Finding and characterising physical states of Euclidean Abelianized loop quantum gravity using neural quantum states
Hanno Sahlmann, Waleed Sherif

TL;DR
This paper employs neural quantum states and variational Monte Carlo to identify and characterize physical states in 4D Euclidean loop quantum gravity, revealing distinct solution families for different Hamiltonian operators.
Contribution
It introduces a neural network-based variational approach to find and analyze solutions of the Hamilton constraint in Euclidean loop quantum gravity on a complete graph.
Findings
Distinct solution families for $ H$ and $ H^\u2212$ are identified.
Solutions for $ H$ are flat on minimal loops with non-zero volume.
Solutions for $ H^$ are normalisable with non-trivial charge correlations.
Abstract
We study physical (near-kernel of constraints) states of 4-d Euclidean loop quantum gravity in Smolin's weak coupling limit on the complete graph using variational Monte Carlo with neural network quantum states. We investigate the Hamilton constraint in the ordering proposed by Thiemann, as well as and . We find that the variational optimisation selects distinct solution families for and across several considered cutoffs on the kinematical degrees of freedom. The solution family of is flat on all minimal loops and has non-vanishing volume expectation values. Its edge-charge marginals delocalise with increasing cutoff, which indicates they are approximations to solutions that are non-normalisable in the kinematical inner product. The solution family for is normalisable,…
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