凝聚态中的神经量子态：进展、最佳实践与展望
Neural quantum states in condensed matter: advances, best practices, and prospects
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中文总结 AI 辅助
该文综述神经量子态在凝聚态系统应用的进展，讨论其计算相关架构等内容，指出面临的挑战并展望其拓展强关联量子物质经典模拟范围的前景。
中文摘要 AI 辅助
神经量子态通过将神经网络参数化与蒙特卡洛采样相结合，为量子多体波函数提供了灵活的变分表示。在这篇观点文章中，我们综述了其在凝聚态系统应用中的最新进展，重点关注阻挫量子磁体、相互作用格点费米子以及非平衡动力学。我们讨论了最先进计算所基于的架构、对称性约束、优化方法和采样策略，并总结了可靠模拟的实用指南。我们还探讨了主要的剩余挑战，包括学习非平凡的符号和相位结构、控制变分偏差、强制执行物理对称性、将优化扩展到大型网络，以及实现稳定的实时演化。最后，我们概述了神经量子态可能拓展强关联量子物质经典模拟范围的有前景方向。
英文摘要
Neural quantum states provide flexible variational representations of quantum many-body wave functions by combining neural-network parametrizations with Monte Carlo sampling. In this perspective, we review recent advances in their application to condensed-matter systems, focusing on frustrated quantum magnets, interacting lattice fermions, and non-equilibrium dynamics. We discuss the architectures, symmetry constraints, optimization methods, and sampling strategies underlying state-of-the-art calculations, and summarize practical guidelines for reliable simulations. We also examine the principal remaining challenges, including learning non-trivial sign and phase structures, controlling variational bias, enforcing physical symmetries, scaling optimization to large networks, and achieving stable real-time evolution. Finally, we outline promising directions in which neural quantum states may extend the reach of classical simulations of strongly correlated quantum matter.
