A pairwise autoregressive graphical model trained with first-order gradients matches or outperforms heavier neural-network quantum states for stoquastic spin Hamiltonians, especially with limited time and samples.
In our benchmarks, we adopt the Quimb li- brary [16] and the ITensor library [15] as reference im- plementations of tensor network algorithms
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Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians
A pairwise autoregressive graphical model trained with first-order gradients matches or outperforms heavier neural-network quantum states for stoquastic spin Hamiltonians, especially with limited time and samples.