signs. Solution time with a novel SEMO (spin-error mitigation for optimisation) error-mitigated quantum annealing for the problem is compared with the standard D-Wave quantum annealing and BQM hybrid solvers, and various classical solvers including simulated annealing, Tabu search, Goemans-Williamson and Goemans-Williamson-inspired algorithms. It has been quantitatively demonstrated that the SEMO error-mitigated quantum annealing is outperforming other approaches. The error-mitigated quantum annealing approach presented in this article would be applicable in solving other discrete optimisation problems efficiently, which is particularly impactful for certain time-critical applications. &rft.creator=Yang, Sam &rft.creator=Tyson, Peter &rft.date=2026&rft.edition=v1&rft.relation=http://doi.org/10.1002/apxr.202500216&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2026.&rft_subject=Weighted Max-Cut&rft_subject=quantum annealing&rft_subject=spin-error mitigation for optimisation (SEMO)&rft_subject=simulated annealing&rft_subject=Tabu search&rft_subject=Goemans-Williamson algorithm&rft_subject=cubic lattice&rft_subject=Modelling and simulation&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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The weighted Max-Cut is a NP hard problem with application implications. This collection includes Python script and numerical results solving the Max-Cut problem on a cubic lattice with 11x11x11 nodes and random edge weights with mixed signs. Solution time with a novel SEMO (spin-error mitigation for optimisation) error-mitigated quantum annealing for the problem is compared with the standard D-Wave quantum annealing and BQM hybrid solvers, and various classical solvers including simulated annealing, Tabu search, Goemans-Williamson and Goemans-Williamson-inspired algorithms. It has been quantitatively demonstrated that the SEMO error-mitigated quantum annealing is outperforming other approaches. The error-mitigated quantum annealing approach presented in this article would be applicable in solving other discrete optimisation problems efficiently, which is particularly impactful for certain time-critical applications.

Available: 2026-04-07

Data time period: 2025-07-01 to 2026-04-03

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Subjects
Artificial Intelligence | Goemans-Williamson algorithm | Information and Computing Sciences | Modelling and Simulation | Tabu search | Weighted Max-Cut | cubic lattice | quantum annealing | simulated annealing | spin-error mitigation for optimisation (SEMO) |

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