论文标题

量规量表神经网络的量子晶格量表理论

Gauge equivariant neural networks for quantum lattice gauge theories

论文作者

Luo, Di, Carleo, Giuseppe, Clark, Bryan K., Stokes, James

论文摘要

量规对称性在出现在诸如基本颗粒的量子场理论和量子材料自由度的量子场理论中的物理学中起关键作用。引入了渴望有效模拟具有精确局部规格不变性的多体量子系统的愿望,介绍了量规量表的神经网络量子量状态,这完全满足了描述量子lattice Gauge理论的局部希尔伯特空间约束,并在不同的地质上使用ZD量规组。分析表明,在定期识别出的方格上,重点介绍了Z2量规组的特殊情况,因此在分析上表明了loop-gas解决方案作为特殊情况。量规模棱两可的神经网络量子状态与变异量子蒙特卡洛(Carlo)结合使用,以获得Z2理论的基态波函数的紧凑描述,远离确切的可解决极限,并证明Wilson Loop Ord Ord Order Order Arder Commeter的限制/切除/脱凝相位过渡。

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body quantum systems with exact local gauge invariance, gauge equivariant neural-network quantum states are introduced, which exactly satisfy the local Hilbert space constraints necessary for the description of quantum lattice gauge theory with Zd gauge group on different geometries. Focusing on the special case of Z2 gauge group on a periodically identified square lattice, the equivariant architecture is analytically shown to contain the loop-gas solution as a special case. Gauge equivariant neural-network quantum states are used in combination with variational quantum Monte Carlo to obtain compact descriptions of the ground state wavefunction for the Z2 theory away from the exactly solvable limit, and to demonstrate the confining/deconfining phase transition of the Wilson loop order parameter.

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