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.
Instead of defining the quantum state from a parameterized family of prob- ability distributions, now we define a ”Neural Quantum State” [5] (NQS), as the variational Ansatz
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.