Dynamical frustration in parametrically driven space-time metamaterials produces topologically protected unidirectional phase dislocations that self-organize into synchronized defects in 2D.
Ising machines as hardware solvers of combinatorial optimization problems
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SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
Ising machine probabilistic computing achieves optimal ML detection for XL-MIMO up to 2048x2048 antennas in 100 iterations and extends to 256-QAM via p-dits while matching or beating MMSE.
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Dynamical frustration in space-time metamaterials
Dynamical frustration in parametrically driven space-time metamaterials produces topologically protected unidirectional phase dislocations that self-organize into synchronized defects in 2D.