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

ABSTRACT 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.