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).
Transformer Wave Function for the Shastry- Sutherland Model: emergence of a Spin-Liquid Phase
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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.
Hyperbolic recurrent networks, especially a Lorentz-model RNN, reach lower variational ground-state energies than Euclidean RNN/GRU wavefunctions on 100-spin Heisenberg chains, with up to three times fewer parameters.
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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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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New non-Euclidean neural quantum states from hyperbolic Lorentz recurrent architectures
Hyperbolic recurrent networks, especially a Lorentz-model RNN, reach lower variational ground-state energies than Euclidean RNN/GRU wavefunctions on 100-spin Heisenberg chains, with up to three times fewer parameters.
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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.