Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals.

翻译：神经网络量子态（NNQS）已成为量子多体问题的重要候选方法，但其实际应用常受限于采样与局域能量计算的高昂成本。我们针对从头算电子结构计算开发了一种高性能NNQS方法。主要创新包括：（1）基于Transformer架构的量子波函数拟设；（2）面向变分蒙特卡洛（VMC）算法的数据中心化并行方案，该方案能保持数据局部性并适应不同计算架构；（3）一种降低采样成本、实现良好负载均衡的并行批采样策略；（4）兼具内存与计算效率的并行局域能量评估方案；（5）实际化学体系研究证明，我们的方法在精度上优于现有最优方法，且对于含高达$120$个自旋轨道的大分子体系展现出强伸缩性与弱伸缩性。