Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
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Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.
Optimization-free Recursive QAOA solves the Binary Paint Shop Problem near-optimally with reduced quantum resources and robustness to parameter choice compared to standard QAOA.
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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Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
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Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.
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Optimisation-Free Recursive QAOA for the Binary Paint Shop Problem
Optimization-free Recursive QAOA solves the Binary Paint Shop Problem near-optimally with reduced quantum resources and robustness to parameter choice compared to standard QAOA.
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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.