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

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.