Transfer learning with (L,2)-tiling, which repeats a small-system weight pattern into a larger restricted Boltzmann machine, reaches the ground state faster and more accurately than random initialization in several quantum phases.
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Transfer learning for scalability of neural-network quantum states
Transfer learning with (L,2)-tiling, which repeats a small-system weight pattern into a larger restricted Boltzmann machine, reaches the ground state faster and more accurately than random initialization in several quantum phases.