Twirled perfect tensor networks are introduced as a class satisfying computational covariance, bounding complexity by the Python's Lunch Conjecture exponent, and combining holographic features of perfect and random tensor networks including a lattice RT formula.
Reflected entropy in random tensor networks
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Multi-entropy exhibits a structural obstruction to replica symmetry breaking in random tensor networks due to incompatible boundary permutations in the replica hypercube, unlike entanglement negativity.
Summing non-perturbative contributions in the gravitational path integral, extended via matrix integral saddles including one- and two-eigenvalue instantons, resolves negativity of bulk entropies in two-sided black holes.
citing papers explorer
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Twirled Perfect Tensor Networks: Computationally covariant holographic tensor networks
Twirled perfect tensor networks are introduced as a class satisfying computational covariance, bounding complexity by the Python's Lunch Conjecture exponent, and combining holographic features of perfect and random tensor networks including a lattice RT formula.
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Structural Obstruction to Replica Symmetry Breaking for Multi-Entropy in Random Tensor Networks
Multi-entropy exhibits a structural obstruction to replica symmetry breaking in random tensor networks due to incompatible boundary permutations in the replica hypercube, unlike entanglement negativity.
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Living on the edge: a non-perturbative resolution to the negativity of bulk entropies
Summing non-perturbative contributions in the gravitational path integral, extended via matrix integral saddles including one- and two-eigenvalue instantons, resolves negativity of bulk entropies in two-sided black holes.