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Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
In particular, for 90% of random 4-SAT instances we find solutions that are inaccessible for the best specialized deterministic algorithm known as Survey Propagation (SP) with an order of magnitude improvement in the quality of solutions for the hardest 10% instances.
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing
Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system.
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