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Machine learning models recognize phase transitions in quantum geometries, suggesting improvements over traditional methods.
Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters.
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Ambjørn et al. (2025) studied this question.
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