We use classical Monte Carlo simulations to generate samples for the 2D Ising model, with each sample representing a snapshot of the system at a given temperature. These snapshots can be viewed as nodes within a wave function network. The connections between the nodes are determined by their Hamming distances. By analyzing the behavior of the distribution of these distances, we explore the structure of the wave function network across different temperatures and relate our understandings to the classical phase transition in the 2D Ising model.
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Classical Monte Carlo simulations generate 2D Ising model samples, forming a wave function network with connections based on Hamming distances to explore the phase transition.
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