AI 中文总结
研究自注意力神经网络的变分波函数在大系统规模下的可扩展性，通过应用于二维均匀电子气，处理多达169个粒子，获得更低能量，恢复集体模式色散，且可观测量表明收敛到热力学极限。
AI 中文摘要
自注意力神经网络提供了强大的变分波函数，超越了传统变分假设的表现力。但其表现力伴随着计算复杂度的增加，引发了关于可扩展性的紧迫问题，即在大系统规模下此类波函数能否保持准确性。我们将自注意力波函数应用于二维均匀电子气，处理多达N = 169个粒子，得到的能量系统地低于现有最佳的扩散蒙特卡罗方法。直接获取基态波函数还使我们能够恢复液相的完整集体模式色散。N = 91和N = 169时的可观测量几乎完全一致，表明收敛到热力学极限。
英文摘要
Self-attention neural networks provide powerful variational wavefunctions that surpass the expressivity of traditional variational ansatze. This expressivity, however, comes with increased computational complexity, raising a pressing question about scalability -- can such wavefunctions retain their accuracy at large system sizes? We apply self-attention wavefunctions to the two-dimensional homogeneous electron gas for up to N=169 particles, obtaining energies systematically lower than state-of-the-art DMC. Direct access to the ground state wavefunction further lets us recover the full collective-mode dispersion of the liquid phase, from the small-q plasmon branch to a roton-like minimum near q=2k_F. Observables at N=91 and N=169 are in near-perfect agreement, indicating convergence to the thermodynamic limit.
