A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
Transformer variational wave functions for frus- trated quantum spin systems
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Hyperbolic RNN and GRU neural quantum states outperform Euclidean versions on Heisenberg J1J2 and J1J2J3 models with 100 spins.
Ground-state phase reconstruction for Heisenberg antiferromagnets with fixed amplitudes is equivalent to weighted Max-Cut on the Hilbert-space graph, establishing worst-case NP-hardness.
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Neural network quantum states in the grand canonical ensemble
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
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New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks
Hyperbolic RNN and GRU neural quantum states outperform Euclidean versions on Heisenberg J1J2 and J1J2J3 models with 100 spins.
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Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Heisenberg Antiferromagnets
Ground-state phase reconstruction for Heisenberg antiferromagnets with fixed amplitudes is equivalent to weighted Max-Cut on the Hilbert-space graph, establishing worst-case NP-hardness.