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.
For each (L, q, β) configuration, ten independent trials were per- formed, each consisting of 106 p-dit updates per spin, fol- lowing standard Monte Carlo sampling procedures [27]
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.stat-mech 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.