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Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit

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arxiv 2405.00661 v1 pith:TPDUXSVU submitted 2024-05-01 gr-qc hep-thphysics.comp-ph

classification gr-qchep-thphysics.comp-ph
keywords quantumconstraintsgravityconstraintsolutionstextbf-theorycoupling
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abstract

We consider 3-dimensional Euclidean gravity in the weak coupling limit of Smolin and show that it is BF-theory with $\text{U(1)}^3$ as a Lie group. The theory is quantised using loop quantum gravity methods. The kinematical degrees of freedom are truncated, on account of computational feasibility, by fixing a graph and deforming the algebra of the holonomies to impose a cutoff on the charge vectors. This leads to a quantum theory related to $\text{U}_q \text{(1)}^3$ BF-theory. The effect of imposing the cutoff on the charges is examined. We also implement the quantum volume operator of 3d loop quantum gravity. Most importantly we compare two constraints for the quantum model obtained: a master constraint enforcing curvature and Gauss constraint, as well as a combination of a quantum Hamilton constraint constructed using Thiemann's strategy and the Gauss master constraint. The two constraints are solved using the neural network quantum state ansatz, demonstrating its ability to explore models which are out of reach for exact numerical methods. The solutions spaces are quantitatively compared and although the forms of the constraints are radically different, the solutions turn out to have a surprisingly large overlap. We also investigate the behavior of the quantum volume in solutions to the constraints.

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Cited by 2 Pith papers

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  1. Simultaneous approximation of multiple degenerate states using a single neural network quantum state

    quant-ph 2025-09 conditional novelty 6.0 of 10

    A single shared trunk plus one linear head per state can represent a degenerate eigenspace exactly if the trunk width is at least the combined linear rank of target log-moduli and phases minus one on the common support.

  2. Deep learning spinfoam vertex amplitudes: the Euclidean Barrett-Crane model

    gr-qc 2025-05 conditional novelty 6.0 of 10

    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.

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