PWO is a trust-region optimizer for autoregressive NQS that improves stability over Adam and stochastic reconfiguration methods while scaling to 1.5B-parameter models on spin systems.
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2026 2verdicts
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HQT uses generative attention to reach E/N = -0.5001(1) on the 8x8 J1-J2 Heisenberg model at J2=0.5 and transfers zero-shot to 10x10 lattices via positional embedding interpolation to obtain E/N = -0.49782(3).
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One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
PWO is a trust-region optimizer for autoregressive NQS that improves stability over Adam and stochastic reconfiguration methods while scaling to 1.5B-parameter models on spin systems.
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Holographic Quantum Transformer: A Generalist Neuro-Symbolic Architecture for Solving Frustrated Systems via Generative Attention
HQT uses generative attention to reach E/N = -0.5001(1) on the 8x8 J1-J2 Heisenberg model at J2=0.5 and transfers zero-shot to 10x10 lattices via positional embedding interpolation to obtain E/N = -0.49782(3).