The Hintons in Your Neural Network: A Quantum Field Theory View of Deep Learning
Abstract
In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent’s uncertainty about the input signal. We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles, dubbed “Hintons”. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing, and provides quantum deformations of neural networks that can be run efficiently on those devices. Finally, we discuss a semi-classical limit of the quantum deformed models which is amenable to classical simulation.
Cite
Text
Bondesan and Welling. "The Hintons in Your Neural Network: A Quantum Field Theory View of Deep Learning." International Conference on Machine Learning, 2021.Markdown
[Bondesan and Welling. "The Hintons in Your Neural Network: A Quantum Field Theory View of Deep Learning." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/bondesan2021icml-hintons/)BibTeX
@inproceedings{bondesan2021icml-hintons,
title = {{The Hintons in Your Neural Network: A Quantum Field Theory View of Deep Learning}},
author = {Bondesan, Roberto and Welling, Max},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {1038-1048},
volume = {139},
url = {https://mlanthology.org/icml/2021/bondesan2021icml-hintons/}
}