Absence of quantum advantage for log-depth QAOA on the binary paint shop problem implies a classical mean-field algorithm achieving a paint-swap ratio of approximately 0.2799, outperforming known heuristics and quantum methods.
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GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.
Classical solvers solve random Ising models on heavy-hex graphs efficiently, with Gurobi showing linear or weakly quadratic scaling up to 100k variables and simulated annealing showing exponential time-to-solution without cubic terms.
citing papers explorer
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No quantum advantage implies improved bounds and classical algorithms for the binary paint shop problem
Absence of quantum advantage for log-depth QAOA on the binary paint shop problem implies a classical mean-field algorithm achieving a paint-swap ratio of approximately 0.2799, outperforming known heuristics and quantum methods.
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Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms
GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.
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Classical Combinatorial Optimization Scaling for Random Ising Models on 2D Heavy-Hex Graphs
Classical solvers solve random Ising models on heavy-hex graphs efficiently, with Gurobi showing linear or weakly quadratic scaling up to 100k variables and simulated annealing showing exponential time-to-solution without cubic terms.