Kibble-Zurek Dynamics in Two-dimensional Frustrated Systems with a Neural Foundation-state Subspace Method

arXiv:2609.05556 · cond-mat.str-el, quant-ph · Submitted 2026-09-03 · Read on arXiv

cond-mat.str-el, quant-ph

Submitted: 2026-09-03

Updated: 2026-09-18

Terminology

• Transformer — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Neural Foundation-state Subspace — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Entanglement — 3× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Phase Transitions — 2× in this paper · explained in 2 episodes
• Antiferromagnetism — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Effective Hamiltonian — 1× in this paper · explained in 2 episodes
• Entanglement Entropy — 1× in this paper · explained in 2 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Hubbard Model — 1× in this paper · explained in 3 episodes
• Quantum Physics — 1× in this paper · explained in 11 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Stochastic Reconfiguration — 1× in this paper
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Topological Defects — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Universality Class — 1× in this paper · explained in 2 episodes

Sources

• Real-time Dynamics in 3D for up to 1000 Qubits with Neural Quantum States: Quenches and the Quantum Kibble--Zurek Mechanism
• Thermalization Dynamics in the Two-Dimensional Hubbard Model with Neural-Network Quantum States
• Removing nodal and support-mismatch pathologies in Variational Monte Carlo via blurred sampling
• Approaching the Thermodynamic Limit with Neural-Network Quantum States
• Disentangling Tensor Network States with Deep Neural Network
• Scaling Laws for Neural-Network Quantum States
• Quantum Spin Glass in the Two-Dimensional Disordered Heisenberg Model via Foundation Neural-Network Quantum States
• QERNEL: a Scalable Large Electron Model
• Grassmann Variational Monte Carlo with neural wave functions
• Neural Network Approach to Scaling Analysis of Critical Phenomena

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