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.
& Camsari, K
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Simulations predict that a virtually connected photonic probabilistic computer solves Erdos-Renyi graph spin-glass ground states orders of magnitude faster than digital annealing units by avoiding embedding and sparsification.
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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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A virtually connected probabilistic computer as a solver for higher-order, densely connected, or reconfigurable combinatorial optimisation problems
Simulations predict that a virtually connected photonic probabilistic computer solves Erdos-Renyi graph spin-glass ground states orders of magnitude faster than digital annealing units by avoiding embedding and sparsification.