Hyperbolic RNN and GRU neural quantum states outperform Euclidean versions on Heisenberg J1J2 and J1J2J3 models with 100 spins.
Transformer Wave Function for the Shastry- Sutherland Model: emergence of a Spin-Liquid Phase,
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iPEPS simulations with bond-dimension extrapolation locate a quantum spin liquid phase in the Shastry-Sutherland model for 0.785(5) ≤ J'/J ≤ 0.82(1).
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.
Presents a Neural Galerkin method that solves quantum dynamics globally via variational minimization of a Schrödinger loss, demonstrated on 1D/2D transverse-field Ising quenches showing non-thermalization in 2D.
citing papers explorer
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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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Quantum spin liquid phase in the Shastry-Sutherland model revealed by high-precision infinite projected entangled-pair states
iPEPS simulations with bond-dimension extrapolation locate a quantum spin liquid phase in the Shastry-Sutherland model for 0.785(5) ≤ J'/J ≤ 0.82(1).
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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.
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Time-dependent Neural Galerkin Method for Quantum Dynamics
Presents a Neural Galerkin method that solves quantum dynamics globally via variational minimization of a Schrödinger loss, demonstrated on 1D/2D transverse-field Ising quenches showing non-thermalization in 2D.