One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods. Yet their optimization remains comparatively underexplored: Adam is a scalable method but ignores function space geometry, while stochastic reconfiguration is principled but costly and numerically fragile in large models. To address this gap, we show tha
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- Linked via arxiv authorJuan Agustín Duque →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorSergio García Heredia →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorVinicius Hernandes →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorEliška Greplová →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorThomas Spriggs →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorAaron Courville →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
- Linked via arxiv authorAnna Dawid →
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective