Quantum fields as deep learning

TL;DR

This paper proposes a novel analogy between quantum fields and deep learning models, suggesting that the thermal properties of quantum fields relate to neural network algorithms, with potential implications for understanding quantum phenomena in curved spacetime.

Contribution

It introduces a conjecture linking quantum field theory to deep learning, highlighting a new perspective on quantum entanglement and renormalization as computational processes.

Findings

Quantum fields exhibit thermal distributions similar to deep learning processes.

Renormalization group flows in quantum fields resemble deep neural network algorithms.

The analogy extends to quantum fields in curved spacetime, such as black holes.

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.