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,
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
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A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.