AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrödinger equation for complex many-body systems. Among neural network quantum state (NNQS) approaches, the NNQS-SCI (Selected Configuration Interaction) method stands out as a state-of-the-art technique, recognized for its high accuracy and scalability. However, its application to larger systems is severely constrained by a hybrid CPU-GPU architecture. Specifically, centralized CPU-based global de-duplication creates a severe scalability barrier due to communication bottlenecks, while host-resident coupled-configuration generation induces prohibitive computational overheads. We introduce QiankunNet-cuSCI, a fully GPU-accelerated SCI framework designed to overcome these bottlenecks. It first integrates a distributed, load-balanced global de-duplication algorithm to minimize redundancy and communication overhead at scale. To address compute limitations, it employs specialized, fine-grained CUDA kernels for exact coupled configuration generation. Finally, to break the single-GPU memory barrier exposed by this full acceleration, it incorporates a GPU memory-centric runtime featuring GPU-side pooling, streaming mini-batches, and overlapped offloading. This design enables much larger configuration spaces and shifts the bottleneck from host-side limitations back to on-device inference. Our evaluation demonstrates that our work fundamentally expands the scale of solvable problems. On an NVIDIA A100 cluster with 64 GPUs, our work achieves up to 2.32 × end-to-end speedup over the highly-optimized NNQS-SCI baseline while preserving the same chemical accuracy. Furthermore, it demonstrates excellent distributed performance, maintaining over 90% parallel efficiency in strong scaling tests.

Neural Network Quantum States (NNQS) offer a powerful variational Monte Carlo (VMC) approach for quantum many-body problems, balancing polynomial scaling with high expressive power. However, scaling NNQS to large chemical systems faces challenges in preserving accuracy with exact energy and managing vast configurations efficiently. In this work, we introduce NNQS-SCI, a high-performance Selected Configuration Interaction (SCI) based NNQS method designed to overcome these limitations. NNQS-SCI employs highly parallelized Slater-Condon rules for fast local energy evaluations, avoiding accuracy loss, while its adaptive SCI engine dynamically manages billions of configurations without space explosion or arbitrary cutoffs that plague other NNQS-CI approaches. Optimized for extreme scalability via multi-level parallelism and memory compression, NNQS-SCI successfully simulates systems up to 152 spin orbitals, tackling Hilbert space dimensions exceeding 1014 and demonstrating significant advances in scale and efficiency. NNQS-SCI thus provides a robust and scalable path towards high-accuracy quantum chemistry on high-performance computing platforms.

Raman spectroscopy offers invaluable insights into the chemical composition and structural characteristics of various materials, making it a powerful tool for structural analysis. However, accurate quantum mechanical simulations of Raman spectra for large systems, such as biological materials, have been limited due to immense computational costs and technical challenges. In this study, we developed efficient algorithms and optimized implementations on heterogeneous computing architectures to enable fast and highly scalable ab initio simulations of Raman spectra for large-scale biological systems with up to 100 million atoms. Our simulations have achieved nearly linear strong and weak scaling on two cutting-edge high-performance computing systems, with peak FP64 performances reaching 400 PFLOPS on 96,000 nodes of new Sunway supercomputer and 85 PFLOPS on 6,000 node of ORISE supercomputer. These advances provide promising prospects for extending quantum mechanical simulations to biological systems.