Performance of quantum annealing inspired algorithms for combinatorial optimization problems

Performance of quantum annealing inspired algorithms for combinatorial optimization problems

Blog Article

Abstract Two classes of quantum-annealing-inspired-algorithms (QAIA), namely different variants of simulated coherent Ising machine 15-eg1053cl and simulated bifurcation, have been proposed for efficiently solving combinatorial optimization problems recently.In order to certify the superiority of these algorithms, standardized comparisons among them and against other physics-based algorithms are necessary.In this work, for Max-Cut problems up to 20,000 nodes, we benchmark QAIA against quantum annealing and other physics-based algorithms.We found that ballistic simulated bifurcation excelled for chimera and small-scale graphs, achieving nearly a 50-fold reduction in time-to-solution compared to quantum annealing.For large-scale graphs, discrete simulated bifurcation achieves the lowest time-to-target and outperforms D-Wave Advantage system when tasked with finding the maximum cut value in pegasus graphs.

Our results suggest that QAIA represents a promising means for solving combinatorial optimization problems in practice, and can act as a natural baseline for nanosculpt competing quantum algorithms.