Generative flow networks in covariant loop quantum gravity
Phys. Rev. D 112, 086001 – Published 2 October, 2025
DOI: https://doi.org/10.1103/pvqh-3t24
Abstract
Spin foams arose as the covariant (path integral) formulation of quantum gravity depicting transition amplitudes between different quantum geometry states. As such, they provide a scheme to study the no-boundary proposal, specifically the nothing to something transition, and compute relevant observables using high-performance computing. Following recent advances, where stochastic algorithms (Markov chain Monte Carlo) were used, we employ “generative flow networks,” a newly developed machine-learning algorithm to compute the expectation value of the dihedral angle for a 4-simplex and compare the results with previous works.
