Quantum fields as deep learning

Abstract

In this essay we conjecture that quantum fields such as the Higgs field is related to a restricted Boltzmann machine for deep neural networks. An accelerating Rindler observer in a flat spacetime sees the quantum fields having a thermal distribution from the quantum entanglement, and a renormalization group process for the thermal fields on a lattice is similar to a deep learning algorithm. This correspondence can be generalized for the KMS states of quantum fields in a curved spacetime like a black hole.

References in corpus (2)

• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Holography as deep learning

Cited by in corpus (4)

• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Deep Learning and AdS/QCD
• Deep learning-based holography for T-linear resistivity
• Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory $(2)$