Mean-field constraints restore sparsity in Potts machines by replacing dense pairwise constraint couplings with dynamically updated single-node biases, achieving comparable partitioning quality with reduced density and accelerated FPGA performance.
Efficient optimization with encoded ising models
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Restoring Sparsity in Potts Machines via Mean-Field Constraints
Mean-field constraints restore sparsity in Potts machines by replacing dense pairwise constraint couplings with dynamically updated single-node biases, achieving comparable partitioning quality with reduced density and accelerated FPGA performance.