发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对含时神经量子态难以传播稀疏支撑态的问题，提出插值采样方法，结合全局时间变分原理，在二维横向场伊辛模型中实现极尖锐态的精确传播，并引入联合采样与混合提议策略提升效率。
AI 中文摘要
神经量子态（NQSs）已成为量子动力学中一种强大的变分拟设。其高纠缠容量有望克服纠缠壁垒。然而，现有的含时NQS方法由于此类态的支撑与其时间导数之间存在固有失配，难以有效传播稀疏支撑的初始态。为解决这一问题，我们引入了插值采样方法作为一种新颖的重要性采样形式，其中样本从波函数与其时间导数之间插值的分布中抽取。结合全局时间变分原理，我们以二维横向场伊辛模型为例证明，插值采样能够精确传播标准波函数采样无法处理的极尖锐态。我们进一步通过引入（i）一种构型-时间联合采样方案（其中自旋构型和时间均被视为随机变量）以及（ii）一种融合Krylov子空间知识的混合样本提议策略来提高采样效率。我们的工作将含时NQS方法的应用范围扩展到更广泛的物理相关场景，同时挑战了普遍依赖Born分布及其近似变体作为默认采样基础的做法。
英文摘要
Neural quantum states (NQSs) have emerged as a powerful ansatz for quantum dynamics. Their high entanglement capacity promises to overcome the entanglement barrier. However, existing time-dependent NQS methods are ill-equipped to propagate sparsely supported initial states due to the inherent mismatch between the supports of such states and their time derivatives. To address this issue, we introduce the interpolation sampling method as a novel form of importance sampling, where samples are drawn from a distribution that interpolates between the wave function and its time derivative. Combined with a global-in-time variational principle, we demonstrate with the example of the two-dimensional transverse-field Ising model that interpolation sampling allows for accurate propagation of extremely peaked states that standard wave function sampling could not tackle. We further improve sampling efficiency by introducing (i) a configuration-time joint sampling scheme where spin configurations and time are both treated as random variables, and (ii) a hybrid strategy for sample proposal that incorporates the knowledge of a Krylov subspace. Our work extends the scope of time-dependent NQS methods to a wider range of physically relevant scenarios, while challenging the prevailing reliance on the Born distribution and its close variants as the default basis for sampling.
Comments36 pages, 11 figures
