Quantum field-theoretic machine learning

The precise equivalence between discretized Euclidean field theories and Markov random fields, as established by the Hammersley-Clifford theorem, opens up the opportunity to investigate machine learning from the perspective of quantum field theory. In this talk I will discuss a variety of interconnected topics: Markov properties for quantum fields, the derivation of machine learning algorithms and of neural networks from the $\phi^{4}$ scalar field theory and the minimization of distance metrics between probability distributions. I will then conclude by presenting applications pertinent to the quantum field-theoretic machine learning algorithms.