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Applications of Machine Learning to Lattice Quantum Field Theory
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies.
Quantum field theories, Markov random fields and machine learning
The transition to Euclidean space and the discretization of quantum field theories on spatial or space-time lattices opens up the opportunity to investigate probabilistic machine learning within quantum field theory.
Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology
In particular, we introduce a new way of computing the persistent homology of lattice spin model configurations and, by considering the fluctuations in the output of logistic regression and k-nearest neighbours models trained on persistence images, we develop a methodology to extract estimates of the critical temperature and the critical exponent of the correlation length.
Machine learning with quantum field theories
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random fields, opens up the opportunity to investigate machine learning from the perspective of quantum field theory.
Quantum field-theoretic machine learning
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory.
Extending machine learning classification capabilities with histogram reweighting
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods.
