ArticlePrecision chemistry2026
Implicitly Restarted Lanczos Enables Chemically Accurate Shallow Neural Quantum States.
Article in Precision chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The variational optimization of neural quantum states (NQS) is hindered by the slow convergence and numerical instability of conventional optimizers. We address this bottleneck by introducing the implicitly restarted Lanczos (IRL) method as the core engine for NQS training. Our approach recasts the parameter update problem into a Hermitian eigenvalue problem that IRL solves efficiently, automatically determining the optimal descent direction and step size without hyperparameter tuning. We demonstrate that IRL enables shallow NQS architectures to achieve extreme precision in just 3-5 optimization steps, dramatically outperforming existing methods, including minimum-step stochastic reconfiguration (MinSR). For the N
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