# The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning

@inproceedings{Bondesan2021TheHI, title={The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning}, author={Roberto Bondesan and Max Welling}, booktitle={International Conference on Machine Learning}, year={2021} }

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…

## 4 Citations

### A Leap among Quantum Computing and Quantum Neural Networks: A Survey

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Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of learning algorithms…

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This work explicitly construct the quantum field theory corresponding to a general class of deep neural networks encompassing both recurrent and feedforward architectures, and provides a first-principles approach to the rapidly emerging NN-QFT correspondence.

### Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group

- Computer ScienceSciPost Physics
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The analogy between the renormalization group (RG) and deep neural networks, wherein subsequent layers of neurons are analogous to successive steps along the RG, is investigated, and the monotonic increase confirms the connection between the relative entropy and the c-theorem.

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