ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems

Applying quantum chemistry algorithms to large-scale systems requires substantial computational resources scaling with the system size and the desired accuracy. To address this, ByteQC, a fully functional and efficient package for large-scale quantum chemistry simulations, has been open sourced at https://github.com/bytedance/byteqc, leveraging recent advances in computational power and many-body algorithms. Regarding computational power, several standard algorithms are efficiently implemented on modern GPUs, ranging from mean-field calculations (Hartree-Fock and density functional theory) to post-Hartree-Fock methods such as Møller-Plesset perturbation theory and coupled cluster methods. For the algorithmic approach, we also employ a quantum embedding method, which significantly expands the tractable system size while preserving high accuracy at the gold-standard level. All these features have been systematically benchmarked. For standalone algorithms, the benchmark results demonstrate up to a 60× speedup when compared to 100-core CPUs. Additionally, the tractable system sizes have been significantly expanded: 1610 orbitals for coupled cluster with single and double excitations (1380 orbitals with perturbative triple excitations), 11,040 orbitals for Møller-Plesset perturbation theory of second order, 37,120 orbitals for mean-field calculations under open boundary conditions, and over 100,000 orbitals for periodic boundary conditions. For the advanced quantum embedding feature, two representative examples are demonstrated: the water cluster problem (2752 orbitals) and a water monomer adsorbed on a boron nitride surface (3929 orbitals), achieving the gold-standard accuracy. With these efforts, ByteQC is expected to significantly advance research in quantum chemistry, particularly in large-scale, high-accuracy calculations.