Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku

TL;DR

This paper introduces a high-performance neural network quantum states framework for ab initio quantum chemistry that significantly improves scalability and training speed on large-scale systems using advanced parallelism and optimization techniques.

Contribution

The paper presents a scalable NQS training framework with novel parallelism strategies and cache optimization, enabling large-scale quantum chemistry simulations on Fugaku.

Findings

Achieved up to 8.41x speedup in NQS training

Attained 95.8% parallel efficiency on 1,536 nodes

Enabled scalable ab initio quantum chemistry simulations

Abstract

Solving quantum many-body problems is one of the fundamental challenges in quantum chemistry. While neural network quantum states (NQS) have emerged as a promising computational tool, its training process incurs exponentially growing computational demands, becoming prohibitively expensive for large-scale molecular systems and creating fundamental scalability barriers for real-world applications. To address above challenges, we present \ours, a high-performance NQS training framework for \textit{ab initio} electronic structure calculations. First, we propose a scalable sampling parallelism strategy with multi-layers workload division and hybrid sampling scheme, which break the scalability barriers for large-scale NQS training. Then, we introduce multi-level parallelism local energy parallelism, enabling more efficient local energy computation. Last, we employ cache-centric optimization for transformer-based \textit{ansatz} and incorporate it with sampling parallelism strategy, which further speedup up the NQS training and achieve stable memory footprint at scale. Experiments demonstrate that \ours accelerate NQS training with up to 8.41x speedup and attains a parallel efficiency up to 95.8\% when scaling to 1,536 nodes.