  We pursue the use of deep learning methods to improve state-of-the-art
computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills
theory is a close cousin to the theory that describes Higgs boson production at
the Large Hadron Collider; its scattering amplitudes are large mathematical
expressions containing integer coefficients. In this paper, we apply
Transformers to predict these coefficients. The problem can be formulated in a
language-like representation amenable to standard cross-entropy training
objectives. We design two related experiments and show that the model achieves
high accuracy (> 98%) on both tasks. Our work shows that Transformers can be
applied successfully to problems in theoretical physics that require exact
solutions.
