Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
Kawasaki, Physical Review145, 224 (1966)
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Adding a continuous bond energy ε to 2D site percolation shifts the threshold smoothly and drives the correlation-length exponent ν from 1/2 through 4/3 to 1, as shown by Monte Carlo simulations and real-space RG that also reveal an energy-weighted correlation length and antiferromagnetic ordering,
citing papers explorer
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Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems
Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
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Energy-Weighted Site Percolation in Two Dimensions
Adding a continuous bond energy ε to 2D site percolation shifts the threshold smoothly and drives the correlation-length exponent ν from 1/2 through 4/3 to 1, as shown by Monte Carlo simulations and real-space RG that also reveal an energy-weighted correlation length and antiferromagnetic ordering,