A proof-of-principle that simple neural networks can learn Euclidean Barrett-Crane 10j vertex amplitudes: classification generalizes to higher cutoffs, regression works only within the trained low-spin domain.
Generative Flow Networks in Covariant Loop Quantum Gravity
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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 (HPC). Following recent advances, where stochastic algorithms (Markov Chain Monte Carlo-MCMC) 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.
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gr-qc 1years
2025 1verdicts
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Deep learning spinfoam vertex amplitudes: the Euclidean Barrett-Crane model
A proof-of-principle that simple neural networks can learn Euclidean Barrett-Crane 10j vertex amplitudes: classification generalizes to higher cutoffs, regression works only within the trained low-spin domain.