Deep learning lattice gauge theories

• URL: http://arxiv.org/abs/2405.14830v1
• Date: Thu, 23 May 2024 17:46:49 GMT
• Title: Deep learning lattice gauge theories
• Authors: Anuj Apte, Anthony Ashmore, Clay Cordova, Tzu-Chen Huang,
• Abstract summary: We use neural network quantum states to accurately compute the ground state of lattice gauge theories in $2+1$ dimensions. Our findings suggest that neural network quantum states are a promising method for precise studies of lattice gauge theory.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monte Carlo methods have led to profound insights into the strong-coupling behaviour of lattice gauge theories and produced remarkable results such as first-principles computations of hadron masses. Despite tremendous progress over the last four decades, fundamental challenges such as the sign problem and the inability to simulate real-time dynamics remain. Neural network quantum states have emerged as an alternative method that seeks to overcome these challenges. In this work, we use gauge-invariant neural network quantum states to accurately compute the ground state of $\mathbb{Z}_N$ lattice gauge theories in $2+1$ dimensions. Using transfer learning, we study the distinct topological phases and the confinement phase transition of these theories. For $\mathbb{Z}_2$, we identify a continuous transition and compute critical exponents, finding excellent agreement with existing numerics for the expected Ising universality class. In the $\mathbb{Z}_3$ case, we observe a weakly first-order transition and identify the critical coupling. Our findings suggest that neural network quantum states are a promising method for precise studies of lattice gauge theory.

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