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.
Hyperinvariant multiscale entanglement renormalization ansatz: Approximate holographic error correction codes with power-law correlations
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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.