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.

我们开发了一种基于转换器的神经网络量子态方法，用于从头计算电子结构，并采用数据中心的并行化方案，平行化的批量采样策略，以及计算有效的局部能源计算方法。对化学体系的研究表明，我们方法的精度优于现有技术，并且在大分子系统上具有良好的强弱可扩展性。