arxiv:2108.08859

Solving Conformal Field Theories with Artificial Intelligence

Published on Aug 19, 2021

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Abstract

Reinforcement-Learning algorithms, specifically soft Actor-Critic, are used to solve conformal field theories and identify known models through numerical solutions of crossing equations.

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In this paper we deploy for the first time Reinforcement-Learning algorithms in the context of the conformal-bootstrap programme to obtain numerical solutions of conformal field theories (CFTs). As an illustration, we use a soft Actor-Critic algorithm and find approximate solutions to the truncated crossing equations of two-dimensional CFTs, successfully identifying well-known theories like the 2D Ising model and the 2D CFT of a compactified scalar. Our methods can perform efficient high-dimensional searches that can be used to study arbitrary (unitary or non-unitary) CFTs in any spacetime dimension.

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